Expand prototype functional kernel scriptability tests (#5496)

* expand prototype functional scriptability tests

* remove obsolete skips

test/test_prototype_transforms_functional.py

@@ -199,20 +199,30 @@ def resize_bounding_box():
             yield SampleInput(bounding_box, size=size, image_size=bounding_box.image_size)
 
 
-class TestKernelsCommon:
-    @pytest.mark.parametrize("functional_info", FUNCTIONAL_INFOS, ids=lambda functional_info: functional_info.name)
-    def test_scriptable(self, functional_info):
-        jit.script(functional_info.functional)
+@pytest.mark.parametrize(
+    "kernel",
+    [
+        pytest.param(kernel, id=name)
+        for name, kernel in F.__dict__.items()
+        if not name.startswith("_")
+        and callable(kernel)
+        and any(feature_type in name for feature_type in {"image", "segmentation_mask", "bounding_box", "label"})
+        and "pil" not in name
+    ],
+)
+def test_scriptable(kernel):
+    jit.script(kernel)
 
-    @pytest.mark.parametrize(
+
+@pytest.mark.parametrize(
     ("functional_info", "sample_input"),
     [
         pytest.param(functional_info, sample_input, id=f"{functional_info.name}-{idx}")
         for functional_info in FUNCTIONAL_INFOS
         for idx, sample_input in enumerate(functional_info.sample_inputs())
     ],
-    )
-    def test_eager_vs_scripted(self, functional_info, sample_input):
+)
+def test_eager_vs_scripted(functional_info, sample_input):
     eager = functional_info(sample_input)
     scripted = jit.script(functional_info.functional)(*sample_input.args, **sample_input.kwargs)
 

torchvision/prototype/transforms/functional/_type_conversion.py

 import unittest.mock
-from typing import Dict, Any, Tuple, cast
+from typing import Dict, Any, Tuple
 
 import numpy as np
 import PIL.Image
@@ -22,4 +22,4 @@ def decode_video_with_av(encoded_video: torch.Tensor) -> Tuple[torch.Tensor, tor
 
 
 def label_to_one_hot(label: torch.Tensor, *, num_categories: int) -> torch.Tensor:
-    return cast(torch.Tensor, one_hot(label, num_classes=num_categories))
+    return one_hot(label, num_classes=num_categories)  # type: ignore[no-any-return]