port `FixedSizeCrop` from detection references to prototype transforms (#6417)

* port `FixedSizeCrop` from detection references to prototype transforms

* mypy

* [skip ci] call invalid boxes and corresponding masks and labels

* cherry-pick missing functions from #6401

* fix feature wrapping

* add test

* mypy

* add input type restrictions

* add test for _get_params

* fix input checks

test/test_prototype_transforms.py

@@ -10,6 +10,7 @@ from common_utils import assert_equal, cpu_and_gpu
 from test_prototype_transforms_functional import (
     make_bounding_box,
     make_bounding_boxes,
+    make_image,
     make_images,
     make_label,
     make_one_hot_labels,
@@ -1328,3 +1329,161 @@ class TestRandomShortestSize:
         transform(inpt_sentinel)
 
         mock.assert_called_once_with(inpt_sentinel, size=size_sentinel, interpolation=interpolation_sentinel)
+
+
+class TestFixedSizeCrop:
+    def test__get_params(self, mocker):
+        crop_size = (7, 7)
+        batch_shape = (10,)
+        image_size = (11, 5)
+
+        transform = transforms.FixedSizeCrop(size=crop_size)
+
+        sample = dict(
+            image=make_image(size=image_size, color_space=features.ColorSpace.RGB),
+            bounding_boxes=make_bounding_box(
+                format=features.BoundingBoxFormat.XYXY, image_size=image_size, extra_dims=batch_shape
+            ),
+        )
+        params = transform._get_params(sample)
+
+        assert params["needs_crop"]
+        assert params["height"] <= crop_size[0]
+        assert params["width"] <= crop_size[1]
+
+        assert (
+            isinstance(params["is_valid"], torch.Tensor)
+            and params["is_valid"].dtype is torch.bool
+            and params["is_valid"].shape == batch_shape
+        )
+
+        assert params["needs_pad"]
+        assert any(pad > 0 for pad in params["padding"])
+
+    @pytest.mark.parametrize("needs", list(itertools.product((False, True), repeat=2)))
+    def test__transform(self, mocker, needs):
+        fill_sentinel = mocker.MagicMock()
+        padding_mode_sentinel = mocker.MagicMock()
+
+        transform = transforms.FixedSizeCrop((-1, -1), fill=fill_sentinel, padding_mode=padding_mode_sentinel)
+        transform._transformed_types = (mocker.MagicMock,)
+        mocker.patch("torchvision.prototype.transforms._geometry.has_all", return_value=True)
+        mocker.patch("torchvision.prototype.transforms._geometry.has_any", return_value=True)
+
+        needs_crop, needs_pad = needs
+        top_sentinel = mocker.MagicMock()
+        left_sentinel = mocker.MagicMock()
+        height_sentinel = mocker.MagicMock()
+        width_sentinel = mocker.MagicMock()
+        padding_sentinel = mocker.MagicMock()
+        mocker.patch(
+            "torchvision.prototype.transforms._geometry.FixedSizeCrop._get_params",
+            return_value=dict(
+                needs_crop=needs_crop,
+                top=top_sentinel,
+                left=left_sentinel,
+                height=height_sentinel,
+                width=width_sentinel,
+                padding=padding_sentinel,
+                needs_pad=needs_pad,
+            ),
+        )
+
+        inpt_sentinel = mocker.MagicMock()
+
+        mock_crop = mocker.patch("torchvision.prototype.transforms._geometry.F.crop")
+        mock_pad = mocker.patch("torchvision.prototype.transforms._geometry.F.pad")
+        transform(inpt_sentinel)
+
+        if needs_crop:
+            mock_crop.assert_called_once_with(
+                inpt_sentinel,
+                top=top_sentinel,
+                left=left_sentinel,
+                height=height_sentinel,
+                width=width_sentinel,
+            )
+        else:
+            mock_crop.assert_not_called()
+
+        if needs_pad:
+            # If we cropped before, the input to F.pad is no longer inpt_sentinel. Thus, we can't use
+            # `MagicMock.assert_called_once_with` and have to perform the checks manually
+            mock_pad.assert_called_once()
+            args, kwargs = mock_pad.call_args
+            if not needs_crop:
+                assert args[0] is inpt_sentinel
+            assert args[1] is padding_sentinel
+            assert kwargs == dict(fill=fill_sentinel, padding_mode=padding_mode_sentinel)
+        else:
+            mock_pad.assert_not_called()
+
+    def test__transform_culling(self, mocker):
+        batch_size = 10
+        image_size = (10, 10)
+
+        is_valid = torch.randint(0, 2, (batch_size,), dtype=torch.bool)
+        mocker.patch(
+            "torchvision.prototype.transforms._geometry.FixedSizeCrop._get_params",
+            return_value=dict(
+                needs_crop=True,
+                top=0,
+                left=0,
+                height=image_size[0],
+                width=image_size[1],
+                is_valid=is_valid,
+                needs_pad=False,
+            ),
+        )
+
+        bounding_boxes = make_bounding_box(
+            format=features.BoundingBoxFormat.XYXY, image_size=image_size, extra_dims=(batch_size,)
+        )
+        segmentation_masks = make_segmentation_mask(size=image_size, extra_dims=(batch_size,))
+        labels = make_label(size=(batch_size,))
+
+        transform = transforms.FixedSizeCrop((-1, -1))
+        mocker.patch("torchvision.prototype.transforms._geometry.has_all", return_value=True)
+        mocker.patch("torchvision.prototype.transforms._geometry.has_any", return_value=True)
+
+        output = transform(
+            dict(
+                bounding_boxes=bounding_boxes,
+                segmentation_masks=segmentation_masks,
+                labels=labels,
+            )
+        )
+
+        assert_equal(output["bounding_boxes"], bounding_boxes[is_valid])
+        assert_equal(output["segmentation_masks"], segmentation_masks[is_valid])
+        assert_equal(output["labels"], labels[is_valid])
+
+    def test__transform_bounding_box_clamping(self, mocker):
+        batch_size = 3
+        image_size = (10, 10)
+
+        mocker.patch(
+            "torchvision.prototype.transforms._geometry.FixedSizeCrop._get_params",
+            return_value=dict(
+                needs_crop=True,
+                top=0,
+                left=0,
+                height=image_size[0],
+                width=image_size[1],
+                is_valid=torch.full((batch_size,), fill_value=True),
+                needs_pad=False,
+            ),
+        )
+
+        bounding_box = make_bounding_box(
+            format=features.BoundingBoxFormat.XYXY, image_size=image_size, extra_dims=(batch_size,)
+        )
+        mock = mocker.patch("torchvision.prototype.transforms._geometry.F.clamp_bounding_box")
+
+        transform = transforms.FixedSizeCrop((-1, -1))
+        mocker.patch("torchvision.prototype.transforms._geometry.has_all", return_value=True)
+        mocker.patch("torchvision.prototype.transforms._geometry.has_any", return_value=True)
+
+        transform(bounding_box)
+
+        mock.assert_called_once()

torchvision/prototype/transforms/__init__.py

@@ -20,6 +20,7 @@ from ._geometry import (
     CenterCrop,
     ElasticTransform,
     FiveCrop,
+    FixedSizeCrop,
     Pad,
     RandomAffine,
     RandomCrop,

torchvision/prototype/transforms/_geometry.py

@@ -783,3 +783,100 @@ class RandomShortestSize(Transform):
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         return F.resize(inpt, size=params["size"], interpolation=self.interpolation)
+
+
+class FixedSizeCrop(Transform):
+    def __init__(
+        self,
+        size: Union[int, Sequence[int]],
+        fill: Union[int, float, Sequence[int], Sequence[float]] = 0,
+        padding_mode: str = "constant",
+    ) -> None:
+        super().__init__()
+        size = tuple(_setup_size(size, error_msg="Please provide only two dimensions (h, w) for size."))
+        self.crop_height = size[0]
+        self.crop_width = size[1]
+        self.fill = fill  # TODO: Fill is currently respected only on PIL. Apply tensor patch.
+        self.padding_mode = padding_mode
+
+    def _get_params(self, sample: Any) -> Dict[str, Any]:
+        image = query_image(sample)
+        _, height, width = get_image_dimensions(image)
+        new_height = min(height, self.crop_height)
+        new_width = min(width, self.crop_width)
+
+        needs_crop = new_height != height or new_width != width
+
+        offset_height = max(height - self.crop_height, 0)
+        offset_width = max(width - self.crop_width, 0)
+
+        r = torch.rand(1)
+        top = int(offset_height * r)
+        left = int(offset_width * r)
+
+        if needs_crop:
+            bounding_boxes = query_bounding_box(sample)
+            bounding_boxes = cast(
+                features.BoundingBox, F.crop(bounding_boxes, top=top, left=left, height=height, width=width)
+            )
+            bounding_boxes = features.BoundingBox.new_like(
+                bounding_boxes,
+                F.clamp_bounding_box(
+                    bounding_boxes, format=bounding_boxes.format, image_size=bounding_boxes.image_size
+                ),
+            )
+            height_and_width = bounding_boxes.to_format(features.BoundingBoxFormat.XYWH)[..., 2:]
+            is_valid = torch.all(height_and_width > 0, dim=-1)
+        else:
+            is_valid = None
+
+        pad_bottom = max(self.crop_height - new_height, 0)
+        pad_right = max(self.crop_width - new_width, 0)
+
+        needs_pad = pad_bottom != 0 or pad_right != 0
+
+        return dict(
+            needs_crop=needs_crop,
+            top=top,
+            left=left,
+            height=new_height,
+            width=new_width,
+            is_valid=is_valid,
+            padding=[0, 0, pad_right, pad_bottom],
+            needs_pad=needs_pad,
+        )
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        if params["needs_crop"]:
+            inpt = F.crop(
+                inpt,
+                top=params["top"],
+                left=params["left"],
+                height=params["height"],
+                width=params["width"],
+            )
+            if isinstance(inpt, (features.Label, features.OneHotLabel, features.SegmentationMask)):
+                inpt = inpt.new_like(inpt, inpt[params["is_valid"]])  # type: ignore[arg-type]
+            elif isinstance(inpt, features.BoundingBox):
+                inpt = features.BoundingBox.new_like(
+                    inpt,
+                    F.clamp_bounding_box(inpt[params["is_valid"]], format=inpt.format, image_size=inpt.image_size),
+                )
+
+        if params["needs_pad"]:
+            inpt = F.pad(inpt, params["padding"], fill=self.fill, padding_mode=self.padding_mode)
+
+        return inpt
+
+    def forward(self, *inputs: Any) -> Any:
+        sample = inputs if len(inputs) > 1 else inputs[0]
+        if not (
+            has_all(sample, features.BoundingBox)
+            and has_any(sample, PIL.Image.Image, features.Image, is_simple_tensor)
+            and has_any(sample, features.Label, features.OneHotLabel)
+        ):
+            raise TypeError(
+                f"{type(self).__name__}() requires input sample to contain Images or PIL Images, "
+                "BoundingBoxes and Labels or OneHotLabels. Sample can also contain Segmentation Masks."
+            )
+        return super().forward(sample)