enable get_params alias for transforms v2 (#7153)

test/test_prototype_transforms_consistency.py

@@ -655,6 +655,39 @@ def test_call_consistency(config, args_kwargs):
     )
 
 
+@pytest.mark.parametrize(
+    "config",
+    [config for config in CONSISTENCY_CONFIGS if hasattr(config.legacy_cls, "get_params")],
+    ids=lambda config: config.legacy_cls.__name__,
+)
+def test_get_params_alias(config):
+    assert config.prototype_cls.get_params is config.legacy_cls.get_params
+
+
+@pytest.mark.parametrize(
+    ("transform_cls", "args_kwargs"),
+    [
+        (prototype_transforms.RandomResizedCrop, ArgsKwargs(make_image(), scale=[0.3, 0.7], ratio=[0.5, 1.5])),
+        (prototype_transforms.RandomErasing, ArgsKwargs(make_image(), scale=(0.3, 0.7), ratio=(0.5, 1.5))),
+        (prototype_transforms.ColorJitter, ArgsKwargs(brightness=None, contrast=None, saturation=None, hue=None)),
+        (prototype_transforms.ElasticTransform, ArgsKwargs(alpha=[15.3, 27.2], sigma=[2.5, 3.9], size=[17, 31])),
+        (prototype_transforms.GaussianBlur, ArgsKwargs(0.3, 1.4)),
+        (
+            prototype_transforms.RandomAffine,
+            ArgsKwargs(degrees=[-20.0, 10.0], translate=None, scale_ranges=None, shears=None, img_size=[15, 29]),
+        ),
+        (prototype_transforms.RandomCrop, ArgsKwargs(make_image(size=(61, 47)), output_size=(19, 25))),
+        (prototype_transforms.RandomPerspective, ArgsKwargs(23, 17, 0.5)),
+        (prototype_transforms.RandomRotation, ArgsKwargs(degrees=[-20.0, 10.0])),
+        (prototype_transforms.AutoAugment, ArgsKwargs(5)),
+    ],
+)
+def test_get_params_jit(transform_cls, args_kwargs):
+    args, kwargs = args_kwargs
+
+    torch.jit.script(transform_cls.get_params)(*args, **kwargs)
+
+
 @pytest.mark.parametrize(
     ("config", "args_kwargs"),
     [

torchvision/prototype/transforms/_transform.py

@@ -56,10 +56,19 @@ class Transform(nn.Module):
 
         return ", ".join(extra)
 
-    # This attribute should be set on all transforms that have a v1 equivalent. Doing so enables the v2 transformation
-    # to be scriptable. See `_extract_params_for_v1_transform()` and `__prepare_scriptable__` for details.
+    # This attribute should be set on all transforms that have a v1 equivalent. Doing so enables two things:
+    # 1. In case the v1 transform has a static `get_params` method, it will also be available under the same name on
+    #    the v2 transform. See `__init_subclass__` for details.
+    # 2. The v2 transform will be JIT scriptable. See `_extract_params_for_v1_transform` and `__prepare_scriptable__`
+    #    for details.
     _v1_transform_cls: Optional[Type[nn.Module]] = None
 
+    def __init_subclass__(cls) -> None:
+        # Since `get_params` is a `@staticmethod`, we have to bind it to the class itself rather than to an instance.
+        # This method is called after subclassing has happened, i.e. `cls` is the subclass, e.g. `Resize`.
+        if cls._v1_transform_cls is not None and hasattr(cls._v1_transform_cls, "get_params"):
+            cls.get_params = cls._v1_transform_cls.get_params  # type: ignore[attr-defined]
+
     def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
         # This method is called by `__prepare_scriptable__` to instantiate the equivalent v1 transform from the current
         # v2 transform instance. It does two things: