Added resized_crop_bounding_box op (#5853)

* [proto] Added crop_bounding_box op

* Added tests for resized_crop_bounding_box

* Fixed code formatting

test/test_prototype_transforms_functional.py

@@ -352,6 +352,16 @@ def vertical_flip_segmentation_mask():
         yield SampleInput(mask)
 
 
+@register_kernel_info_from_sample_inputs_fn
+def resized_crop_bounding_box():
+    for bounding_box, top, left, height, width, size in itertools.product(
+        make_bounding_boxes(), [-8, 9], [-8, 9], [32, 22], [34, 20], [(32, 32), (16, 18)]
+    ):
+        yield SampleInput(
+            bounding_box, format=bounding_box.format, top=top, left=left, height=height, width=width, size=size
+        )
+
+
 @pytest.mark.parametrize(
     "kernel",
     [
@@ -842,14 +852,18 @@ def test_correctness_rotate_segmentation_mask_on_fixed_input(device):
 
 
 @pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize(
+    "format",
+    [features.BoundingBoxFormat.XYXY, features.BoundingBoxFormat.XYWH, features.BoundingBoxFormat.CXCYWH],
+)
 @pytest.mark.parametrize(
     "top, left, height, width, expected_bboxes",
     [
         [8, 12, 30, 40, [(-2.0, 7.0, 13.0, 27.0), (38.0, -3.0, 58.0, 14.0), (33.0, 38.0, 44.0, 54.0)]],
         [-8, 12, 70, 40, [(-2.0, 23.0, 13.0, 43.0), (38.0, 13.0, 58.0, 30.0), (33.0, 54.0, 44.0, 70.0)]],
     ],
 )
-def test_correctness_crop_bounding_box(device, top, left, height, width, expected_bboxes):
+def test_correctness_crop_bounding_box(device, format, top, left, height, width, expected_bboxes):
 
     # Expected bboxes computed using Albumentations:
     # import numpy as np
@@ -871,14 +885,19 @@ def test_correctness_crop_bounding_box(device, top, left, height, width, expecte
         [45.0, 46.0, 56.0, 62.0],
     ]
     in_boxes = features.BoundingBox(in_boxes, format=features.BoundingBoxFormat.XYXY, image_size=size, device=device)
+    if format != features.BoundingBoxFormat.XYXY:
+        in_boxes = convert_bounding_box_format(in_boxes, features.BoundingBoxFormat.XYXY, format)
 
     output_boxes = F.crop_bounding_box(
         in_boxes,
-        in_boxes.format,
+        format,
         top,
         left,
     )
 
+    if format != features.BoundingBoxFormat.XYXY:
+        output_boxes = convert_bounding_box_format(output_boxes, format, features.BoundingBoxFormat.XYXY)
+
     torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)
 
 
@@ -933,3 +952,49 @@ def test_correctness_vertical_flip_segmentation_mask_on_fixed_input(device):
     expected_mask = torch.zeros((3, 3, 3), dtype=torch.long, device=device)
     expected_mask[:, -1, :] = 1
     torch.testing.assert_close(out_mask, expected_mask)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize(
+    "format",
+    [features.BoundingBoxFormat.XYXY, features.BoundingBoxFormat.XYWH, features.BoundingBoxFormat.CXCYWH],
+)
+@pytest.mark.parametrize(
+    "top, left, height, width, size",
+    [
+        [0, 0, 30, 30, (60, 60)],
+        [-5, 5, 35, 45, (32, 34)],
+    ],
+)
+def test_correctness_resized_crop_bounding_box(device, format, top, left, height, width, size):
+    def _compute_expected(bbox, top_, left_, height_, width_, size_):
+        # bbox should be xyxy
+        bbox[0] = (bbox[0] - left_) * size_[1] / width_
+        bbox[1] = (bbox[1] - top_) * size_[0] / height_
+        bbox[2] = (bbox[2] - left_) * size_[1] / width_
+        bbox[3] = (bbox[3] - top_) * size_[0] / height_
+        return bbox
+
+    image_size = (100, 100)
+    # xyxy format
+    in_boxes = [
+        [10.0, 10.0, 20.0, 20.0],
+        [5.0, 10.0, 15.0, 20.0],
+    ]
+    expected_bboxes = []
+    for in_box in in_boxes:
+        expected_bboxes.append(_compute_expected(list(in_box), top, left, height, width, size))
+    expected_bboxes = torch.tensor(expected_bboxes, device=device)
+
+    in_boxes = features.BoundingBox(
+        in_boxes, format=features.BoundingBoxFormat.XYXY, image_size=image_size, device=device
+    )
+    if format != features.BoundingBoxFormat.XYXY:
+        in_boxes = convert_bounding_box_format(in_boxes, features.BoundingBoxFormat.XYXY, format)
+
+    output_boxes = F.resized_crop_bounding_box(in_boxes, format, top, left, height, width, size)
+
+    if format != features.BoundingBoxFormat.XYXY:
+        output_boxes = convert_bounding_box_format(output_boxes, format, features.BoundingBoxFormat.XYXY)
+
+    torch.testing.assert_close(output_boxes, expected_bboxes)

torchvision/prototype/transforms/functional/__init__.py

@@ -49,6 +49,7 @@
     center_crop_image_pil,
     resized_crop_image_tensor,
     resized_crop_image_pil,
+    resized_crop_bounding_box,
     affine_bounding_box,
     affine_image_tensor,
     affine_image_pil,

torchvision/prototype/transforms/functional/_geometry.py

@@ -542,6 +542,19 @@ def resized_crop_image_pil(
     return resize_image_pil(img, size, interpolation=interpolation)
 
 
+def resized_crop_bounding_box(
+    bounding_box: torch.Tensor,
+    format: features.BoundingBoxFormat,
+    top: int,
+    left: int,
+    height: int,
+    width: int,
+    size: List[int],
+) -> torch.Tensor:
+    bounding_box = crop_bounding_box(bounding_box, format, top, left)
+    return resize_bounding_box(bounding_box, size, (height, width))
+
+
 def _parse_five_crop_size(size: List[int]) -> List[int]:
     if isinstance(size, numbers.Number):
         size = [int(size), int(size)]