port `ScaleJitter` from detection reference to prototype transforms (#6411)

* port ScaleJitter from detection reference to prototype transforms

* add test

* use MagicMock as sentinel

test/test_prototype_transforms.py

@@ -1125,3 +1125,42 @@ class TestCompose:
         inpt = torch.rand(1, 3, 32, 32)
         output = c(inpt)
         assert isinstance(output, torch.Tensor)
+
+
+class TestScaleJitter:
+    def test__get_params(self, mocker):
+        image_size = (24, 32)
+        target_size = (16, 12)
+        scale_range = (0.5, 1.5)
+
+        transform = transforms.ScaleJitter(target_size=target_size, scale_range=scale_range)
+
+        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
+        height, width = size
+
+        assert int(target_size[0] * scale_range[0]) <= height <= int(target_size[0] * scale_range[1])
+        assert int(target_size[1] * scale_range[0]) <= width <= int(target_size[1] * scale_range[1])
+
+    def test__transform(self, mocker):
+        interpolation_sentinel = mocker.MagicMock()
+
+        transform = transforms.ScaleJitter(target_size=(16, 12), interpolation=interpolation_sentinel)
+        transform._transformed_types = (mocker.MagicMock,)
+
+        size_sentinel = mocker.MagicMock()
+        mocker.patch(
+            "torchvision.prototype.transforms._geometry.ScaleJitter._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

@@ -30,6 +30,7 @@ from ._geometry import (
     RandomVerticalFlip,
     RandomZoomOut,
     Resize,
+    ScaleJitter,
     TenCrop,
 )
 from ._meta import ConvertBoundingBoxFormat, ConvertImageColorSpace, ConvertImageDtype

torchvision/prototype/transforms/_geometry.py

@@ -631,3 +631,29 @@ class ElasticTransform(Transform):
             fill=self.fill,
             interpolation=self.interpolation,
         )
+
+
+class ScaleJitter(Transform):
+    def __init__(
+        self,
+        target_size: Tuple[int, int],
+        scale_range: Tuple[float, float] = (0.1, 2.0),
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+    ):
+        super().__init__()
+        self.target_size = target_size
+        self.scale_range = scale_range
+        self.interpolation = interpolation
+
+    def _get_params(self, sample: Any) -> Dict[str, Any]:
+        image = query_image(sample)
+        _, orig_height, orig_width = get_image_dimensions(image)
+
+        r = self.scale_range[0] + torch.rand(1) * (self.scale_range[1] - self.scale_range[0])
+        new_width = int(self.target_size[1] * r)
+        new_height = int(self.target_size[0] * 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)