add tests for F.pad_bounding_box (#6038)

* add tests for F.pad_bounding_box

* Added correctness tests for pad and reimplemented bbox op to keep dtype

* Update _geometry.py
Co-authored-by: vfdev <vfdev.5@gmail.com>

test/test_prototype_transforms_functional.py

@@ -382,6 +382,15 @@ def pad_segmentation_mask():
         yield SampleInput(mask, padding=padding, padding_mode=padding_mode)
 
 
+@register_kernel_info_from_sample_inputs_fn
+def pad_bounding_box():
+    for bounding_box, padding in itertools.product(
+        make_bounding_boxes(),
+        [[1], [1, 1], [1, 1, 2, 2]],
+    ):
+        yield SampleInput(bounding_box, padding=padding, format=bounding_box.format)
+
+
 @register_kernel_info_from_sample_inputs_fn
 def perspective_bounding_box():
     for bounding_box, perspective_coeffs in itertools.product(
@@ -1103,10 +1112,7 @@ def test_correctness_pad_segmentation_mask_on_fixed_input(device):
     torch.testing.assert_close(out_mask, expected_mask)
 
 
-@pytest.mark.parametrize("padding", [[1, 2, 3, 4], [1], 1, [1, 2]])
-def test_correctness_pad_segmentation_mask(padding):
-    def _compute_expected_mask():
-        def parse_padding():
+def _parse_padding(padding):
     if isinstance(padding, int):
         return [padding] * 4
     if isinstance(padding, list):
@@ -1117,8 +1123,56 @@ def test_correctness_pad_segmentation_mask(padding):
 
     return padding
 
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("padding", [[1], [1, 1], [1, 1, 2, 2]])
+def test_correctness_pad_bounding_box(device, padding):
+    def _compute_expected_bbox(bbox, padding_):
+        pad_left, pad_up, _, _ = _parse_padding(padding_)
+
+        bbox_format = bbox.format
+        bbox_dtype = bbox.dtype
+        bbox = convert_bounding_box_format(bbox, old_format=bbox_format, new_format=features.BoundingBoxFormat.XYXY)
+
+        bbox[0::2] += pad_left
+        bbox[1::2] += pad_up
+
+        bbox = convert_bounding_box_format(
+            bbox, old_format=features.BoundingBoxFormat.XYXY, new_format=bbox_format, copy=False
+        )
+        if bbox.dtype != bbox_dtype:
+            # Temporary cast to original dtype
+            # e.g. float32 -> int
+            bbox = bbox.to(bbox_dtype)
+        return bbox
+
+    for bboxes in make_bounding_boxes():
+        bboxes = bboxes.to(device)
+        bboxes_format = bboxes.format
+        bboxes_image_size = bboxes.image_size
+
+        output_boxes = F.pad_bounding_box(bboxes, padding, format=bboxes_format)
+
+        if bboxes.ndim < 2:
+            bboxes = [bboxes]
+
+        expected_bboxes = []
+        for bbox in bboxes:
+            bbox = features.BoundingBox(bbox, format=bboxes_format, image_size=bboxes_image_size)
+            expected_bboxes.append(_compute_expected_bbox(bbox, padding))
+
+        if len(expected_bboxes) > 1:
+            expected_bboxes = torch.stack(expected_bboxes)
+        else:
+            expected_bboxes = expected_bboxes[0]
+        torch.testing.assert_close(output_boxes, expected_bboxes)
+
+
+@pytest.mark.parametrize("padding", [[1, 2, 3, 4], [1], 1, [1, 2]])
+def test_correctness_pad_segmentation_mask(padding):
+    def _compute_expected_mask(mask, padding_):
         h, w = mask.shape[-2], mask.shape[-1]
-        pad_left, pad_up, pad_right, pad_down = parse_padding()
+        pad_left, pad_up, pad_right, pad_down = _parse_padding(padding_)
 
         new_h = h + pad_up + pad_down
         new_w = w + pad_left + pad_right
@@ -1132,7 +1186,7 @@ def test_correctness_pad_segmentation_mask(padding):
     for mask in make_segmentation_masks():
         out_mask = F.pad_segmentation_mask(mask, padding, "constant")
 
-        expected_mask = _compute_expected_mask()
+        expected_mask = _compute_expected_mask(mask, padding)
         torch.testing.assert_close(out_mask, expected_mask)
 
 

torchvision/prototype/transforms/functional/_geometry.py

@@ -415,16 +415,15 @@ def pad_bounding_box(
 ) -> torch.Tensor:
     left, _, top, _ = _FT._parse_pad_padding(padding)
 
-    bounding_box = convert_bounding_box_format(
-        bounding_box, old_format=format, new_format=features.BoundingBoxFormat.XYXY
-    )
-
-    bounding_box[..., 0::2] += left
-    bounding_box[..., 1::2] += top
-
-    return convert_bounding_box_format(
-        bounding_box, old_format=features.BoundingBoxFormat.XYXY, new_format=format, copy=False
-    )
+    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
+    return bounding_box
 
 
 crop_image_tensor = _FT.crop