Undo more tutorial changes

Author: scotts

examples/decoding/approximate_mode.py

@@ -52,7 +52,7 @@
     "-stream_loop", "99",  # repeat video 100 times
     "-i", f"{short_video_path}",
     "-c", "copy",
-    f"{long_video_path}",
+    f"{long_video_path}"
 ]
 subprocess.run(ffmpeg_command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
 

examples/decoding/basic_example.py

@@ -13,7 +13,6 @@
 :class:`~torchcodec.decoders.VideoDecoder` class.
 """
 
-
 # %%
 # First, a bit of boilerplate: we'll download a video from the web, and define a
 # plotting utility. You can ignore that part and jump right below to
@@ -42,7 +41,7 @@ def plot(frames: torch.Tensor, title: str | None = None):
         print("Cannot plot, please run `pip install torchvision matplotlib`")
         return
 
-    plt.rcParams["savefig.bbox"] = "tight"
+    plt.rcParams['savefig.bbox'] = 'tight'
     fig, ax = plt.subplots()
     ax.imshow(to_pil_image(make_grid(frames)))
     ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
@@ -76,7 +75,7 @@ def plot(frames: torch.Tensor, title: str | None = None):
 # ---------------------------------------
 
 first_frame = decoder[0]  # using a single int index
-every_twenty_frame = decoder[0:-1:20]  # using slices
+every_twenty_frame = decoder[0 : -1 : 20]  # using slices
 
 print(f"{first_frame.shape = }")
 print(f"{first_frame.dtype = }")
@@ -116,7 +115,10 @@ def plot(frames: torch.Tensor, title: str | None = None):
 # The decoder is a normal iterable object and can be iterated over like so:
 
 for frame in decoder:
-    assert isinstance(frame, torch.Tensor) and frame.shape == (3, decoder.metadata.height, decoder.metadata.width)
+    assert (
+        isinstance(frame, torch.Tensor)
+        and frame.shape == (3, decoder.metadata.height, decoder.metadata.width)
+    )
 
 # %%
 # Retrieving pts and duration of frames

examples/decoding/custom_frame_mappings.py

@@ -51,7 +51,7 @@
     "-stream_loop", "50",  # repeat video 50 times to get a ~12 min video
     "-i", f"{short_video_path}",
     "-c", "copy",
-    f"{long_video_path}",
+    f"{long_video_path}"
 ]
 subprocess.run(ffmpeg_command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
 

examples/decoding/parallel_decoding.py

@@ -24,7 +24,6 @@
 pool manager.
 """
 
-
 # %%
 # Let's first define some utility functions for benchmarking and data
 # processing.  We'll also download a video and create a longer version by
@@ -80,10 +79,10 @@ def split_indices(indices: list[int], num_chunks: int) -> list[list[int]]:
     chunks = []
 
     for i in range(num_chunks - 1):
-        chunks.append(indices[i * chunk_size : (i + 1) * chunk_size])
+        chunks.append(indices[i * chunk_size:(i + 1) * chunk_size])
 
     # Last chunk may be slightly larger
-    chunks.append(indices[(num_chunks - 1) * chunk_size :])
+    chunks.append(indices[(num_chunks - 1) * chunk_size:])
     return chunks
 
 
@@ -107,7 +106,7 @@ def generate_long_video(temp_dir: str):
         "-stream_loop", "49",  # repeat video 50 times
         "-i", str(short_video_path),
         "-c", "copy",
-        str(long_video_path),
+        str(long_video_path)
     ]
     subprocess.run(ffmpeg_command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
 
@@ -178,7 +177,7 @@ def decode_sequentially(indices: list[int], video_path=long_video_path):
 def decode_with_ffmpeg_parallelism(
     indices: list[int],
     num_threads: int,
-    video_path=long_video_path,
+    video_path=long_video_path
 ):
     """Decode frames using FFmpeg's internal threading."""
     decoder = VideoDecoder(video_path, num_ffmpeg_threads=num_threads, seek_mode="approximate")

examples/decoding/sampling.py

@@ -14,7 +14,6 @@
 input to video models.
 """
 
-
 # %%
 # First, a bit of boilerplate: we'll download a video from the web, and define a
 # plotting utility. You can ignore that part and jump right below to
@@ -43,7 +42,7 @@ def plot(frames: torch.Tensor, title: str | None = None):
         print("Cannot plot, please run `pip install torchvision matplotlib`")
         return
 
-    plt.rcParams["savefig.bbox"] = "tight"
+    plt.rcParams['savefig.bbox'] = 'tight'
     fig, ax = plt.subplots()
     ax.imshow(to_pil_image(make_grid(frames)))
     ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
@@ -191,7 +190,7 @@ def plot(frames: torch.Tensor, title: str | None = None):
     num_frames_per_clip=4,
     seconds_between_frames=0.5,
     sampling_range_start=2,
-    sampling_range_end=5,
+    sampling_range_end=5
 )
 clips
 

examples/encoding/video_encoding.py

@@ -178,7 +178,9 @@ def play_video(encoded_bytes):
 #     to check available options for your selected codec.
 
 # Standard pixel format
-yuv420_encoded_frames = encoder.to_tensor(format="mp4", codec="libx264", pixel_format="yuv420p")
+yuv420_encoded_frames = encoder.to_tensor(
+    format="mp4", codec="libx264", pixel_format="yuv420p"
+)
 play_video(yuv420_encoded_frames)
 
 # %%
@@ -271,8 +273,8 @@ def play_video(encoded_bytes):
     custom_output,
     codec="libx264",
     extra_options={
-        "g": 50,  # Keyframe every 50 frames
-        "max_b_frames": 0,  # Disable B-frames for faster decoding
+        "g": 50,               # Keyframe every 50 frames
+        "max_b_frames": 0,     # Disable B-frames for faster decoding
         "tune": "fastdecode",  # Optimize for fast decoding
     },
 )