[proto] Optimized functional pad op for bboxes + tests (#6890)

* [proto] Speed-up crop on bboxes and tests

* Fix linter

* Update _geometry.py

* Fixed device issue

* Revert changes in test/prototype_transforms_kernel_infos.py

* Fixed failing correctness tests

* [proto] Optimized functional pad op for bboxes + tests

* Renamed copy-pasted variable name

* Code update

* Fixes according to the review

test/prototype_transforms_kernel_infos.py

@@ -25,7 +25,7 @@
 )
 from torch.utils._pytree import tree_map
 from torchvision.prototype import features
-from torchvision.transforms.functional_tensor import _max_value as get_max_value
+from torchvision.transforms.functional_tensor import _max_value as get_max_value, _parse_pad_padding
 
 __all__ = ["KernelInfo", "KERNEL_INFOS"]
 
@@ -1078,6 +1078,38 @@ def sample_inputs_pad_video():
         yield ArgsKwargs(video_loader, padding=[1])
 
 
+def reference_pad_bounding_box(bounding_box, *, format, spatial_size, padding, padding_mode):
+
+    left, right, top, bottom = _parse_pad_padding(padding)
+
+    affine_matrix = np.array(
+        [
+            [1, 0, left],
+            [0, 1, top],
+        ],
+        dtype="float32",
+    )
+
+    height = spatial_size[0] + top + bottom
+    width = spatial_size[1] + left + right
+
+    expected_bboxes = reference_affine_bounding_box_helper(bounding_box, format=format, affine_matrix=affine_matrix)
+    return expected_bboxes, (height, width)
+
+
+def reference_inputs_pad_bounding_box():
+    for bounding_box_loader, padding in itertools.product(
+        make_bounding_box_loaders(extra_dims=((), (4,))), [1, (1,), (1, 2), (1, 2, 3, 4), [1], [1, 2], [1, 2, 3, 4]]
+    ):
+        yield ArgsKwargs(
+            bounding_box_loader,
+            format=bounding_box_loader.format,
+            spatial_size=bounding_box_loader.spatial_size,
+            padding=padding,
+            padding_mode="constant",
+        )
+
+
 KERNEL_INFOS.extend(
     [
         KernelInfo(
@@ -1097,6 +1129,8 @@ def sample_inputs_pad_video():
         KernelInfo(
             F.pad_bounding_box,
             sample_inputs_fn=sample_inputs_pad_bounding_box,
+            reference_fn=reference_pad_bounding_box,
+            reference_inputs_fn=reference_inputs_pad_bounding_box,
             test_marks=[
                 xfail_jit_python_scalar_arg("padding"),
                 xfail_jit_tuple_instead_of_list("padding"),

torchvision/prototype/transforms/functional/_geometry.py

@@ -768,14 +768,11 @@ def pad_bounding_box(
 
     left, right, top, bottom = _parse_pad_padding(padding)
 
-    bounding_box = bounding_box.clone()
-
-    # this works without conversion since padding only affects xy coordinates
-    bounding_box[..., 0] += left
-    bounding_box[..., 1] += top
     if format == features.BoundingBoxFormat.XYXY:
-        bounding_box[..., 2] += left
-        bounding_box[..., 3] += top
+        pad = [left, top, left, top]
+    else:
+        pad = [left, top, 0, 0]
+    bounding_box = bounding_box + torch.tensor(pad, dtype=bounding_box.dtype, device=bounding_box.device)
 
     height, width = spatial_size
     height += top + bottom
@@ -821,14 +818,13 @@ def crop_bounding_box(
     width: int,
 ) -> Tuple[torch.Tensor, Tuple[int, int]]:
 
-    bounding_box = bounding_box.clone()
-
     # Crop or implicit pad if left and/or top have negative values:
     if format == features.BoundingBoxFormat.XYXY:
-        sub = torch.tensor([left, top, left, top], device=bounding_box.device)
+        sub = [left, top, left, top]
     else:
-        sub = torch.tensor([left, top, 0, 0], device=bounding_box.device)
-    bounding_box = bounding_box.sub_(sub)
+        sub = [left, top, 0, 0]
+
+    bounding_box = bounding_box - torch.tensor(sub, dtype=bounding_box.dtype, device=bounding_box.device)
 
     return bounding_box, (height, width)