port tests for transforms.RandomZoomOut

test/test_transforms_v2.py

@@ -136,7 +136,6 @@ class TestSmoke:
             (transforms.RandomRotation(degrees=30), None),
             (transforms.RandomShortestSize(min_size=10, antialias=True), None),
             (transforms.RandomVerticalFlip(p=1.0), None),
-            (transforms.RandomZoomOut(p=1.0), None),
             (transforms.Resize([16, 16], antialias=True), None),
             (transforms.ScaleJitter((16, 16), scale_range=(0.8, 1.2), antialias=True), None),
             (transforms.ClampBoundingBoxes(), None),
@@ -388,34 +387,6 @@ def was_applied(output, inpt):
         assert transform.was_applied(output, input)
 
 
-class TestRandomZoomOut:
-    def test_assertions(self):
-        with pytest.raises(TypeError, match="Got inappropriate fill arg"):
-            transforms.RandomZoomOut(fill="abc")
-
-        with pytest.raises(TypeError, match="should be a sequence of length"):
-            transforms.RandomZoomOut(0, side_range=0)
-
-        with pytest.raises(ValueError, match="Invalid canvas side range"):
-            transforms.RandomZoomOut(0, side_range=[4.0, 1.0])
-
-    @pytest.mark.parametrize("fill", [0, [1, 2, 3], (2, 3, 4)])
-    @pytest.mark.parametrize("side_range", [(1.0, 4.0), [2.0, 5.0]])
-    def test__get_params(self, fill, side_range):
-        transform = transforms.RandomZoomOut(fill=fill, side_range=side_range)
-
-        h, w = size = (24, 32)
-        image = make_image(size)
-
-        params = transform._get_params([image])
-
-        assert len(params["padding"]) == 4
-        assert 0 <= params["padding"][0] <= (side_range[1] - 1) * w
-        assert 0 <= params["padding"][1] <= (side_range[1] - 1) * h
-        assert 0 <= params["padding"][2] <= (side_range[1] - 1) * w
-        assert 0 <= params["padding"][3] <= (side_range[1] - 1) * h
-
-
 class TestElasticTransform:
     def test_assertions(self):
 

test/test_transforms_v2_refactored.py

@@ -4000,3 +4000,67 @@ def test_random_transform_correctness(self, num_input_channels):
         expected = F.to_image(F.rgb_to_grayscale(F.to_pil_image(image), num_output_channels=num_input_channels))
 
         assert_equal(actual, expected, rtol=0, atol=1)
+
+
+class TestRandomZoomOut:
+    # Tests are light because this largely relies on the already tested `pad` kernels.
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [
+            make_image_tensor,
+            make_image_pil,
+            make_image,
+            make_bounding_boxes,
+            make_segmentation_mask,
+            make_detection_mask,
+            make_video,
+        ],
+    )
+    def test_transform(self, make_input):
+        check_transform(transforms.RandomZoomOut(p=1), make_input())
+
+    def test_transform_error(self):
+        for side_range in [None, 1, [1, 2, 3]]:
+            with pytest.raises(
+                ValueError if isinstance(side_range, list) else TypeError, match="should be a sequence of length 2"
+            ):
+                transforms.RandomZoomOut(side_range=side_range)
+
+        for side_range in [[0.5, 1.5], [2.0, 1.0]]:
+            with pytest.raises(ValueError, match="Invalid side range"):
+                transforms.RandomZoomOut(side_range=side_range)
+
+    @pytest.mark.parametrize("side_range", [(1.0, 4.0), [2.0, 5.0]])
+    @pytest.mark.parametrize(
+        "make_input",
+        [
+            make_image_tensor,
+            make_image_pil,
+            make_image,
+            make_bounding_boxes,
+            make_segmentation_mask,
+            make_detection_mask,
+            make_video,
+        ],
+    )
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_transform_params_correctness(self, side_range, make_input, device):
+        if make_input is make_image_pil and device != "cpu":
+            pytest.skip("PIL image tests with parametrization device!='cpu' will degenerate to that anyway.")
+
+        transform = transforms.RandomZoomOut(side_range=side_range)
+
+        input = make_input()
+        height, width = F.get_size(input)
+
+        params = transform._get_params([input])
+        assert "padding" in params
+
+        padding = params["padding"]
+        assert len(padding) == 4
+
+        assert 0 <= padding[0] <= (side_range[1] - 1) * width
+        assert 0 <= padding[1] <= (side_range[1] - 1) * height
+        assert 0 <= padding[2] <= (side_range[1] - 1) * width
+        assert 0 <= padding[3] <= (side_range[1] - 1) * height

torchvision/transforms/v2/_geometry.py

@@ -546,7 +546,7 @@ def __init__(
 
         self.side_range = side_range
         if side_range[0] < 1.0 or side_range[0] > side_range[1]:
-            raise ValueError(f"Invalid canvas side range provided {side_range}.")
+            raise ValueError(f"Invalid side range provided {side_range}.")
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
         orig_h, orig_w = query_size(flat_inputs)