align transforms v2 signatures with v1 (#7301)

Co-authored-by: vfdev <vfdev.5@gmail.com>

test/test_transforms_v2_consistency.py

@@ -540,9 +540,12 @@ def test_signature_consistency(config):
             f"not. Please add a default value."
         )
 
-    legacy_kinds = {name: param.kind for name, param in legacy_params.items()}
-    prototype_kinds = {name: prototype_params[name].kind for name in legacy_kinds.keys()}
-    assert prototype_kinds == legacy_kinds
+    legacy_signature = list(legacy_params.keys())
+    # Since we made sure that we don't have any extra parameters without default above, we clamp the prototype signature
+    # to the same number of parameters as the legacy one
+    prototype_signature = list(prototype_params.keys())[: len(legacy_signature)]
+
+    assert prototype_signature == legacy_signature
 
 
 def check_call_consistency(

torchvision/transforms/v2/_container.py

@@ -124,8 +124,8 @@ class RandomChoice(Transform):
     def __init__(
         self,
         transforms: Sequence[Callable],
-        probabilities: Optional[List[float]] = None,
         p: Optional[List[float]] = None,
+        probabilities: Optional[List[float]] = None,
     ) -> None:
         if not isinstance(transforms, Sequence):
             raise TypeError("Argument transforms should be a sequence of callables")

torchvision/transforms/v2/_geometry.py

@@ -575,8 +575,8 @@ class RandomRotation(Transform):
         degrees: Union[numbers.Number, Sequence],
         interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
         expand: bool = False,
-        fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
         center: Optional[List[float]] = None,
+        fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
     ) -> None:
         super().__init__()
         self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
@@ -903,9 +903,9 @@ class RandomPerspective(_RandomApplyTransform):
     def __init__(
         self,
         distortion_scale: float = 0.5,
-        fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
-        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
         p: float = 0.5,
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
     ) -> None:
         super().__init__(p=p)
 
@@ -966,8 +966,8 @@ class ElasticTransform(Transform):
         self,
         alpha: Union[float, Sequence[float]] = 50.0,
         sigma: Union[float, Sequence[float]] = 5.0,
-        fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
         interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        fill: Union[datapoints._FillType, Dict[Type, datapoints._FillType]] = 0,
     ) -> None:
         super().__init__()
         self.alpha = _setup_float_or_seq(alpha, "alpha", 2)