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feat: add functional center crop on mask #5961

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Merged
merged 6 commits into from
May 23, 2022
Merged

feat: add functional center crop on mask #5961

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cb36ff6
feat: add functional center crop on mask
May 7, 2022
9e2739b
test: add correctness center crop with random segmentation mask
May 10, 2022
9a94978
test: improvements
May 12, 2022
802b9b4
test: improvements
May 21, 2022
97cfbf8
Apply suggestions from code review
vfdev-5 May 23, 2022
f6984f7
Merge branch 'main' into feature/5782-proto-center-crop-mask
vfdev-5 May 23, 2022
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33 changes: 32 additions & 1 deletion test/test_prototype_transforms_functional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,11 +10,11 @@
from torch import jit
from torch.nn.functional import one_hot
from torchvision.prototype import features
from torchvision.prototype.transforms.functional._geometry import _center_crop_compute_padding
from torchvision.prototype.transforms.functional._meta import convert_bounding_box_format
from torchvision.transforms.functional import _get_perspective_coeffs
from torchvision.transforms.functional_tensor import _max_value as get_max_value


make_tensor = functools.partial(torch.testing.make_tensor, device="cpu")


Expand Down Expand Up @@ -421,6 +421,14 @@ def center_crop_bounding_box():
)


def center_crop_segmentation_mask():
for mask, output_size in itertools.product(
make_segmentation_masks(image_sizes=((16, 16), (7, 33), (31, 9))),
[[4, 3], [42, 70], [4]], # crop sizes < image sizes, crop_sizes > image sizes, single crop size
):
yield SampleInput(mask, output_size)


@pytest.mark.parametrize(
"kernel",
[
Expand Down Expand Up @@ -1337,3 +1345,26 @@ def _compute_expected_bbox(bbox, output_size_):
else:
expected_bboxes = expected_bboxes[0]
torch.testing.assert_close(output_boxes, expected_bboxes)


@pytest.mark.parametrize("device", cpu_and_gpu())
@pytest.mark.parametrize("output_size", [[4, 2], [4], [7, 6]])
def test_correctness_center_crop_segmentation_mask(device, output_size):
def _compute_expected_segmentation_mask(mask, output_size):
crop_height, crop_width = output_size if len(output_size) > 1 else [output_size[0], output_size[0]]

_, image_height, image_width = mask.shape
if crop_width > image_height or crop_height > image_width:
padding = _center_crop_compute_padding(crop_height, crop_width, image_height, image_width)
mask = F.pad_image_tensor(mask, padding, fill=0)

left = round((image_width - crop_width) * 0.5)
top = round((image_height - crop_height) * 0.5)
Comment on lines +1361 to +1362
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The kernel has an additional int call:

vision/torchvision/prototype/transforms/functional/_geometry.py

Lines 585 to 586 in b969cca

crop_top = int(round((image_height - crop_height) / 2.0))
crop_left = int(round((image_width - crop_width) / 2.0))

@vfdev-5 I recall we had issues with round before. Should we just switch to int in general?

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@pmeier I do not quite remember what was the issue with round (maybe jit behaves differently to eager mode). For me the code you mention is more like a definition of crop_top and crop_left. For example, for bboxes we could also keep these values as float but let's define that crop_top/left are rounded integers.


return mask[:, top : top + crop_height, left : left + crop_width]

mask = torch.randint(0, 2, size=(1, 6, 6), dtype=torch.long, device=device)
actual = F.center_crop_segmentation_mask(mask, output_size)

expected = _compute_expected_segmentation_mask(mask, output_size)
torch.testing.assert_close(expected, actual)
1 change: 1 addition & 0 deletions torchvision/prototype/transforms/functional/__init__.py
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Expand Up @@ -46,6 +46,7 @@
resize_image_pil,
resize_segmentation_mask,
center_crop_bounding_box,
center_crop_segmentation_mask,
center_crop_image_tensor,
center_crop_image_pil,
resized_crop_bounding_box,
Expand Down
4 changes: 4 additions & 0 deletions torchvision/prototype/transforms/functional/_geometry.py
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Original file line number Diff line number Diff line change
Expand Up @@ -630,6 +630,10 @@ def center_crop_bounding_box(
return crop_bounding_box(bounding_box, format, top=crop_top, left=crop_left)


def center_crop_segmentation_mask(segmentation_mask: torch.Tensor, output_size: List[int]) -> torch.Tensor:
return center_crop_image_tensor(img=segmentation_mask, output_size=output_size)


def resized_crop_image_tensor(
img: torch.Tensor,
top: int,
Expand Down