Extend `RandomShortestSize` to support Video specific flavour of the augmentation (#6770)

* Extend RandomShortestSize to support Video specific flavour of the augmentation

* Adding a test.

* Apply changes from code review

test/test_prototype_transforms.py

@@ -1379,10 +1379,9 @@ class TestScaleJitter:
 
 
 class TestRandomShortestSize:
-    def test__get_params(self, mocker):
+    @pytest.mark.parametrize("min_size,max_size", [([5, 9], 20), ([5, 9], None)])
+    def test__get_params(self, min_size, max_size, mocker):
         spatial_size = (3, 10)
-        min_size = [5, 9]
-        max_size = 20
 
         transform = transforms.RandomShortestSize(min_size=min_size, max_size=max_size)
 
@@ -1395,10 +1394,9 @@ class TestRandomShortestSize:
         assert isinstance(size, tuple) and len(size) == 2
 
         longer = max(size)
-        assert longer <= max_size
-
         shorter = min(size)
-        if longer == max_size:
+        if max_size is not None:
+            assert longer <= max_size
             assert shorter <= max_size
         else:
             assert shorter in min_size

torchvision/prototype/transforms/_geometry.py

@@ -730,7 +730,7 @@ class RandomShortestSize(Transform):
     def __init__(
         self,
         min_size: Union[List[int], Tuple[int], int],
-        max_size: int,
+        max_size: Optional[int] = None,
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
         antialias: Optional[bool] = None,
     ):
@@ -744,7 +744,9 @@ class RandomShortestSize(Transform):
         orig_height, orig_width = query_spatial_size(flat_inputs)
 
         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))
+        r = min_size / min(orig_height, orig_width)
+        if self.max_size is not None:
+            r = min(r, self.max_size / max(orig_height, orig_width))
 
         new_width = int(orig_width * r)
         new_height = int(orig_height * r)