Add tests for prototype <-> legacy transforms consistency (#6514)

* add consistency checks for prototype and legacy transforms

* fix Resize

test/test_prototype_transforms_consistency.py

+import itertools
+
+import pytest
+import torch.testing
+from test_prototype_transforms_functional import make_images
+from torchvision import transforms as legacy_transforms
+from torchvision.prototype import features, transforms as prototype_transforms
+from torchvision.prototype.transforms.functional import to_image_pil, to_image_tensor
+
+DEFAULT_MAKE_IMAGES_KWARGS = dict(color_spaces=[features.ColorSpace.RGB], extra_dims=[(4,)])
+
+
+class ArgsKwargs:
+    def __init__(self, *args, **kwargs):
+        self.args = args
+        self.kwargs = kwargs
+
+    def __iter__(self):
+        yield self.args
+        yield self.kwargs
+
+    def __str__(self):
+        return ", ".join(
+            itertools.chain(
+                [repr(arg) for arg in self.args],
+                [f"{param}={repr(kwarg)}" for param, kwarg in self.kwargs.items()],
+            )
+        )
+
+
+class ConsistencyConfig:
+    def __init__(
+        self, prototype_cls, legacy_cls, transform_args_kwargs=None, make_images_kwargs=None, supports_pil=True
+    ):
+        self.prototype_cls = prototype_cls
+        self.legacy_cls = legacy_cls
+        self.transform_args_kwargs = transform_args_kwargs or [((), dict())]
+        self.make_images_kwargs = make_images_kwargs or DEFAULT_MAKE_IMAGES_KWARGS
+        self.supports_pil = supports_pil
+
+    def parametrization(self):
+        return [
+            pytest.param(
+                self.prototype_cls,
+                self.legacy_cls,
+                args_kwargs,
+                self.make_images_kwargs,
+                self.supports_pil,
+                id=f"{self.prototype_cls.__name__}({args_kwargs})",
+            )
+            for args_kwargs in self.transform_args_kwargs
+        ]
+
+
+CONSISTENCY_CONFIGS = [
+    ConsistencyConfig(
+        prototype_transforms.Normalize,
+        legacy_transforms.Normalize,
+        [
+            ArgsKwargs(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
+        ],
+        supports_pil=False,
+        make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, dtypes=[torch.float]),
+    ),
+    ConsistencyConfig(
+        prototype_transforms.Resize,
+        legacy_transforms.Resize,
+        [
+            ArgsKwargs(32),
+            ArgsKwargs((32, 29)),
+            ArgsKwargs((31, 28), interpolation=prototype_transforms.InterpolationMode.BICUBIC),
+        ],
+    ),
+    ConsistencyConfig(
+        prototype_transforms.CenterCrop,
+        legacy_transforms.CenterCrop,
+        [
+            ArgsKwargs(18),
+            ArgsKwargs((18, 13)),
+        ],
+    ),
+]
+
+
+@pytest.mark.parametrize(
+    ("prototype_transform_cls", "legacy_transform_cls", "args_kwargs", "make_images_kwargs", "supports_pil"),
+    itertools.chain.from_iterable(config.parametrization() for config in CONSISTENCY_CONFIGS),
+)
+def test_consistency(prototype_transform_cls, legacy_transform_cls, args_kwargs, make_images_kwargs, supports_pil):
+    args, kwargs = args_kwargs
+
+    try:
+        legacy = legacy_transform_cls(*args, **kwargs)
+    except Exception as exc:
+        raise pytest.UsageError(
+            f"Initializing the legacy transform failed with the error above. "
+            f"Please correct the `ArgsKwargs({args_kwargs})` in the `ConsistencyConfig`."
+        ) from exc
+
+    try:
+        prototype = prototype_transform_cls(*args, **kwargs)
+    except Exception as exc:
+        raise AssertionError(
+            "Initializing the prototype transform failed with the error above. "
+            "This means there is a consistency bug in the constructor."
+        ) from exc
+
+    for image in make_images(**make_images_kwargs):
+        image_tensor = torch.Tensor(image)
+        image_pil = to_image_pil(image) if image.ndim == 3 and supports_pil else None
+
+        try:
+            output_legacy_tensor = legacy(image_tensor)
+        except Exception as exc:
+            raise pytest.UsageError(
+                f"Transforming a tensor image with shape {tuple(image.shape)} failed with the error above. "
+                "This means that you need to specify the parameters passed to `make_images` through the "
+                "`make_images_kwargs` of the `ConsistencyConfig`."
+            ) from exc
+
+        try:
+            output_prototype_tensor = prototype(image_tensor)
+        except Exception as exc:
+            raise AssertionError(
+                f"Transforming a tensor image with shape {tuple(image.shape)} failed with the error above. "
+                f"This means there is a consistency bug either in `_get_params` "
+                f"or in the `is_simple_tensor` path in `_transform`."
+            ) from exc
+
+        torch.testing.assert_close(
+            output_prototype_tensor,
+            output_legacy_tensor,
+            atol=0,
+            rtol=0,
+            msg=lambda msg: f"Tensor image consistency check failed with: \n\n{msg}",
+        )
+
+        try:
+            output_prototype_image = prototype(image)
+        except Exception as exc:
+            raise AssertionError(
+                f"Transforming a feature image with shape {tuple(image.shape)} failed with the error above. "
+                f"This means there is a consistency bug either in `_get_params` "
+                f"or in the `features.Image` path in `_transform`."
+            ) from exc
+
+        torch.testing.assert_close(
+            torch.Tensor(output_prototype_image),
+            output_prototype_tensor,
+            atol=0,
+            rtol=0,
+            msg=lambda msg: f"Output for feature and tensor images is not equal: \n\n{msg}",
+        )
+
+        if image_pil is not None:
+            torch.testing.assert_close(
+                to_image_tensor(prototype(image_pil)),
+                to_image_tensor(legacy(image_pil)),
+                atol=0,
+                rtol=0,
+                msg=lambda msg: f"PIL image consistency check failed with: \n\n{msg}",
+            )

torchvision/prototype/transforms/_geometry.py

@@ -44,7 +44,11 @@ class Resize(Transform):
     ) -> None:
         super().__init__()
 
-        self.size = _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
+        self.size = (
+            [size]
+            if isinstance(size, int)
+            else _setup_size(size, error_msg="Please provide only two dimensions (h, w) for size.")
+        )
         self.interpolation = interpolation
         self.max_size = max_size
         self.antialias = antialias