port `RandomShortestSize` from detection references to prototype transforms (#6418)

* port `RandomShortestSize` from detection references to prototype transforms

* mypy

* add test

test/test_prototype_transforms.py

@@ -1164,3 +1164,48 @@ class TestScaleJitter:
         transform(inpt_sentinel)
 
         mock.assert_called_once_with(inpt_sentinel, size=size_sentinel, interpolation=interpolation_sentinel)
+
+
+class TestRandomShortestSize:
+    def test__get_params(self, mocker):
+        image_size = (3, 10)
+        min_size = [5, 9]
+        max_size = 20
+
+        transform = transforms.RandomShortestSize(min_size=min_size, max_size=max_size)
+
+        sample = mocker.MagicMock(spec=features.Image, num_channels=3, image_size=image_size)
+        params = transform._get_params(sample)
+
+        assert "size" in params
+        size = params["size"]
+
+        assert isinstance(size, tuple) and len(size) == 2
+
+        longer = max(size)
+        assert longer <= max_size
+
+        shorter = min(size)
+        if longer == max_size:
+            assert shorter <= max_size
+        else:
+            assert shorter in min_size
+
+    def test__transform(self, mocker):
+        interpolation_sentinel = mocker.MagicMock()
+
+        transform = transforms.RandomShortestSize(min_size=[3, 5, 7], max_size=12, interpolation=interpolation_sentinel)
+        transform._transformed_types = (mocker.MagicMock,)
+
+        size_sentinel = mocker.MagicMock()
+        mocker.patch(
+            "torchvision.prototype.transforms._geometry.RandomShortestSize._get_params",
+            return_value=dict(size=size_sentinel),
+        )
+
+        inpt_sentinel = mocker.MagicMock()
+
+        mock = mocker.patch("torchvision.prototype.transforms._geometry.F.resize")
+        transform(inpt_sentinel)
+
+        mock.assert_called_once_with(inpt_sentinel, size=size_sentinel, interpolation=interpolation_sentinel)

torchvision/prototype/transforms/__init__.py

@@ -27,6 +27,7 @@ from ._geometry import (
     RandomPerspective,
     RandomResizedCrop,
     RandomRotation,
+    RandomShortestSize,
     RandomVerticalFlip,
     RandomZoomOut,
     Resize,

torchvision/prototype/transforms/_geometry.py

@@ -644,3 +644,31 @@ class ScaleJitter(Transform):
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         return F.resize(inpt, size=params["size"], interpolation=self.interpolation)
+
+
+class RandomShortestSize(Transform):
+    def __init__(
+        self,
+        min_size: Union[List[int], Tuple[int], int],
+        max_size: int,
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+    ):
+        super().__init__()
+        self.min_size = [min_size] if isinstance(min_size, int) else list(min_size)
+        self.max_size = max_size
+        self.interpolation = interpolation
+
+    def _get_params(self, sample: Any) -> Dict[str, Any]:
+        image = query_image(sample)
+        _, orig_height, orig_width = get_image_dimensions(image)
+
+        min_size = self.min_size[int(torch.randint(len(self.min_size), ()))]
+        r = min(min_size / min(orig_height, orig_width), self.max_size / max(orig_height, orig_width))
+
+        new_width = int(orig_width * r)
+        new_height = int(orig_height * r)
+
+        return dict(size=(new_height, new_width))
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return F.resize(inpt, size=params["size"], interpolation=self.interpolation)