Lint

Author: scotts

src/torchcodec/decoders/_video_decoder.py

@@ -103,7 +103,7 @@ def __init__(
         dimension_order: Literal["NCHW", "NHWC"] = "NCHW",
         num_ffmpeg_threads: int = 1,
         device: Optional[Union[str, torch_device]] = "cpu",
-        transforms: List[Any] = [], # TRANSFORMS TODO: what is the user-facing type?
+        transforms: List[Any] = [],  # TRANSFORMS TODO: what is the user-facing type?
         seek_mode: Literal["exact", "approximate"] = "exact",
         custom_frame_mappings: Optional[
             Union[str, bytes, io.RawIOBase, io.BufferedReader]
@@ -435,6 +435,7 @@ def _get_and_validate_stream_metadata(
         num_frames,
     )
 
+
 def make_transform_specs(transforms: List[Any]) -> str:
     from torchvision.transforms import v2
 
@@ -447,11 +448,10 @@ def make_transform_specs(transforms: List[Any]) -> str:
                 )
             transform_specs.append(f"resize, {transform.size[0]}, {transform.size[1]}")
         else:
-            raise ValueError(
-                f"Unsupported transform {transform}."
-            )
+            raise ValueError(f"Unsupported transform {transform}.")
     return ";".join(transform_specs)
 
+
 def _read_custom_frame_mappings(
     custom_frame_mappings: Union[str, bytes, io.RawIOBase, io.BufferedReader]
 ) -> tuple[Tensor, Tensor, Tensor]:

test/generate_reference_resources.py

@@ -133,7 +133,6 @@ def generate_nasa_13013_references():
         )
 
 
-
 def generate_h265_video_references():
     # This video was generated by running the following:
     # conda install -c conda-forge x265

test/test_transform_ops.py

@@ -40,7 +40,9 @@ def test_resize_torchvision(self, height_scaling_factor, width_scaling_factor):
         height = int(NASA_VIDEO.get_height() * height_scaling_factor)
         width = int(NASA_VIDEO.get_width() * width_scaling_factor)
 
-        decoder_resize = VideoDecoder(NASA_VIDEO.path, transforms=[v2.Resize(size=(height, width))])
+        decoder_resize = VideoDecoder(
+            NASA_VIDEO.path, transforms=[v2.Resize(size=(height, width))]
+        )
         decoder_full = VideoDecoder(NASA_VIDEO.path)
         for frame_index in [0, 10, 17, 100, 230, 389]:
             expected_shape = (NASA_VIDEO.get_num_color_channels(), height, width)
@@ -60,16 +62,19 @@ def test_resize_ffmpeg(self):
         width = 240
         expected_shape = (NASA_VIDEO.get_num_color_channels(), height, width)
         resize_filtergraph = f"scale={width}:{height}:flags=bilinear"
-        decoder_resize = VideoDecoder(NASA_VIDEO.path, transforms=[v2.Resize(size=(height, width))])
+        decoder_resize = VideoDecoder(
+            NASA_VIDEO.path, transforms=[v2.Resize(size=(height, width))]
+        )
         for frame_index in [17, 230, 389]:
             frame_resize = decoder_resize[frame_index]
-            frame_ref = NASA_VIDEO.get_frame_data_by_index(frame_index, filters=resize_filtergraph)
+            frame_ref = NASA_VIDEO.get_frame_data_by_index(
+                frame_index, filters=resize_filtergraph
+            )
 
             assert frame_resize.shape == expected_shape
             assert frame_ref.shape == expected_shape
             assert_frames_equal(frame_resize, frame_ref)
 
-
     def test_resize_fails(self):
         with pytest.raises(
             ValueError,
@@ -84,6 +89,7 @@ def test_transform_fails(self):
         ):
             VideoDecoder(NASA_VIDEO.path, transforms=[v2.RandomHorizontalFlip(p=1.0)])
 
+
 class TestCoreVideoDecoderTransformOps:
     # We choose arbitrary values for width and height scaling to get better
     # test coverage. Some pairs upscale the image while others downscale it.