[fbsync] [proto] Ported RandomIoUCrop from detection refs (#6401)

Summary:
* [proto] Ported RandomIoUCrop from detection refs

* Scope acceptable data types

* Added get_params test

* Added test__transform_empty_params

* Added support for OneHotLabel and tests

* Added tests for mask

* Updated error message

* Apply suggestions from code review

* Added support for OHE masks and tests

* Ignored mypy error

* Fixed forward call on sample

* Added a todo

Reviewed By: datumbox

Differential Revision: D39013670

fbshipit-source-id: 47101bf538c88a5905ab6e5f4c5984271b8c57cd

Co-authored-by: Philip Meier <[email protected]>
Co-authored-by: Philip Meier <[email protected]>

Author: 2 people

test/test_prototype_transforms.py

@@ -6,7 +6,7 @@
 
 import pytest
 import torch
-from common_utils import assert_equal
+from common_utils import assert_equal, cpu_and_gpu
 from test_prototype_transforms_functional import (
     make_bounding_box,
     make_bounding_boxes,
@@ -15,6 +15,7 @@
     make_one_hot_labels,
     make_segmentation_mask,
 )
+from torchvision.ops.boxes import box_iou
 from torchvision.prototype import features, transforms
 from torchvision.transforms.functional import InterpolationMode, pil_to_tensor, to_pil_image
 
@@ -1127,6 +1128,124 @@ def test_ctor(self, trfms):
         assert isinstance(output, torch.Tensor)
 
 
+class TestRandomIoUCrop:
+    @pytest.mark.parametrize("device", cpu_and_gpu())
+    @pytest.mark.parametrize("options", [[0.5, 0.9], [2.0]])
+    def test__get_params(self, device, options, mocker):
+        image = mocker.MagicMock(spec=features.Image)
+        image.num_channels = 3
+        image.image_size = (24, 32)
+        bboxes = features.BoundingBox(
+            torch.tensor([[1, 1, 10, 10], [20, 20, 23, 23], [1, 20, 10, 23], [20, 1, 23, 10]]),
+            format="XYXY",
+            image_size=image.image_size,
+            device=device,
+        )
+        sample = [image, bboxes]
+
+        transform = transforms.RandomIoUCrop(sampler_options=options)
+
+        n_samples = 5
+        for _ in range(n_samples):
+
+            params = transform._get_params(sample)
+
+            if options == [2.0]:
+                assert len(params) == 0
+                return
+
+            assert len(params["is_within_crop_area"]) > 0
+            assert params["is_within_crop_area"].dtype == torch.bool
+
+            orig_h = image.image_size[0]
+            orig_w = image.image_size[1]
+            assert int(transform.min_scale * orig_h) <= params["height"] <= int(transform.max_scale * orig_h)
+            assert int(transform.min_scale * orig_w) <= params["width"] <= int(transform.max_scale * orig_w)
+
+            left, top = params["left"], params["top"]
+            new_h, new_w = params["height"], params["width"]
+            ious = box_iou(
+                bboxes,
+                torch.tensor([[left, top, left + new_w, top + new_h]], dtype=bboxes.dtype, device=bboxes.device),
+            )
+            assert ious.max() >= options[0] or ious.max() >= options[1], f"{ious} vs {options}"
+
+    def test__transform_empty_params(self, mocker):
+        transform = transforms.RandomIoUCrop(sampler_options=[2.0])
+        image = features.Image(torch.rand(1, 3, 4, 4))
+        bboxes = features.BoundingBox(torch.tensor([[1, 1, 2, 2]]), format="XYXY", image_size=(4, 4))
+        label = features.Label(torch.tensor([1]))
+        sample = [image, bboxes, label]
+        # Let's mock transform._get_params to control the output:
+        transform._get_params = mocker.MagicMock(return_value={})
+        output = transform(sample)
+        torch.testing.assert_close(output, sample)
+
+    def test_forward_assertion(self):
+        transform = transforms.RandomIoUCrop()
+        with pytest.raises(
+            TypeError,
+            match="requires input sample to contain Images or PIL Images, BoundingBoxes and Labels or OneHotLabels",
+        ):
+            transform(torch.tensor(0))
+
+    def test__transform(self, mocker):
+        transform = transforms.RandomIoUCrop()
+
+        image = features.Image(torch.rand(3, 32, 24))
+        bboxes = make_bounding_box(format="XYXY", image_size=(32, 24), extra_dims=(6,))
+        label = features.Label(torch.randint(0, 10, size=(6,)))
+        ohe_label = features.OneHotLabel(torch.zeros(6, 10).scatter_(1, label.unsqueeze(1), 1))
+        masks = make_segmentation_mask((32, 24))
+        ohe_masks = features.SegmentationMask(torch.randint(0, 2, size=(6, 32, 24)))
+        sample = [image, bboxes, label, ohe_label, masks, ohe_masks]
+
+        fn = mocker.patch("torchvision.prototype.transforms.functional.crop", side_effect=lambda x, **params: x)
+        is_within_crop_area = torch.tensor([0, 1, 0, 1, 0, 1], dtype=torch.bool)
+
+        params = dict(top=1, left=2, height=12, width=12, is_within_crop_area=is_within_crop_area)
+        transform._get_params = mocker.MagicMock(return_value=params)
+        output = transform(sample)
+
+        assert fn.call_count == 4
+
+        expected_calls = [
+            mocker.call(image, top=params["top"], left=params["left"], height=params["height"], width=params["width"]),
+            mocker.call(bboxes, top=params["top"], left=params["left"], height=params["height"], width=params["width"]),
+            mocker.call(masks, top=params["top"], left=params["left"], height=params["height"], width=params["width"]),
+            mocker.call(
+                ohe_masks, top=params["top"], left=params["left"], height=params["height"], width=params["width"]
+            ),
+        ]
+
+        fn.assert_has_calls(expected_calls)
+
+        expected_within_targets = sum(is_within_crop_area)
+
+        # check number of bboxes vs number of labels:
+        output_bboxes = output[1]
+        assert isinstance(output_bboxes, features.BoundingBox)
+        assert len(output_bboxes) == expected_within_targets
+
+        # check labels
+        output_label = output[2]
+        assert isinstance(output_label, features.Label)
+        assert len(output_label) == expected_within_targets
+        torch.testing.assert_close(output_label, label[is_within_crop_area])
+
+        output_ohe_label = output[3]
+        assert isinstance(output_ohe_label, features.OneHotLabel)
+        torch.testing.assert_close(output_ohe_label, ohe_label[is_within_crop_area])
+
+        output_masks = output[4]
+        assert isinstance(output_masks, features.SegmentationMask)
+        assert output_masks.shape[:-2] == masks.shape[:-2]
+
+        output_ohe_masks = output[5]
+        assert isinstance(output_ohe_masks, features.SegmentationMask)
+        assert len(output_ohe_masks) == expected_within_targets
+
+
 class TestScaleJitter:
     def test__get_params(self, mocker):
         image_size = (24, 32)

torchvision/prototype/transforms/__init__.py

@@ -24,6 +24,7 @@
     RandomAffine,
     RandomCrop,
     RandomHorizontalFlip,
+    RandomIoUCrop,
     RandomPerspective,
     RandomResizedCrop,
     RandomRotation,

torchvision/prototype/transforms/_geometry.py

@@ -5,15 +5,17 @@
 
 import PIL.Image
 import torch
+from torchvision.ops.boxes import box_iou
 from torchvision.prototype import features
 from torchvision.prototype.transforms import functional as F, Transform
 from torchvision.transforms.functional import InterpolationMode, pil_to_tensor
 from torchvision.transforms.functional_tensor import _parse_pad_padding
 from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size
+
 from typing_extensions import Literal
 
 from ._transform import _RandomApplyTransform
-from ._utils import get_image_dimensions, has_any, is_simple_tensor, query_image
+from ._utils import get_image_dimensions, has_all, has_any, is_simple_tensor, query_bounding_box, query_image
 
 
 class RandomHorizontalFlip(_RandomApplyTransform):
@@ -620,6 +622,116 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         )
 
 
+class RandomIoUCrop(Transform):
+    def __init__(
+        self,
+        min_scale: float = 0.3,
+        max_scale: float = 1.0,
+        min_aspect_ratio: float = 0.5,
+        max_aspect_ratio: float = 2.0,
+        sampler_options: Optional[List[float]] = None,
+        trials: int = 40,
+    ):
+        super().__init__()
+        # Configuration similar to https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_coco.py#L89-L174
+        self.min_scale = min_scale
+        self.max_scale = max_scale
+        self.min_aspect_ratio = min_aspect_ratio
+        self.max_aspect_ratio = max_aspect_ratio
+        if sampler_options is None:
+            sampler_options = [0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
+        self.options = sampler_options
+        self.trials = trials
+
+    def _get_params(self, sample: Any) -> Dict[str, Any]:
+
+        image = query_image(sample)
+        _, orig_h, orig_w = get_image_dimensions(image)
+        bboxes = query_bounding_box(sample)
+
+        while True:
+            # sample an option
+            idx = int(torch.randint(low=0, high=len(self.options), size=(1,)))
+            min_jaccard_overlap = self.options[idx]
+            if min_jaccard_overlap >= 1.0:  # a value larger than 1 encodes the leave as-is option
+                return dict()
+
+            for _ in range(self.trials):
+                # check the aspect ratio limitations
+                r = self.min_scale + (self.max_scale - self.min_scale) * torch.rand(2)
+                new_w = int(orig_w * r[0])
+                new_h = int(orig_h * r[1])
+                aspect_ratio = new_w / new_h
+                if not (self.min_aspect_ratio <= aspect_ratio <= self.max_aspect_ratio):
+                    continue
+
+                # check for 0 area crops
+                r = torch.rand(2)
+                left = int((orig_w - new_w) * r[0])
+                top = int((orig_h - new_h) * r[1])
+                right = left + new_w
+                bottom = top + new_h
+                if left == right or top == bottom:
+                    continue
+
+                # check for any valid boxes with centers within the crop area
+                xyxy_bboxes = F.convert_bounding_box_format(
+                    bboxes, old_format=bboxes.format, new_format=features.BoundingBoxFormat.XYXY, copy=True
+                )
+                cx = 0.5 * (xyxy_bboxes[..., 0] + xyxy_bboxes[..., 2])
+                cy = 0.5 * (xyxy_bboxes[..., 1] + xyxy_bboxes[..., 3])
+                is_within_crop_area = (left < cx) & (cx < right) & (top < cy) & (cy < bottom)
+                if not is_within_crop_area.any():
+                    continue
+
+                # check at least 1 box with jaccard limitations
+                xyxy_bboxes = xyxy_bboxes[is_within_crop_area]
+                ious = box_iou(
+                    xyxy_bboxes,
+                    torch.tensor([[left, top, right, bottom]], dtype=xyxy_bboxes.dtype, device=xyxy_bboxes.device),
+                )
+                if ious.max() < min_jaccard_overlap:
+                    continue
+
+                return dict(top=top, left=left, height=new_h, width=new_w, is_within_crop_area=is_within_crop_area)
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        if len(params) < 1:
+            return inpt
+
+        is_within_crop_area = params["is_within_crop_area"]
+
+        if isinstance(inpt, (features.Label, features.OneHotLabel)):
+            return inpt.new_like(inpt, inpt[is_within_crop_area])  # type: ignore[arg-type]
+
+        output = F.crop(inpt, top=params["top"], left=params["left"], height=params["height"], width=params["width"])
+
+        if isinstance(output, features.BoundingBox):
+            bboxes = output[is_within_crop_area]
+            bboxes = F.clamp_bounding_box(bboxes, output.format, output.image_size)
+            output = features.BoundingBox.new_like(output, bboxes)
+        elif isinstance(output, features.SegmentationMask) and output.shape[-3] > 1:
+            # apply is_within_crop_area if mask is one-hot encoded
+            masks = output[is_within_crop_area]
+            output = features.SegmentationMask.new_like(output, masks)
+
+        return output
+
+    def forward(self, *inputs: Any) -> Any:
+        sample = inputs if len(inputs) > 1 else inputs[0]
+        # TODO: Allow image to be a torch.Tensor
+        if not (
+            has_all(sample, features.BoundingBox)
+            and has_any(sample, PIL.Image.Image, features.Image)
+            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)
+
+
 class ScaleJitter(Transform):
     def __init__(
         self,

torchvision/prototype/transforms/_utils.py

@@ -17,6 +17,15 @@ def query_image(sample: Any) -> Union[PIL.Image.Image, torch.Tensor, features.Im
     raise TypeError("No image was found in the sample")
 
 
+def query_bounding_box(sample: Any) -> features.BoundingBox:
+    flat_sample, _ = tree_flatten(sample)
+    for i in flat_sample:
+        if isinstance(i, features.BoundingBox):
+            return i
+
+    raise TypeError("No bounding box was found in the sample")
+
+
 def get_image_dimensions(image: Union[PIL.Image.Image, torch.Tensor, features.Image]) -> Tuple[int, int, int]:
     if isinstance(image, features.Image):
         channels = image.num_channels

torchvision/prototype/transforms/functional/__init__.py

@@ -1,5 +1,6 @@
 from torchvision.transforms import InterpolationMode  # usort: skip
 from ._meta import (
+    clamp_bounding_box,
     convert_bounding_box_format,
     convert_color_space_image_tensor,
     convert_color_space_image_pil,

torchvision/prototype/transforms/functional/_meta.py

@@ -61,6 +61,15 @@ def convert_bounding_box_format(
     return bounding_box
 
 
+def clamp_bounding_box(
+    bounding_box: torch.Tensor, format: BoundingBoxFormat, image_size: Tuple[int, int]
+) -> torch.Tensor:
+    xyxy_boxes = convert_bounding_box_format(bounding_box, format, BoundingBoxFormat.XYXY)
+    xyxy_boxes[..., 0::2].clamp_(min=0, max=image_size[1])
+    xyxy_boxes[..., 1::2].clamp_(min=0, max=image_size[0])
+    return convert_bounding_box_format(xyxy_boxes, BoundingBoxFormat.XYXY, format, copy=False)
+
+
 def _split_alpha(image: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
     return image[..., :-1, :, :], image[..., -1:, :, :]