Simplify test and fix some kernels that I forgot

Author: NicolasHug

test/test_transforms_v2.py

@@ -551,6 +551,7 @@ def affine_bounding_boxes(bounding_boxes):
         ),
         format=format,
         canvas_size=canvas_size,
+        clamping_mode=clamping_mode,
     )
 
 
@@ -639,6 +640,7 @@ def affine_rotated_bounding_boxes(bounding_boxes):
         ).reshape(bounding_boxes.shape),
         format=format,
         canvas_size=canvas_size,
+        clamping_mode=clamping_mode,
     )
 
 
@@ -4355,7 +4357,6 @@ def test_functional(self, make_input):
             (F.resized_crop_image, torch.Tensor),
             (F._geometry._resized_crop_image_pil, PIL.Image.Image),
             (F.resized_crop_image, tv_tensors.Image),
-            (F.resized_crop_bounding_boxes, tv_tensors.BoundingBoxes),
             (F.resized_crop_mask, tv_tensors.Mask),
             (F.resized_crop_video, tv_tensors.Video),
             (F.resized_crop_keypoints, tv_tensors.KeyPoints),
@@ -4422,30 +4423,30 @@ def _reference_resized_crop_bounding_boxes(self, bounding_boxes, *, top, left, h
             ],
         )
 
+        affine_matrix = (resize_affine_matrix @ crop_affine_matrix)[:2, :]
+
         helper = (
             reference_affine_rotated_bounding_boxes_helper
             if tv_tensors.is_rotated_bounding_format(bounding_boxes.format)
             else reference_affine_bounding_boxes_helper
         )
 
-        bounding_boxes = helper(bounding_boxes, affine_matrix=crop_affine_matrix, new_canvas_size=(height, width))
-
-        return helper(
-            bounding_boxes,
-            affine_matrix=resize_affine_matrix,
-            new_canvas_size=size,
-        )
+        return helper(bounding_boxes, affine_matrix=affine_matrix, new_canvas_size=size, clamp=False)
 
     @pytest.mark.parametrize("format", list(tv_tensors.BoundingBoxFormat))
     def test_functional_bounding_boxes_correctness(self, format):
-        bounding_boxes = make_bounding_boxes(self.INPUT_SIZE, format=format)
+        # Note that we don't want to clamp because in
+        # _reference_resized_crop_bounding_boxes we are fusing the crop and the
+        # resize operation, where none of the croppings happen - particularly,
+        # the intermediate one.
+        bounding_boxes = make_bounding_boxes(self.INPUT_SIZE, format=format, clamping_mode="none")
 
         actual = F.resized_crop(bounding_boxes, **self.CROP_KWARGS, size=self.OUTPUT_SIZE)
         expected = self._reference_resized_crop_bounding_boxes(
             bounding_boxes, **self.CROP_KWARGS, size=self.OUTPUT_SIZE
         )
 
-        torch.testing.assert_close(actual, expected, atol=1e-5, rtol=1e-5)
+        torch.testing.assert_close(actual, expected)
         assert_equal(F.get_size(actual), F.get_size(expected))
 
     def _reference_resized_crop_keypoints(self, keypoints, *, top, left, height, width, size):

torchvision/transforms/v2/functional/_geometry.py

@@ -2620,11 +2620,18 @@ def center_crop_bounding_boxes(
     format: tv_tensors.BoundingBoxFormat,
     canvas_size: tuple[int, int],
     output_size: list[int],
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> tuple[torch.Tensor, tuple[int, int]]:
     crop_height, crop_width = _center_crop_parse_output_size(output_size)
     crop_top, crop_left = _center_crop_compute_crop_anchor(crop_height, crop_width, *canvas_size)
     return crop_bounding_boxes(
-        bounding_boxes, format, top=crop_top, left=crop_left, height=crop_height, width=crop_width
+        bounding_boxes,
+        format,
+        top=crop_top,
+        left=crop_left,
+        height=crop_height,
+        width=crop_width,
+        clamping_mode=clamping_mode,
     )
 
 
@@ -2633,7 +2640,11 @@ def _center_crop_bounding_boxes_dispatch(
     inpt: tv_tensors.BoundingBoxes, output_size: list[int]
 ) -> tv_tensors.BoundingBoxes:
     output, canvas_size = center_crop_bounding_boxes(
-        inpt.as_subclass(torch.Tensor), format=inpt.format, canvas_size=inpt.canvas_size, output_size=output_size
+        inpt.as_subclass(torch.Tensor),
+        format=inpt.format,
+        canvas_size=inpt.canvas_size,
+        output_size=output_size,
+        clamping_mode=inpt.clamping_mode,
     )
     return tv_tensors.wrap(output, like=inpt, canvas_size=canvas_size)
 
@@ -2780,17 +2791,29 @@ def resized_crop_bounding_boxes(
     height: int,
     width: int,
     size: list[int],
+    clamping_mode: CLAMPING_MODE_TYPE = "soft",
 ) -> tuple[torch.Tensor, tuple[int, int]]:
-    bounding_boxes, canvas_size = crop_bounding_boxes(bounding_boxes, format, top, left, height, width)
-    return resize_bounding_boxes(bounding_boxes, format=format, canvas_size=canvas_size, size=size)
+    bounding_boxes, canvas_size = crop_bounding_boxes(
+        bounding_boxes, format, top, left, height, width, clamping_mode=clamping_mode
+    )
+    return resize_bounding_boxes(
+        bounding_boxes, format=format, canvas_size=canvas_size, size=size, clamping_mode=clamping_mode
+    )
 
 
 @_register_kernel_internal(resized_crop, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False)
 def _resized_crop_bounding_boxes_dispatch(
     inpt: tv_tensors.BoundingBoxes, top: int, left: int, height: int, width: int, size: list[int], **kwargs
 ) -> tv_tensors.BoundingBoxes:
     output, canvas_size = resized_crop_bounding_boxes(
-        inpt.as_subclass(torch.Tensor), format=inpt.format, top=top, left=left, height=height, width=width, size=size
+        inpt.as_subclass(torch.Tensor),
+        format=inpt.format,
+        top=top,
+        left=left,
+        height=height,
+        width=width,
+        size=size,
+        clamping_mode=inpt.clamping_mode,
     )
     return tv_tensors.wrap(output, like=inpt, canvas_size=canvas_size)