port RandomResize from segmentation references (#6561)

* port RandomResize from segmentation references

* mypy

* remove optional max_size

* add interpolation and antialias

* [SKIP CI] only CircleCI

* fix bug

* [SKIP CI] only CircleCI

* [SKIP CI] add test

test/test_prototype_transforms.py

@@ -1670,3 +1670,43 @@ class TestLabelToOneHot:
         assert isinstance(ohe_labels, features.OneHotLabel)
         assert ohe_labels.shape == (4, 3)
         assert ohe_labels.categories == labels.categories == categories
+
+
+class TestRandomResize:
+    def test__get_params(self):
+        min_size = 3
+        max_size = 6
+
+        transform = transforms.RandomResize(min_size=min_size, max_size=max_size)
+
+        for _ in range(10):
+            params = transform._get_params(None)
+
+            assert isinstance(params["size"], list) and len(params["size"]) == 1
+            size = params["size"][0]
+
+            assert min_size <= size < max_size
+
+    def test__transform(self, mocker):
+        interpolation_sentinel = mocker.MagicMock()
+        antialias_sentinel = mocker.MagicMock()
+
+        transform = transforms.RandomResize(
+            min_size=-1, max_size=-1, interpolation=interpolation_sentinel, antialias=antialias_sentinel
+        )
+        transform._transformed_types = (mocker.MagicMock,)
+
+        size_sentinel = mocker.MagicMock()
+        mocker.patch(
+            "torchvision.prototype.transforms._geometry.RandomResize._get_params",
+            return_value=dict(size=size_sentinel),
+        )
+
+        inpt_sentinel = mocker.MagicMock()
+
+        mock_resize = mocker.patch("torchvision.prototype.transforms._geometry.F.resize")
+        transform(inpt_sentinel)
+
+        mock_resize.assert_called_with(
+            inpt_sentinel, size_sentinel, interpolation=interpolation_sentinel, antialias=antialias_sentinel
+        )

torchvision/prototype/transforms/__init__.py

@@ -28,6 +28,7 @@ from ._geometry import (
     RandomHorizontalFlip,
     RandomIoUCrop,
     RandomPerspective,
+    RandomResize,
     RandomResizedCrop,
     RandomRotation,
     RandomShortestSize,

torchvision/prototype/transforms/_geometry.py

@@ -866,3 +866,25 @@ class FixedSizeCrop(Transform):
             )
 
         return super().forward(*inputs)
+
+
+class RandomResize(Transform):
+    def __init__(
+        self,
+        min_size: int,
+        max_size: int,
+        interpolation: InterpolationMode = InterpolationMode.BILINEAR,
+        antialias: Optional[bool] = None,
+    ) -> None:
+        super().__init__()
+        self.min_size = min_size
+        self.max_size = max_size
+        self.interpolation = interpolation
+        self.antialias = antialias
+
+    def _get_params(self, sample: Any) -> Dict[str, Any]:
+        size = int(torch.randint(self.min_size, self.max_size, ()))
+        return dict(size=[size])
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return F.resize(inpt, params["size"], interpolation=self.interpolation, antialias=self.antialias)