Singular Sanitize BoundingBox (#7316)

Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

gallery/plot_transforms_v2_e2e.py

@@ -105,13 +105,13 @@ transform = transforms.Compose(
         transforms.RandomHorizontalFlip(),
         transforms.ToImageTensor(),
         transforms.ConvertImageDtype(torch.float32),
-        transforms.SanitizeBoundingBoxes(),
+        transforms.SanitizeBoundingBox(),
     ]
 )
 
 ########################################################################################################################
 # .. note::
-#    Although the :class:`~torchvision.transforms.v2.SanitizeBoundingBoxes` transform is a no-op in this example, but it
+#    Although the :class:`~torchvision.transforms.v2.SanitizeBoundingBox` transform is a no-op in this example, but it
 #    should be placed at least once at the end of a detection pipeline to remove degenerate bounding boxes as well as
 #    the corresponding labels and optionally masks. It is particularly critical to add it if
 #    :class:`~torchvision.transforms.v2.RandomIoUCrop` was used.

test/test_transforms_v2.py

@@ -275,7 +275,7 @@ class TestSmoke:
                 boxes=datapoints.BoundingBox([[0, 0, 0, 0]], format=format, spatial_size=(224, 244)),
                 labels=torch.tensor([3]),
             )
-            assert transforms.SanitizeBoundingBoxes()(sample)["boxes"].shape == (0, 4)
+            assert transforms.SanitizeBoundingBox()(sample)["boxes"].shape == (0, 4)
 
     @parametrize(
         [
@@ -1876,7 +1876,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
             transforms.ConvertImageDtype(torch.float),
         ]
     if sanitize:
-        t += [transforms.SanitizeBoundingBoxes()]
+        t += [transforms.SanitizeBoundingBox()]
     t = transforms.Compose(t)
 
     num_boxes = 5
@@ -1917,7 +1917,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
         # ssd and ssdlite contain RandomIoUCrop which may "remove" some bbox. It
         # doesn't remove them strictly speaking, it just marks some boxes as
         # degenerate and those boxes will be later removed by
-        # SanitizeBoundingBoxes(), which we add to the pipelines if the sanitize
+        # SanitizeBoundingBox(), which we add to the pipelines if the sanitize
         # param is True.
         # Note that the values below are probably specific to the random seed
         # set above (which is fine).
@@ -1989,7 +1989,7 @@ def test_sanitize_bounding_boxes(min_size, labels_getter, sample_type):
         img = sample.pop("image")
         sample = (img, sample)
 
-    out = transforms.SanitizeBoundingBoxes(min_size=min_size, labels_getter=labels_getter)(sample)
+    out = transforms.SanitizeBoundingBox(min_size=min_size, labels_getter=labels_getter)(sample)
 
     if sample_type is tuple:
         out_image = out[0]
@@ -2023,13 +2023,13 @@ def test_sanitize_bounding_boxes_default_heuristic(key, sample_type):
     sample = {key: labels, "another_key": "whatever"}
     if sample_type is tuple:
         sample = (None, sample, "whatever_again")
-    assert transforms.SanitizeBoundingBoxes._find_labels_default_heuristic(sample) is labels
+    assert transforms.SanitizeBoundingBox._find_labels_default_heuristic(sample) is labels
 
     if key.lower() != "labels":
         # If "labels" is in the dict (case-insensitive),
         # it takes precedence over other keys which would otherwise be a match
         d = {key: "something_else", "labels": labels}
-        assert transforms.SanitizeBoundingBoxes._find_labels_default_heuristic(d) is labels
+        assert transforms.SanitizeBoundingBox._find_labels_default_heuristic(d) is labels
 
 
 def test_sanitize_bounding_boxes_errors():
@@ -2041,25 +2041,25 @@ def test_sanitize_bounding_boxes_errors():
     )
 
     with pytest.raises(ValueError, match="min_size must be >= 1"):
-        transforms.SanitizeBoundingBoxes(min_size=0)
+        transforms.SanitizeBoundingBox(min_size=0)
     with pytest.raises(ValueError, match="labels_getter should either be a str"):
-        transforms.SanitizeBoundingBoxes(labels_getter=12)
+        transforms.SanitizeBoundingBox(labels_getter=12)
 
     with pytest.raises(ValueError, match="Could not infer where the labels are"):
         bad_labels_key = {"bbox": good_bbox, "BAD_KEY": torch.arange(good_bbox.shape[0])}
-        transforms.SanitizeBoundingBoxes()(bad_labels_key)
+        transforms.SanitizeBoundingBox()(bad_labels_key)
 
     with pytest.raises(ValueError, match="If labels_getter is a str or 'default'"):
         not_a_dict = (good_bbox, torch.arange(good_bbox.shape[0]))
-        transforms.SanitizeBoundingBoxes()(not_a_dict)
+        transforms.SanitizeBoundingBox()(not_a_dict)
 
     with pytest.raises(ValueError, match="must be a tensor"):
         not_a_tensor = {"bbox": good_bbox, "labels": torch.arange(good_bbox.shape[0]).tolist()}
-        transforms.SanitizeBoundingBoxes()(not_a_tensor)
+        transforms.SanitizeBoundingBox()(not_a_tensor)
 
     with pytest.raises(ValueError, match="Number of boxes"):
         different_sizes = {"bbox": good_bbox, "labels": torch.arange(good_bbox.shape[0] + 3)}
-        transforms.SanitizeBoundingBoxes()(different_sizes)
+        transforms.SanitizeBoundingBox()(different_sizes)
 
     with pytest.raises(ValueError, match="boxes must be of shape"):
         bad_bbox = datapoints.BoundingBox(  # batch with 2 elements
@@ -2071,7 +2071,7 @@ def test_sanitize_bounding_boxes_errors():
             spatial_size=(20, 20),
         )
         different_sizes = {"bbox": bad_bbox, "labels": torch.arange(bad_bbox.shape[0])}
-        transforms.SanitizeBoundingBoxes()(different_sizes)
+        transforms.SanitizeBoundingBox()(different_sizes)
 
 
 @pytest.mark.parametrize(

test/test_transforms_v2_consistency.py

@@ -1099,7 +1099,7 @@ class TestRefDetTransforms:
                 v2_transforms.Compose(
                     [
                         v2_transforms.RandomIoUCrop(),
-                        v2_transforms.SanitizeBoundingBoxes(labels_getter=lambda sample: sample[1]["labels"]),
+                        v2_transforms.SanitizeBoundingBox(labels_getter=lambda sample: sample[1]["labels"]),
                     ]
                 ),
                 {"with_mask": False},

torchvision/transforms/v2/__init__.py

@@ -40,7 +40,7 @@ from ._geometry import (
     TenCrop,
 )
 from ._meta import ClampBoundingBox, ConvertBoundingBoxFormat, ConvertDtype, ConvertImageDtype
-from ._misc import GaussianBlur, Identity, Lambda, LinearTransformation, Normalize, SanitizeBoundingBoxes, ToDtype
+from ._misc import GaussianBlur, Identity, Lambda, LinearTransformation, Normalize, SanitizeBoundingBox, ToDtype
 from ._temporal import UniformTemporalSubsample
 from ._type_conversion import PILToTensor, ToImagePIL, ToImageTensor, ToPILImage
 

torchvision/transforms/v2/_geometry.py

@@ -1114,7 +1114,7 @@ class RandomIoUCrop(Transform):
 
     .. warning::
         In order to properly remove the bounding boxes below the IoU threshold, `RandomIoUCrop`
-        must be followed by :class:`~torchvision.transforms.v2.SanitizeBoundingBoxes`, either immediately
+        must be followed by :class:`~torchvision.transforms.v2.SanitizeBoundingBox`, either immediately
         after or later in the transforms pipeline.
 
     If the input is a :class:`torch.Tensor` or a ``Datapoint`` (e.g. :class:`~torchvision.datapoints.Image`,
@@ -1222,7 +1222,7 @@ class RandomIoUCrop(Transform):
 
         if isinstance(output, datapoints.BoundingBox):
             # We "mark" the invalid boxes as degenreate, and they can be
-            # removed by a later call to SanitizeBoundingBoxes()
+            # removed by a later call to SanitizeBoundingBox()
             output[~params["is_within_crop_area"]] = 0
 
         return output

torchvision/transforms/v2/_misc.py

@@ -246,7 +246,7 @@ class ToDtype(Transform):
         return inpt.to(dtype=dtype)
 
 
-class SanitizeBoundingBoxes(Transform):
+class SanitizeBoundingBox(Transform):
     # This removes boxes and their corresponding labels:
     # - small or degenerate bboxes based on min_size (this includes those where X2 <= X1 or Y2 <= Y1)
     # - boxes with any coordinate outside the range of the image (negative, or > spatial_size)
@@ -269,7 +269,7 @@ class SanitizeBoundingBoxes(Transform):
         elif callable(labels_getter):
             self._labels_getter = labels_getter
         elif isinstance(labels_getter, str):
-            self._labels_getter = lambda inputs: SanitizeBoundingBoxes._get_dict_or_second_tuple_entry(inputs)[
+            self._labels_getter = lambda inputs: SanitizeBoundingBox._get_dict_or_second_tuple_entry(inputs)[
                 labels_getter  # type: ignore[index]
             ]
         elif labels_getter is None:
@@ -300,7 +300,7 @@ class SanitizeBoundingBoxes(Transform):
     def _find_labels_default_heuristic(inputs: Dict[str, Any]) -> Optional[torch.Tensor]:
         # Tries to find a "labels" key, otherwise tries for the first key that contains "label" - case insensitive
         # Returns None if nothing is found
-        inputs = SanitizeBoundingBoxes._get_dict_or_second_tuple_entry(inputs)
+        inputs = SanitizeBoundingBox._get_dict_or_second_tuple_entry(inputs)
         candidate_key = None
         with suppress(StopIteration):
             candidate_key = next(key for key in inputs.keys() if key.lower() == "labels")