[Cherry-picks for 0.0.4 release] Various cherry-picks

README.md

@@ -93,6 +93,7 @@ ffmpeg -f lavfi -i \
 ```
 
 ## Installing TorchCodec
+### Installing CPU-only TorchCodec
 
 1. Install the latest stable version of PyTorch following the
    [official instructions](https://pytorch.org/get-started/locally/). For other
@@ -127,9 +128,65 @@ The following table indicates the compatibility between versions of
 | not yet supported  | `2.5`              | `>=3.9`, `<=3.12`   |
 | `0.0.3`            | `2.4`              | `>=3.8`, `<=3.12`   |
 
+### Installing CUDA-enabled TorchCodec
+
+First, make sure you have a GPU that has NVDEC hardware that can decode the
+format you want. Refer to Nvidia's GPU support matrix for more details
+[here](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new).
+
+1. Install CUDA Toolkit. Pytorch and TorchCodec supports CUDA Toolkit
+   versions 11.8, 12.1 or 12.4. In particular TorchCodec depends on
+   CUDA libraries libnpp and libnvrtc (which are part of CUDA Toolkit).
+
+2. Install Pytorch that corresponds to your CUDA Toolkit version using the
+   [official instructions](https://pytorch.org/get-started/locally/).
+
+3. Install or compile FFmpeg with NVDEC support.
+   TorchCodec with CUDA should work with FFmpeg versions in [4, 7].
+
+   If FFmpeg is not already installed, or you need a more recent version, an
+   easy way to install it is to use `conda`:
+
+   ```bash
+   conda install ffmpeg
+   # or
+   conda install ffmpeg -c conda-forge
+   ```
+
+   If you are building FFmpeg from source you can follow Nvidia's guide to
+   configuring and installing FFmpeg with NVDEC support
+   [here](https://docs.nvidia.com/video-technologies/video-codec-sdk/12.0/ffmpeg-with-nvidia-gpu/index.html).
+
+   After installing FFmpeg make sure it has NVDEC support when you list the supported
+   decoders:
+
+   ```bash
+   ffmpeg -decoders | grep -i nvidia
+   # This should show a line like this:
+   # V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
+   ```
+
+   To check that FFmpeg libraries work with NVDEC correctly you can decode a sample video:
+
+   ```bash
+   ffmpeg -hwaccel cuda -hwaccel_output_format cuda -i test/resources/nasa_13013.mp4 -f null -
+   ```
+
+4. Install TorchCodec by passing in an `--index-url` parameter that corresponds to your CUDA
+   Toolkit version, example:
+
+   ```bash
+   # This corresponds to CUDA Toolkit version 12.4 and nightly Pytorch.
+   pip install torchcodec --index-url=https://download.pytorch.org/whl/nightly/cu124
+   ```
+
+   Note that without passing in the `--index-url` parameter, `pip` installs TorchCodec
+   binaries from PyPi which are CPU-only and do not have CUDA support.
+
 ## Benchmark Results
 
-The following was generated by running [our benchmark script](./benchmarks/decoders/generate_readme_data.py) on a lightly loaded 56-core machine.
+The following was generated by running [our benchmark script](./benchmarks/decoders/generate_readme_data.py) on a lightly loaded 22-core machine with an Nvidia A100 with
+5 [NVDEC decoders](https://docs.nvidia.com/video-technologies/video-codec-sdk/12.1/nvdec-application-note/index.html#).
 
 ![benchmark_results](./benchmarks/decoders/benchmark_readme_chart.png)
 
@@ -156,3 +213,10 @@ guide](CONTRIBUTING.md) for more details.
 ## License
 
 TorchCodec is released under the [BSD 3 license](./LICENSE).
+
+However, TorchCodec may be used with code not written by Meta which may be
+distributed under different licenses.
+
+For example, if you build TorchCodec with ENABLE_CUDA=1 or use the CUDA-enabled
+release of torchcodec, please review CUDA's license here:
+[Nvidia licenses](https://docs.nvidia.com/cuda/eula/index.html).

benchmarks/decoders/benchmark_decoders_library.py

@@ -38,6 +38,14 @@ def __init__(self):
     def get_frames_from_video(self, video_file, pts_list):
         pass
 
+    @abc.abstractmethod
+    def get_consecutive_frames_from_video(self, video_file, numFramesToDecode):
+        pass
+
+    @abc.abstractmethod
+    def decode_and_transform(self, video_file, pts_list, height, width, device):
+        pass
+
 
 class DecordAccurate(AbstractDecoder):
     def __init__(self):
@@ -89,8 +97,10 @@ def __init__(self, backend):
         self._backend = backend
         self._print_each_iteration_time = False
         import torchvision  # noqa: F401
+        from torchvision.transforms import v2 as transforms_v2
 
         self.torchvision = torchvision
+        self.transforms_v2 = transforms_v2
 
     def get_frames_from_video(self, video_file, pts_list):
         self.torchvision.set_video_backend(self._backend)
@@ -111,6 +121,20 @@ def get_consecutive_frames_from_video(self, video_file, numFramesToDecode):
             frames.append(frame["data"].permute(1, 2, 0))
         return frames
 
+    def decode_and_transform(self, video_file, pts_list, height, width, device):
+        self.torchvision.set_video_backend(self._backend)
+        reader = self.torchvision.io.VideoReader(video_file, "video")
+        frames = []
+        for pts in pts_list:
+            reader.seek(pts)
+            frame = next(reader)
+            frames.append(frame["data"].permute(1, 2, 0))
+        frames = [
+            self.transforms_v2.functional.resize(frame.to(device), (height, width))
+            for frame in frames
+        ]
+        return frames
+
 
 class TorchCodecCore(AbstractDecoder):
     def __init__(self, num_threads=None, color_conversion_library=None, device="cpu"):
@@ -239,6 +263,10 @@ def __init__(self, num_ffmpeg_threads=None, device="cpu"):
         )
         self._device = device
 
+        from torchvision.transforms import v2 as transforms_v2
+
+        self.transforms_v2 = transforms_v2
+
     def get_frames_from_video(self, video_file, pts_list):
         decoder = VideoDecoder(
             video_file, num_ffmpeg_threads=self._num_ffmpeg_threads, device=self._device
@@ -258,6 +286,14 @@ def get_consecutive_frames_from_video(self, video_file, numFramesToDecode):
                 break
         return frames
 
+    def decode_and_transform(self, video_file, pts_list, height, width, device):
+        decoder = VideoDecoder(
+            video_file, num_ffmpeg_threads=self._num_ffmpeg_threads, device=self._device
+        )
+        frames = decoder.get_frames_played_at(pts_list)
+        frames = self.transforms_v2.functional.resize(frames.data, (height, width))
+        return frames
+
 
 @torch.compile(fullgraph=True, backend="eager")
 def compiled_seek_and_next(decoder, pts):
@@ -299,7 +335,9 @@ def __init__(self):
 
         self.torchaudio = torchaudio
 
-        pass
+        from torchvision.transforms import v2 as transforms_v2
+
+        self.transforms_v2 = transforms_v2
 
     def get_frames_from_video(self, video_file, pts_list):
         stream_reader = self.torchaudio.io.StreamReader(src=video_file)
@@ -325,6 +363,21 @@ def get_consecutive_frames_from_video(self, video_file, numFramesToDecode):
 
         return frames
 
+    def decode_and_transform(self, video_file, pts_list, height, width, device):
+        stream_reader = self.torchaudio.io.StreamReader(src=video_file)
+        stream_reader.add_basic_video_stream(frames_per_chunk=1)
+        frames = []
+        for pts in pts_list:
+            stream_reader.seek(pts)
+            stream_reader.fill_buffer()
+            clip = stream_reader.pop_chunks()
+            frames.append(clip[0][0])
+        frames = [
+            self.transforms_v2.functional.resize(frame.to(device), (height, width))
+            for frame in frames
+        ]
+        return frames
+
 
 def create_torchcodec_decoder_from_file(video_file):
     video_decoder = create_from_file(video_file)
@@ -443,7 +496,7 @@ def plot_data(df_data, plot_path):
 
             # Set the title for the subplot
             base_video = Path(video).name.removesuffix(".mp4")
-            ax.set_title(f"{base_video}\n{vcount} x {vtype}", fontsize=11)
+            ax.set_title(f"{base_video}\n{vtype}", fontsize=11)
 
             # Plot bars with error bars
             ax.barh(
@@ -486,6 +539,14 @@ class BatchParameters:
     batch_size: int
 
 
+@dataclass
+class DataLoaderInspiredWorkloadParameters:
+    batch_parameters: BatchParameters
+    resize_height: int
+    resize_width: int
+    resize_device: str
+
+
 def run_batch_using_threads(
     function,
     *args,
@@ -525,6 +586,7 @@ def run_benchmarks(
     num_sequential_frames_from_start: list[int],
     min_runtime_seconds: float,
     benchmark_video_creation: bool,
+    dataloader_parameters: DataLoaderInspiredWorkloadParameters = None,
     batch_parameters: BatchParameters = None,
 ) -> list[dict[str, str | float | int]]:
     # Ensure that we have the same seed across benchmark runs.
@@ -550,6 +612,39 @@ def run_benchmarks(
         for decoder_name, decoder in decoder_dict.items():
             print(f"video={video_file_path}, decoder={decoder_name}")
 
+            if dataloader_parameters:
+                bp = dataloader_parameters.batch_parameters
+                dataloader_result = benchmark.Timer(
+                    stmt="run_batch_using_threads(decoder.decode_and_transform, video_file, pts_list, height, width, device, batch_parameters=batch_parameters)",
+                    globals={
+                        "video_file": str(video_file_path),
+                        "pts_list": uniform_pts_list,
+                        "decoder": decoder,
+                        "run_batch_using_threads": run_batch_using_threads,
+                        "batch_parameters": dataloader_parameters.batch_parameters,
+                        "height": dataloader_parameters.resize_height,
+                        "width": dataloader_parameters.resize_width,
+                        "device": dataloader_parameters.resize_device,
+                    },
+                    label=f"video={video_file_path} {metadata_label}",
+                    sub_label=decoder_name,
+                    description=f"dataloader[threads={bp.num_threads} batch_size={bp.batch_size}] {num_samples} decode_and_transform()",
+                )
+                results.append(
+                    dataloader_result.blocked_autorange(
+                        min_run_time=min_runtime_seconds
+                    )
+                )
+                df_data.append(
+                    convert_result_to_df_item(
+                        results[-1],
+                        decoder_name,
+                        video_file_path,
+                        num_samples * dataloader_parameters.batch_parameters.batch_size,
+                        f"dataloader[threads={bp.num_threads} batch_size={bp.batch_size}] {num_samples} x decode_and_transform()",
+                    )
+                )
+
             for kind, pts_list in [
                 ("uniform", uniform_pts_list),
                 ("random", random_pts_list),