fix prototype transforms for non features (#4942)

Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

test/test_prototype_transforms.py

@@ -38,7 +38,7 @@ def test_feature_type_support():
     [transform_type for transform_type in TRANSFORM_TYPES if transform_type is not transforms.Identity],
     ids=lambda transform_type: transform_type.__name__,
 )
-def test_no_op(transform_type):
+def test_feature_no_op_coverage(transform_type):
     unsupported_features = (
         FEATURE_TYPES - transform_type.supported_feature_types() - set(transform_type.NO_OP_FEATURE_TYPES)
     )
@@ -49,3 +49,13 @@ def test_no_op(transform_type):
             f"no-op for transform `{transform_type.__name__}`. Please either implement a feature transform for them, "
             f"or add them to the the `{transform_type.__name__}.NO_OP_FEATURE_TYPES` collection."
         )
+
+
+def test_non_feature_no_op():
+    class TestTransform(transforms.Transform):
+        @staticmethod
+        def image(input):
+            return input
+
+    no_op_sample = dict(int=0, float=0.0, bool=False, str="str")
+    assert TestTransform()(no_op_sample) == no_op_sample

torchvision/prototype/transforms/_transform.py

@@ -351,14 +351,16 @@ class Transform(nn.Module):
             sample: Sample.
             params: Parameter dictionary ``params`` that will be passed to ``feature_transform(input, **params)``.
         """
-        if isinstance(sample, collections.abc.Sequence):
+        # We explicitly exclude str's here since they are self-referential and would cause an infinite recursion loop:
+        # "a" == "a"[0][0]...
+        if isinstance(sample, collections.abc.Sequence) and not isinstance(sample, str):
             return [self._transform_recursively(item, params=params) for item in sample]
         elif isinstance(sample, collections.abc.Mapping):
             return {name: self._transform_recursively(item, params=params) for name, item in sample.items()}
         else:
             feature_type = type(sample)
             if not self.supports(feature_type):
-                if feature_type in self.NO_OP_FEATURE_TYPES:
+                if not issubclass(feature_type, features.Feature) or feature_type in self.NO_OP_FEATURE_TYPES:
                     return sample
 
                 raise TypeError(
@@ -366,7 +368,7 @@ class Transform(nn.Module):
                     f"If you want it to be a no-op, add the feature type to {type(self).__name__}.NO_OP_FEATURE_TYPES."
                 )
 
-            return self.transform(sample, **params)
+            return self.transform(cast(Union[torch.Tensor, features.Feature], sample), **params)
 
     def get_params(self, sample: Any) -> Dict[str, Any]:
         """Returns the parameter dictionary used to transform the current sample.