Rename features.SegmentationMask to features.Mask (#6579)

* rename features.SegmentationMask -> features.Mask

* rename kernels *_segmentation_mask -> *_mask and cleanup input name

* cleanup

* rename module _segmentation_mask.py -> _mask.py

* fix test

Author: pmeier

test/prototype_common_utils.py

@@ -184,7 +184,7 @@ def make_detection_mask(size=None, *, num_objects=None, extra_dims=(), dtype=tor
     num_objects = num_objects if num_objects is not None else int(torch.randint(1, 11, ()))
     shape = (*extra_dims, num_objects, *size)
     data = make_tensor(shape, low=0, high=2, dtype=dtype)
-    return features.SegmentationMask(data)
+    return features.Mask(data)
 
 
 def make_detection_masks(
@@ -207,7 +207,7 @@ def make_segmentation_mask(size=None, *, num_categories=None, extra_dims=(), dty
     num_categories = num_categories if num_categories is not None else int(torch.randint(1, 11, ()))
     shape = (*extra_dims, *size)
     data = make_tensor(shape, low=0, high=num_categories, dtype=dtype)
-    return features.SegmentationMask(data)
+    return features.Mask(data)
 
 
 def make_segmentation_masks(
@@ -224,7 +224,7 @@ def make_segmentation_masks(
         yield make_segmentation_mask(size=sizes[0], num_categories=num_categories_, dtype=dtype, extra_dims=extra_dims_)
 
 
-def make_detection_and_segmentation_masks(
+def make_masks(
     sizes=((16, 16), (7, 33), (31, 9)),
     dtypes=(torch.uint8,),
     extra_dims=((), (0,), (4,), (2, 3), (5, 0), (0, 5)),

test/test_prototype_transforms.py

@@ -10,11 +10,11 @@
 from prototype_common_utils import (
     make_bounding_box,
     make_bounding_boxes,
-    make_detection_and_segmentation_masks,
     make_detection_mask,
     make_image,
     make_images,
     make_label,
+    make_masks,
     make_one_hot_labels,
     make_segmentation_mask,
 )
@@ -64,7 +64,7 @@ def parametrize_from_transforms(*transforms):
             make_one_hot_labels,
             make_vanilla_tensor_images,
             make_pil_images,
-            make_detection_and_segmentation_masks,
+            make_masks,
         ]:
             inputs = list(creation_fn())
             try:
@@ -132,7 +132,7 @@ def test_mixup_cutmix(self, transform, input):
         transform(input_copy)
 
         # Check if we raise an error if sample contains bbox or mask or label
-        err_msg = "does not support bounding boxes, segmentation masks and plain labels"
+        err_msg = "does not support bounding boxes, masks and plain labels"
         input_copy = dict(input)
         for unsup_data in [
             make_label(),
@@ -241,7 +241,7 @@ def test_convert_color_space_unsupported_types(self):
             color_space=features.ColorSpace.RGB, old_color_space=features.ColorSpace.GRAY
         )
 
-        for inpt in [make_bounding_box(format="XYXY"), make_detection_and_segmentation_masks()]:
+        for inpt in [make_bounding_box(format="XYXY"), make_masks()]:
             output = transform(inpt)
             assert output is inpt
 
@@ -278,13 +278,13 @@ def test_features_image(self, p):
 
         assert_equal(features.Image(expected), actual)
 
-    def test_features_segmentation_mask(self, p):
+    def test_features_mask(self, p):
         input, expected = self.input_expected_image_tensor(p)
         transform = transforms.RandomHorizontalFlip(p=p)
 
-        actual = transform(features.SegmentationMask(input))
+        actual = transform(features.Mask(input))
 
-        assert_equal(features.SegmentationMask(expected), actual)
+        assert_equal(features.Mask(expected), actual)
 
     def test_features_bounding_box(self, p):
         input = features.BoundingBox([0, 0, 5, 5], format=features.BoundingBoxFormat.XYXY, image_size=(10, 10))
@@ -331,13 +331,13 @@ def test_features_image(self, p):
 
         assert_equal(features.Image(expected), actual)
 
-    def test_features_segmentation_mask(self, p):
+    def test_features_mask(self, p):
         input, expected = self.input_expected_image_tensor(p)
         transform = transforms.RandomVerticalFlip(p=p)
 
-        actual = transform(features.SegmentationMask(input))
+        actual = transform(features.Mask(input))
 
-        assert_equal(features.SegmentationMask(expected), actual)
+        assert_equal(features.Mask(expected), actual)
 
     def test_features_bounding_box(self, p):
         input = features.BoundingBox([0, 0, 5, 5], format=features.BoundingBoxFormat.XYXY, image_size=(10, 10))
@@ -1253,7 +1253,7 @@ def test__transform(self, mocker):
         torch.testing.assert_close(output_ohe_label, ohe_label[is_within_crop_area])
 
         output_masks = output[4]
-        assert isinstance(output_masks, features.SegmentationMask)
+        assert isinstance(output_masks, features.Mask)
         assert len(output_masks) == expected_within_targets
 
 
@@ -1372,10 +1372,10 @@ def test__extract_image_targets_assertion(self, mocker):
             # labels, bboxes, masks
             mocker.MagicMock(spec=features.Label),
             mocker.MagicMock(spec=features.BoundingBox),
-            mocker.MagicMock(spec=features.SegmentationMask),
+            mocker.MagicMock(spec=features.Mask),
             # labels, bboxes, masks
             mocker.MagicMock(spec=features.BoundingBox),
-            mocker.MagicMock(spec=features.SegmentationMask),
+            mocker.MagicMock(spec=features.Mask),
         ]
 
         with pytest.raises(TypeError, match="requires input sample to contain equal sized list of Images"):
@@ -1393,11 +1393,11 @@ def test__extract_image_targets(self, image_type, label_type, mocker):
             # labels, bboxes, masks
             mocker.MagicMock(spec=label_type),
             mocker.MagicMock(spec=features.BoundingBox),
-            mocker.MagicMock(spec=features.SegmentationMask),
+            mocker.MagicMock(spec=features.Mask),
             # labels, bboxes, masks
             mocker.MagicMock(spec=label_type),
             mocker.MagicMock(spec=features.BoundingBox),
-            mocker.MagicMock(spec=features.SegmentationMask),
+            mocker.MagicMock(spec=features.Mask),
         ]
 
         images, targets = transform._extract_image_targets(flat_sample)
@@ -1413,7 +1413,7 @@ def test__extract_image_targets(self, image_type, label_type, mocker):
         for target in targets:
             for key, type_ in [
                 ("boxes", features.BoundingBox),
-                ("masks", features.SegmentationMask),
+                ("masks", features.Mask),
                 ("labels", label_type),
             ]:
                 assert key in target
@@ -1436,7 +1436,7 @@ def test__copy_paste(self, label_type):
             "boxes": features.BoundingBox(
                 torch.tensor([[2.0, 3.0, 8.0, 9.0], [20.0, 20.0, 30.0, 30.0]]), format="XYXY", image_size=(32, 32)
             ),
-            "masks": features.SegmentationMask(masks),
+            "masks": features.Mask(masks),
             "labels": label_type(labels),
         }
 
@@ -1451,7 +1451,7 @@ def test__copy_paste(self, label_type):
             "boxes": features.BoundingBox(
                 torch.tensor([[12.0, 13.0, 19.0, 18.0], [1.0, 15.0, 8.0, 19.0]]), format="XYXY", image_size=(32, 32)
             ),
-            "masks": features.SegmentationMask(paste_masks),
+            "masks": features.Mask(paste_masks),
             "labels": label_type(paste_labels),
         }
 
@@ -1586,7 +1586,7 @@ def test__transform_culling(self, mocker):
         bounding_boxes = make_bounding_box(
             format=features.BoundingBoxFormat.XYXY, image_size=image_size, extra_dims=(batch_size,)
         )
-        segmentation_masks = make_detection_mask(size=image_size, extra_dims=(batch_size,))
+        masks = make_detection_mask(size=image_size, extra_dims=(batch_size,))
         labels = make_label(size=(batch_size,))
 
         transform = transforms.FixedSizeCrop((-1, -1))
@@ -1596,13 +1596,13 @@ def test__transform_culling(self, mocker):
         output = transform(
             dict(
                 bounding_boxes=bounding_boxes,
-                segmentation_masks=segmentation_masks,
+                masks=masks,
                 labels=labels,
             )
         )
 
         assert_equal(output["bounding_boxes"], bounding_boxes[is_valid])
-        assert_equal(output["segmentation_masks"], segmentation_masks[is_valid])
+        assert_equal(output["masks"], masks[is_valid])
         assert_equal(output["labels"], labels[is_valid])
 
     def test__transform_bounding_box_clamping(self, mocker):