port tests for type conversion transforms

test/test_transforms_v2.py

@@ -122,61 +122,6 @@ def test_check_transformed_types(self, inpt_type, mocker):
                 t(inpt)
 
 
-class TestToImage:
-    @pytest.mark.parametrize(
-        "inpt_type",
-        [torch.Tensor, PIL.Image.Image, tv_tensors.Image, np.ndarray, tv_tensors.BoundingBoxes, str, int],
-    )
-    def test__transform(self, inpt_type, mocker):
-        fn = mocker.patch(
-            "torchvision.transforms.v2.functional.to_image",
-            return_value=torch.rand(1, 3, 8, 8),
-        )
-
-        inpt = mocker.MagicMock(spec=inpt_type)
-        transform = transforms.ToImage()
-        transform(inpt)
-        if inpt_type in (tv_tensors.BoundingBoxes, tv_tensors.Image, str, int):
-            assert fn.call_count == 0
-        else:
-            fn.assert_called_once_with(inpt)
-
-
-class TestToPILImage:
-    @pytest.mark.parametrize(
-        "inpt_type",
-        [torch.Tensor, PIL.Image.Image, tv_tensors.Image, np.ndarray, tv_tensors.BoundingBoxes, str, int],
-    )
-    def test__transform(self, inpt_type, mocker):
-        fn = mocker.patch("torchvision.transforms.v2.functional.to_pil_image")
-
-        inpt = mocker.MagicMock(spec=inpt_type)
-        transform = transforms.ToPILImage()
-        transform(inpt)
-        if inpt_type in (PIL.Image.Image, tv_tensors.BoundingBoxes, str, int):
-            assert fn.call_count == 0
-        else:
-            fn.assert_called_once_with(inpt, mode=transform.mode)
-
-
-class TestToTensor:
-    @pytest.mark.parametrize(
-        "inpt_type",
-        [torch.Tensor, PIL.Image.Image, tv_tensors.Image, np.ndarray, tv_tensors.BoundingBoxes, str, int],
-    )
-    def test__transform(self, inpt_type, mocker):
-        fn = mocker.patch("torchvision.transforms.functional.to_tensor")
-
-        inpt = mocker.MagicMock(spec=inpt_type)
-        with pytest.warns(UserWarning, match="deprecated and will be removed"):
-            transform = transforms.ToTensor()
-        transform(inpt)
-        if inpt_type in (tv_tensors.Image, torch.Tensor, tv_tensors.BoundingBoxes, str, int):
-            assert fn.call_count == 0
-        else:
-            fn.assert_called_once_with(inpt)
-
-
 class TestContainers:
     @pytest.mark.parametrize("transform_cls", [transforms.Compose, transforms.RandomChoice, transforms.RandomOrder])
     def test_assertions(self, transform_cls):

test/test_transforms_v2_consistency.py

@@ -72,21 +72,6 @@ def __init__(
 LINEAR_TRANSFORMATION_MATRIX = torch.rand([LINEAR_TRANSFORMATION_MEAN.numel()] * 2)
 
 CONSISTENCY_CONFIGS = [
-    ConsistencyConfig(
-        v2_transforms.ToPILImage,
-        legacy_transforms.ToPILImage,
-        [NotScriptableArgsKwargs()],
-        make_images_kwargs=dict(
-            color_spaces=[
-                "GRAY",
-                "GRAY_ALPHA",
-                "RGB",
-                "RGBA",
-            ],
-            extra_dims=[()],
-        ),
-        supports_pil=False,
-    ),
     ConsistencyConfig(
         v2_transforms.Lambda,
         legacy_transforms.Lambda,
@@ -97,14 +82,6 @@ def __init__(
         # images given that the transform does nothing but call it anyway.
         supports_pil=False,
     ),
-    ConsistencyConfig(
-        v2_transforms.PILToTensor,
-        legacy_transforms.PILToTensor,
-    ),
-    ConsistencyConfig(
-        v2_transforms.ToTensor,
-        legacy_transforms.ToTensor,
-    ),
     ConsistencyConfig(
         v2_transforms.Compose,
         legacy_transforms.Compose,

test/test_transforms_v2_refactored.py

@@ -5046,3 +5046,67 @@ def test_transform_error_cuda(self):
                 ValueError, match="Input tensor should be on the same device as transformation matrix and mean vector"
             ):
                 transform(input)
+
+
+def make_image_numpy(*args, **kwargs):
+    image = make_image_tensor(*args, **kwargs)
+    return image.permute((1, 2, 0)).numpy()
+
+
+class TestToImage:
+    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image_pil, make_image, make_image_numpy])
+    @pytest.mark.parametrize("fn", [F.to_image, transform_cls_to_functional(transforms.ToImage)])
+    def test_functional_and_transform(self, make_input, fn):
+        input = make_input()
+        output = fn(input)
+
+        assert isinstance(output, tv_tensors.Image)
+
+        input_size = list(input.shape[:2]) if isinstance(input, np.ndarray) else F.get_size(input)
+        assert F.get_size(output) == input_size
+
+        if isinstance(input, torch.Tensor):
+            assert output.data_ptr() == input.data_ptr()
+
+    def test_functional_error(self):
+        with pytest.raises(TypeError, match="Input can either be a numpy array or a PIL image"):
+            F.to_image(object())
+
+
+class TestToPILImage:
+    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image, make_image_numpy])
+    @pytest.mark.parametrize("color_space", ["RGB", "GRAY"])
+    @pytest.mark.parametrize("fn", [F.to_pil_image, transform_cls_to_functional(transforms.ToPILImage)])
+    def test_functional_and_transform(self, make_input, color_space, fn):
+        input = make_input(color_space=color_space)
+        output = fn(input)
+
+        assert isinstance(output, PIL.Image.Image)
+
+        input_size = list(input.shape[:2]) if isinstance(input, np.ndarray) else F.get_size(input)
+        assert F.get_size(output) == input_size
+
+    def test_functional_error(self):
+        with pytest.raises(TypeError, match="pic should be Tensor or ndarray"):
+            F.to_pil_image(object())
+
+        for ndim in [1, 4]:
+            with pytest.raises(ValueError, match="pic should be 2/3 dimensional"):
+                F.to_pil_image(torch.empty(*[1] * ndim))
+
+        with pytest.raises(ValueError, match="pic should not have > 4 channels"):
+            num_channels = 5
+            F.to_pil_image(torch.empty(num_channels, 1, 1))
+
+
+class TestToTensor:
+    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image_pil, make_image, make_image_numpy])
+    def test_smoke(self, make_input):
+        with pytest.warns(UserWarning, match="deprecated and will be removed"):
+            transform = transforms.ToTensor()
+
+        input = make_input()
+        output = transform(input)
+
+        input_size = list(input.shape[:2]) if isinstance(input, np.ndarray) else F.get_size(input)
+        assert F.get_size(output) == input_size