Add sanitize_bounding_boxes kernel/functional

docs/source/transforms.rst

@@ -414,6 +414,7 @@ Functionals
 
     v2.functional.normalize
     v2.functional.erase
+    v2.functional.sanitize_bounding_boxes
     v2.functional.clamp_bounding_boxes
     v2.functional.uniform_temporal_subsample
 

test/test_transforms_v2.py

@@ -5675,18 +5675,7 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
 
 
 class TestSanitizeBoundingBoxes:
-    @pytest.mark.parametrize("min_size", (1, 10))
-    @pytest.mark.parametrize("labels_getter", ("default", lambda inputs: inputs["labels"], None, lambda inputs: None))
-    @pytest.mark.parametrize("sample_type", (tuple, dict))
-    def test_transform(self, min_size, labels_getter, sample_type):
-
-        if sample_type is tuple and not isinstance(labels_getter, str):
-            # The "lambda inputs: inputs["labels"]" labels_getter used in this test
-            # doesn't work if the input is a tuple.
-            return
-
-        H, W = 256, 128
-
+    def _get_boxes_and_valid_mask(self, H=256, W=128, min_size=10):
         boxes_and_validity = [
             ([0, 1, 10, 1], False),  # Y1 == Y2
             ([0, 1, 0, 20], False),  # X1 == X2
@@ -5706,18 +5695,31 @@ def test_transform(self, min_size, labels_getter, sample_type):
         ]
 
         random.shuffle(boxes_and_validity)  # For test robustness: mix order of wrong and correct cases
-        boxes, is_valid_mask = zip(*boxes_and_validity)
-        valid_indices = [i for (i, is_valid) in enumerate(is_valid_mask) if is_valid]
-
-        boxes = torch.tensor(boxes)
-        labels = torch.arange(boxes.shape[0])
+        boxes, expected_valid_mask = zip(*boxes_and_validity)
 
         boxes = tv_tensors.BoundingBoxes(
             boxes,
             format=tv_tensors.BoundingBoxFormat.XYXY,
             canvas_size=(H, W),
         )
 
+        return boxes, expected_valid_mask
+
+    @pytest.mark.parametrize("min_size", (1, 10))
+    @pytest.mark.parametrize("labels_getter", ("default", lambda inputs: inputs["labels"], None, lambda inputs: None))
+    @pytest.mark.parametrize("sample_type", (tuple, dict))
+    def test_transform(self, min_size, labels_getter, sample_type):
+
+        if sample_type is tuple and not isinstance(labels_getter, str):
+            # The "lambda inputs: inputs["labels"]" labels_getter used in this test
+            # doesn't work if the input is a tuple.
+            return
+
+        H, W = 256, 128
+        boxes, expected_valid_mask = self._get_boxes_and_valid_mask(H=H, W=W, min_size=min_size)
+        valid_indices = [i for (i, is_valid) in enumerate(expected_valid_mask) if is_valid]
+
+        labels = torch.arange(boxes.shape[0])
         masks = tv_tensors.Mask(torch.randint(0, 2, size=(boxes.shape[0], H, W)))
         whatever = torch.rand(10)
         input_img = torch.randint(0, 256, size=(1, 3, H, W), dtype=torch.uint8)
@@ -5763,6 +5765,44 @@ def test_transform(self, min_size, labels_getter, sample_type):
             # This works because we conveniently set labels to arange(num_boxes)
             assert out_labels.tolist() == valid_indices
 
+    @pytest.mark.parametrize("input_type", (torch.Tensor, tv_tensors.BoundingBoxes))
+    def test_functional(self, input_type):
+        # Note: the "functional" F.sanitize_bounding_boxes was added after the class, so there is some
+        # redundancy with test_transform() in terms of correctness checks. But that's OK.
+
+        H, W, min_size = 256, 128, 10
+
+        boxes, expected_valid_mask = self._get_boxes_and_valid_mask(H=H, W=W, min_size=min_size)
+
+        if input_type is tv_tensors.BoundingBoxes:
+            format = canvas_size = None
+        else:
+            # just passing "XYXY" explicitly to make sure we support strings
+            format, canvas_size = "XYXY", boxes.canvas_size
+            boxes = boxes.as_subclass(torch.Tensor)
+
+        boxes, valid = F.sanitize_bounding_boxes(boxes, format=format, canvas_size=canvas_size, min_size=min_size)
+
+        assert_equal(valid, torch.tensor(expected_valid_mask))
+        assert type(valid) == torch.Tensor
+        assert boxes.shape[0] == sum(valid)
+        assert isinstance(boxes, input_type)
+
+    def test_kernel(self):
+        H, W, min_size = 256, 128, 10
+        boxes, _ = self._get_boxes_and_valid_mask(H=H, W=W, min_size=min_size)
+
+        format, canvas_size = boxes.format, boxes.canvas_size
+        boxes = boxes.as_subclass(torch.Tensor)
+
+        check_kernel(
+            F.sanitize_bounding_boxes,
+            input=boxes,
+            format=format,
+            canvas_size=canvas_size,
+            check_batched_vs_unbatched=False,
+        )
+
     def test_no_label(self):
         # Non-regression test for https://github.com/pytorch/vision/issues/7878
 
@@ -5776,7 +5816,7 @@ def test_no_label(self):
         assert isinstance(out_img, tv_tensors.Image)
         assert isinstance(out_boxes, tv_tensors.BoundingBoxes)
 
-    def test_errors(self):
+    def test_errors_transform(self):
         good_bbox = tv_tensors.BoundingBoxes(
             [[0, 0, 10, 10]],
             format=tv_tensors.BoundingBoxFormat.XYXY,
@@ -5799,3 +5839,26 @@ def test_errors(self):
         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)
+
+    def test_errors_functional(self):
+
+        good_bbox = tv_tensors.BoundingBoxes(
+            [[0, 0, 10, 10]],
+            format=tv_tensors.BoundingBoxFormat.XYXY,
+            canvas_size=(20, 20),
+        )
+
+        with pytest.raises(ValueError, match="canvas_size cannot be None if bounding_boxes is a pure tensor"):
+            F.sanitize_bounding_boxes(good_bbox.as_subclass(torch.Tensor), format="XYXY", canvas_size=None)
+
+        with pytest.raises(ValueError, match="canvas_size cannot be None if bounding_boxes is a pure tensor"):
+            F.sanitize_bounding_boxes(good_bbox.as_subclass(torch.Tensor), format=None, canvas_size=(10, 10))
+
+        with pytest.raises(ValueError, match="canvas_size must be None when bounding_boxes is a tv_tensors"):
+            F.sanitize_bounding_boxes(good_bbox, format="XYXY", canvas_size=None)
+
+        with pytest.raises(ValueError, match="canvas_size must be None when bounding_boxes is a tv_tensors"):
+            F.sanitize_bounding_boxes(good_bbox, format="XYXY", canvas_size=None)
+
+        with pytest.raises(ValueError, match="bouding_boxes must be a tv_tensors.BoundingBoxes instance or a"):
+            F.sanitize_bounding_boxes(good_bbox.tolist())

torchvision/prototype/transforms/_augment.py

@@ -123,7 +123,7 @@ def _extract_image_targets(
         if not (len(images) == len(bboxes) == len(masks) == len(labels)):
             raise TypeError(
                 f"{type(self).__name__}() requires input sample to contain equal sized list of Images, "
-                "BoundingBoxeses, Masks and Labels or OneHotLabels."
+                "BoundingBoxes, Masks and Labels or OneHotLabels."
             )
 
         targets = []

torchvision/transforms/v2/_misc.py

@@ -1,5 +1,5 @@
 import warnings
-from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Type, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, Type, Union
 
 import PIL.Image
 
@@ -369,28 +369,14 @@ def forward(self, *inputs: Any) -> Any:
                 f"Number of boxes (shape={boxes.shape}) and number of labels (shape={labels.shape}) do not match."
             )
 
-        boxes = cast(
-            tv_tensors.BoundingBoxes,
-            F.convert_bounding_box_format(
-                boxes,
-                new_format=tv_tensors.BoundingBoxFormat.XYXY,
-            ),
+        valid = F._misc._get_sanitize_bounding_boxes_mask(
+            boxes,
+            format=boxes.format,
+            canvas_size=boxes.canvas_size,
+            min_size=self.min_size,
         )
-        ws, hs = boxes[:, 2] - boxes[:, 0], boxes[:, 3] - boxes[:, 1]
-        valid = (ws >= self.min_size) & (hs >= self.min_size) & (boxes >= 0).all(dim=-1)
-        # TODO: Do we really need to check for out of bounds here? All
-        # transforms should be clamping anyway, so this should never happen?
-        image_h, image_w = boxes.canvas_size
-        valid &= (boxes[:, 0] <= image_w) & (boxes[:, 2] <= image_w)
-        valid &= (boxes[:, 1] <= image_h) & (boxes[:, 3] <= image_h)
-
-        params = dict(valid=valid.as_subclass(torch.Tensor), labels=labels)
-        flat_outputs = [
-            # Even-though it may look like we're transforming all inputs, we don't:
-            # _transform() will only care about BoundingBoxeses and the labels
-            self._transform(inpt, params)
-            for inpt in flat_inputs
-        ]
+        params = dict(valid=valid, labels=labels)
+        flat_outputs = [self._transform(inpt, params) for inpt in flat_inputs]
 
         return tree_unflatten(flat_outputs, spec)
 

torchvision/transforms/v2/functional/__init__.py

@@ -167,6 +167,7 @@
     normalize,
     normalize_image,
     normalize_video,
+    sanitize_bounding_boxes,
     to_dtype,
     to_dtype_image,
     to_dtype_video,

torchvision/transforms/v2/functional/_misc.py

@@ -1,5 +1,5 @@
 import math
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
 import PIL.Image
 import torch
@@ -11,7 +11,9 @@
 
 from torchvision.utils import _log_api_usage_once
 
-from ._utils import _get_kernel, _register_kernel_internal
+from ._meta import _convert_bounding_box_format
+
+from ._utils import _get_kernel, _register_kernel_internal, is_pure_tensor
 
 
 def normalize(
@@ -275,3 +277,89 @@ def to_dtype_video(video: torch.Tensor, dtype: torch.dtype = torch.float, scale:
 def _to_dtype_tensor_dispatch(inpt: torch.Tensor, dtype: torch.dtype, scale: bool = False) -> torch.Tensor:
     # We don't need to unwrap and rewrap here, since TVTensor.to() preserves the type
     return inpt.to(dtype)
+
+
+def sanitize_bounding_boxes(
+    bounding_boxes: torch.Tensor,
+    format: Optional[tv_tensors.BoundingBoxFormat] = None,
+    canvas_size: Optional[Tuple[int, int]] = None,
+    min_size: float = 1.0,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Remove degenerate/invalid bounding boxes and return the corresponding indexing mask.
+
+    This removes bounding boxes that:
+
+    - are below a given ``min_size``: by default this also removes degenerate boxes that have e.g. X2 <= X1.
+    - have any coordinate outside of their corresponding image. You may want to
+      call :func:`~torchvision.transforms.v2.functional.clamp_bounding_boxes` first to avoid undesired removals.
+
+    It is recommended to call it at the end of a pipeline, before passing the
+    input to the models. It is critical to call this transform if
+    :class:`~torchvision.transforms.v2.RandomIoUCrop` was called.
+    If you want to be extra careful, you may call it after all transforms that
+    may modify bounding boxes but once at the end should be enough in most
+    cases.
+
+    Args:
+        bounding_boxes (Tensor or :class:`~torchvision.tv_tensors.BoundingBoxes`): The bounding boxes to be sanitized.
+        format (str or :class:`~torchvision.tv_tensors.BoundingBoxFormat`, optional): The format of the bounding boxes.
+            Must be left to none if ``bounding_boxes`` is a :class:`~torchvision.tv_tensors.BoundingBoxes` object.
+        canvas_size (tuple of int, optional): The canvas_size of the bounding boxes
+            (size of the corresponding image/video).
+            Must be left to none if ``bounding_boxes`` is a :class:`~torchvision.tv_tensors.BoundingBoxes` object.
+        min_size (float, optional) The size below which bounding boxes are removed. Default is 1.
+
+    Returns:
+        out (tuple of Tensors): The subset of valid bounding boxes, and the corresponding indexing mask.
+        The mask can then be used to subset other tensors (e.g. labels) that are associated with the bounding boxes.
+    """
+    if torch.jit.is_scripting() or is_pure_tensor(bounding_boxes):
+        if format is None or canvas_size is None:
+            raise ValueError(
+                "format and canvas_size cannot be None if bounding_boxes is a pure tensor. "
+                f"Got format={format} and canvas_size={canvas_size}."
+                "Set those to appropriate values or pass bounding_boxes as a tv_tensors.BoundingBoxes object."
+            )
+        if isinstance(format, str):
+            format = tv_tensors.BoundingBoxFormat[format.upper()]
+        valid = _get_sanitize_bounding_boxes_mask(
+            bounding_boxes, format=format, canvas_size=canvas_size, min_size=min_size
+        )
+        bounding_boxes = bounding_boxes[valid]
+    else:
+        if not isinstance(bounding_boxes, tv_tensors.BoundingBoxes):
+            raise ValueError("bouding_boxes must be a tv_tensors.BoundingBoxes instance or a pure tensor.")
+        if format is not None or canvas_size is not None:
+            raise ValueError(
+                "format and canvas_size must be None when bounding_boxes is a tv_tensors.BoundingBoxes instance. "
+                f"Got format={format} and canvas_size={canvas_size}. "
+                "Leave those to None or pass bouding_boxes as a pure tensor."
+            )
+        valid = _get_sanitize_bounding_boxes_mask(
+            bounding_boxes, format=bounding_boxes.format, canvas_size=bounding_boxes.canvas_size, min_size=min_size
+        )
+        bounding_boxes = tv_tensors.wrap(bounding_boxes[valid], like=bounding_boxes)
+
+    return bounding_boxes, valid
+
+
+def _get_sanitize_bounding_boxes_mask(
+    bounding_boxes: torch.Tensor,
+    format: tv_tensors.BoundingBoxFormat,
+    canvas_size: Tuple[int, int],
+    min_size: float = 1.0,
+) -> torch.Tensor:
+
+    bounding_boxes = _convert_bounding_box_format(
+        bounding_boxes, new_format=tv_tensors.BoundingBoxFormat.XYXY, old_format=format
+    )
+
+    image_h, image_w = canvas_size
+    ws, hs = bounding_boxes[:, 2] - bounding_boxes[:, 0], bounding_boxes[:, 3] - bounding_boxes[:, 1]
+    valid = (ws >= min_size) & (hs >= min_size) & (bounding_boxes >= 0).all(dim=-1)
+    # TODO: Do we really need to check for out of bounds here? All
+    # transforms should be clamping anyway, so this should never happen?
+    image_h, image_w = canvas_size
+    valid &= (bounding_boxes[:, 0] <= image_w) & (bounding_boxes[:, 2] <= image_w)
+    valid &= (bounding_boxes[:, 1] <= image_h) & (bounding_boxes[:, 3] <= image_h)
+    return valid