make transforms v2 get_params a staticmethod (#7177)

Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

test/test_prototype_transforms_consistency.py

@@ -649,18 +649,15 @@ 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"),
+get_params_parametrization = pytest.mark.parametrize(
+    ("config", "get_params_args_kwargs"),
     [
+        pytest.param(
+            next(config for config in CONSISTENCY_CONFIGS if config.prototype_cls is transform_cls),
+            get_params_args_kwargs,
+            id=transform_cls.__name__,
+        )
+        for transform_cls, get_params_args_kwargs in [
             (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)),
@@ -674,12 +671,36 @@ def test_get_params_alias(config):
             (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)
+
+@get_paramsl_parametrization
+def test_get_params_alias(config, get_params_args_kwargs):
+    assert config.prototype_cls.get_params is config.legacy_cls.get_params
+
+    if not config.args_kwargs:
+        return
+    args, kwargs = config.args_kwargs[0]
+    legacy_transform = config.legacy_cls(*args, **kwargs)
+    prototype_transform = config.prototype_cls(*args, **kwargs)
+
+    assert prototype_transform.get_params is legacy_transform.get_params
+
+
+@get_paramsl_parametrization
+def test_get_params_jit(config, get_params_args_kwargs):
+    get_params_args, get_params_kwargs = get_params_args_kwargs
+
+    torch.jit.script(config.prototype_cls.get_params)(*get_params_args, **get_params_kwargs)
+
+    if not config.args_kwargs:
+        return
+    args, kwargs = config.args_kwargs[0]
+    transform = config.prototype_cls(*args, **kwargs)
+
+    torch.jit.script(transform.get_params)(*get_params_args, **get_params_kwargs)
 
 
 @pytest.mark.parametrize(

torchvision/prototype/transforms/_transform.py

@@ -67,7 +67,7 @@ class Transform(nn.Module):
         # 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]
+            cls.get_params = staticmethod(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