Make RandomApply torchscriptable in V2

test/test_prototype_transforms_consistency.py

@@ -806,6 +806,11 @@ def test_random_apply(self, p, sequence_type):
 
         check_call_consistency(prototype_transform, legacy_transform)
 
+        if sequence_type is nn.ModuleList:
+            # quick and dirty test that it is jit-scriptable
+            scripted = torch.jit.script(prototype_transform)
+            scripted(torch.rand(1, 3, 300, 300))
+
     # We can't test other values for `p` since the random parameter generation is different
     @pytest.mark.parametrize("probabilities", [(0, 1), (1, 0)])
     def test_random_choice(self, probabilities):

torchvision/prototype/transforms/_container.py

@@ -1,9 +1,10 @@
 import warnings
-from typing import Any, Callable, List, Optional, Sequence, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, Union
 
 import torch
 
 from torch import nn
+from torchvision import transforms as _transforms
 from torchvision.prototype.transforms import Transform
 
 
@@ -28,6 +29,8 @@ def extra_repr(self) -> str:
 
 
 class RandomApply(Transform):
+    _v1_transform_cls = _transforms.RandomApply
+
     def __init__(self, transforms: Union[Sequence[Callable], nn.ModuleList], p: float = 0.5) -> None:
         super().__init__()
 
@@ -39,6 +42,9 @@ def __init__(self, transforms: Union[Sequence[Callable], nn.ModuleList], p: floa
             raise ValueError("`p` should be a floating point value in the interval [0.0, 1.0].")
         self.p = p
 
+    def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
+        return {"transforms": self.transforms, "p": self.p}
+
     def forward(self, *inputs: Any) -> Any:
         sample = inputs if len(inputs) > 1 else inputs[0]
 

torchvision/prototype/transforms/_transform.py

@@ -141,8 +141,9 @@ def __prepare_scriptable__(self) -> nn.Module:
         if self._v1_transform_cls is None:
             raise RuntimeError(
                 f"Transform {type(self).__name__} cannot be JIT scripted. "
-                f"This is only support for backward compatibility with transforms which already in v1."
-                f"For torchscript support (on tensors only), you can use the functional API instead."
+                "torchscript is only supported for backward compatibility with transforms "
+                "which are already in torchvision.transforms. "
+                "For torchscript support (on tensors only), you can use the functional API instead."
             )
 
         return self._v1_transform_cls(**self._extract_params_for_v1_transform())