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| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
| from pathlib import Path | ||
| from time import perf_counter_ns | ||
|
|
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| import abc | ||
| import argparse | ||
| import importlib | ||
| import os | ||
|
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| import decord | ||
| import numpy as np | ||
| import torch | ||
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| import torch.utils.benchmark as benchmark | ||
| from torchcodec.samplers import ( | ||
| IndexBasedSamplerArgs, | ||
| TimeBasedSamplerArgs, | ||
| VideoArgs, | ||
| VideoClipSampler, | ||
| ) | ||
| from torchmultimodal.fb.utils.video_utils import ( | ||
| ClipSamplerType, | ||
| VideoClipSampler as tmm_vcs, | ||
| ) | ||
| from torchvision.datasets.video_clip_sampler import ( # @manual=//pytorch/vision:internal_datasets | ||
| TVVideoClipDecoder, | ||
| UniformClipSamplingStrategy, | ||
| VideoClipSampler as ta_vcs, | ||
| ) | ||
|
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||
|
|
||
| class AbstractSampler: | ||
| def __init__(self): | ||
| pass | ||
|
|
||
| @abc.abstractmethod | ||
| def sample_frames_uniformly(self, video_file, clips_per_video): | ||
| pass | ||
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|
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| class TorchCodecTimeBasedSampler(AbstractSampler): | ||
| def __init__(self): | ||
| pass | ||
|
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| def sample_frames_uniformly(self, video_file, clips_per_video): | ||
| arr = np.fromfile(video_file, dtype=np.uint8) | ||
| video_tensor = torch.from_numpy(arr) | ||
| video_input = VideoArgs() | ||
| sampler_input = TimeBasedSamplerArgs( | ||
| sampler_type="uniform", clips_per_video=clips_per_video, frames_per_clip=1 | ||
| ) | ||
| sampler = VideoClipSampler(video_input, sampler_input) | ||
| return sampler(video_tensor) | ||
|
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|
|
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| class TorchCodecIndexBasedSampler(AbstractSampler): | ||
| def __init__(self): | ||
| pass | ||
|
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| def sample_frames_uniformly(self, video_file, clips_per_video): | ||
| arr = np.fromfile(video_file, dtype=np.uint8) | ||
| video_tensor = torch.from_numpy(arr) | ||
| video_input = VideoArgs() | ||
| sampler_input = IndexBasedSamplerArgs( | ||
| sampler_type="uniform", clips_per_video=clips_per_video, frames_per_clip=1 | ||
| ) | ||
| sampler = VideoClipSampler(video_input, sampler_input) | ||
| return sampler(video_tensor) | ||
|
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|
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| class TorchCodecIndexBasedSamplerWithStackedOutput(AbstractSampler): | ||
| """ | ||
| On large batch, torch stack has impact on performance, but it's not obvious locally. | ||
| """ | ||
|
|
||
| def __init__(self): | ||
| pass | ||
|
|
||
| def sample_frames_uniformly(self, video_file, clips_per_video): | ||
| arr = np.fromfile(video_file, dtype=np.uint8) | ||
| video_tensor = torch.from_numpy(arr) | ||
| video_input = VideoArgs() | ||
| sampler_input = IndexBasedSamplerArgs( | ||
| sampler_type="uniform", clips_per_video=clips_per_video, frames_per_clip=1 | ||
| ) | ||
| sampler = VideoClipSampler(video_input, sampler_input) | ||
| clips = sampler(video_tensor) | ||
| return torch.stack([clip[0] for clip in clips]) | ||
|
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|
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| class DecordSampler(AbstractSampler): | ||
| def __init__(self): | ||
| pass | ||
|
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| def sample_frames_uniformly(self, video_file, clips_per_video): | ||
| decord.bridge.set_bridge("torch") | ||
| av_reader = decord.VideoReader(video_file) | ||
| num_frames = len(av_reader) | ||
| frame_indices = np.linspace(0, num_frames - 1, clips_per_video, dtype=int) | ||
| frames = av_reader.get_batch(frame_indices) | ||
| return frames | ||
|
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|
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| class TorchMMSamplerWithTorchVisionBackend(AbstractSampler): | ||
| """ | ||
| Here we use TorchMultimodal sampler as it's updated version on top of torchvision decoder. | ||
| """ | ||
|
|
||
| def __init__(self): | ||
| pass | ||
|
|
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| def sample_frames_uniformly(self, video_file, clips_per_video): | ||
| arr = np.fromfile(video_file, dtype=np.uint8) | ||
| video_tensor = torch.from_numpy(arr) | ||
| sampler = tmm_vcs( | ||
| clip_sampler_type=ClipSamplerType("UNIFORM"), | ||
| clips_per_video=clips_per_video, | ||
| frames_per_clip=1, | ||
| frame_dilation=1, | ||
| ) | ||
| return sampler(video_tensor) | ||
|
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|
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| class TorchVisionNewSamplerWithTorchVisionBackend(AbstractSampler): | ||
| def __init__(self): | ||
| pass | ||
|
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| def sample_frames_uniformly(self, video_file, clips_per_video): | ||
| clip_sampling_strategy = UniformClipSamplingStrategy( | ||
| clips_per_video=clips_per_video | ||
| ) | ||
| decoder = TVVideoClipDecoder(clip_length_in_frames=1, read_audio_stream=False) | ||
| sampler = ta_vcs(clip_sampling_strategy, decoder) | ||
| return sampler(str(video_file)) | ||
|
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|
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| def main(): | ||
| """Benchmarks the performance of different samplers""" | ||
|
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| parser = argparse.ArgumentParser() | ||
| parser.add_argument( | ||
| "--bm_small_video_speed", | ||
| help="Benchmark small video decoding speed", | ||
| default=True, | ||
| action=argparse.BooleanOptionalAction, | ||
| ) | ||
| parser.add_argument( | ||
| "--bm_large_video_speed", | ||
| help="Benchmark large video decoding speed", | ||
| default=True, | ||
| action=argparse.BooleanOptionalAction, | ||
| from torchcodec.decoders import VideoDecoder | ||
| from torchcodec.samplers import clips_at_random_indices | ||
|
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|
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| def bench(f, *args, num_exp=100, warmup=0, **kwargs): | ||
|
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| for _ in range(warmup): | ||
| f(*args, **kwargs) | ||
|
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| times = [] | ||
| for _ in range(num_exp): | ||
| start = perf_counter_ns() | ||
| f(*args, **kwargs) | ||
| end = perf_counter_ns() | ||
| times.append(end - start) | ||
| return torch.tensor(times).float() | ||
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| def report_stats(times, unit="ms"): | ||
| mul = { | ||
| "ns": 1, | ||
| "µs": 1e-3, | ||
| "ms": 1e-6, | ||
| "s": 1e-9, | ||
| }[unit] | ||
| times = times * mul | ||
| std = times.std().item() | ||
| med = times.median().item() | ||
| print(f"{med = :.2f}{unit} +- {std:.2f}") | ||
| return med | ||
|
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|
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| def sample(num_clips): | ||
| decoder = VideoDecoder(VIDEO_PATH) | ||
| clips_at_random_indices( | ||
| decoder, | ||
| num_clips=num_clips, | ||
| num_frames_per_clip=10, | ||
| num_indices_between_frames=2, | ||
| ) | ||
| parser.add_argument( | ||
| "--bm_video_speed_min_run_seconds", | ||
| help="Benchmark minimum run time, in seconds, to wait per datapoint", | ||
| type=float, | ||
| default=5.0, | ||
| ) | ||
| args = parser.parse_args() | ||
|
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| small_video_path = importlib.resources.path(__package__, "nasa_13013.mp4") | ||
| small_video_path = os.fspath(str(small_video_path)) | ||
|
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| large_video_path = importlib.resources.path(__package__, "853.mp4") | ||
| large_video_path = os.fspath(str(large_video_path)) | ||
|
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| clips_per_video = 8 | ||
|
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| sampler_dict = {} | ||
| sampler_dict["TorchCodecTimeBasedSampler"] = TorchCodecTimeBasedSampler() | ||
| sampler_dict["TorchCodecIndexBasedSampler"] = TorchCodecIndexBasedSampler() | ||
| sampler_dict["TorchCodecIndexBasedSamplerWithStackedOutput"] = ( | ||
| TorchCodecIndexBasedSamplerWithStackedOutput() | ||
| ) | ||
| sampler_dict["DecordSampler"] = DecordSampler() | ||
| sampler_dict["TorchMMSamplerWithTorchVisionBackend"] = ( | ||
| TorchMMSamplerWithTorchVisionBackend() | ||
| ) | ||
| sampler_dict["TorchVisionNewSamplerWithTorchVisionBackend"] = ( | ||
| TorchVisionNewSamplerWithTorchVisionBackend() | ||
| ) | ||
|
|
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| results = [] | ||
|
|
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| for sampler_name, sampler in sampler_dict.items(): | ||
| if args.bm_small_video_speed: | ||
| sampler_result = benchmark.Timer( | ||
| stmt="sampler.sample_frames_uniformly(video_file, clips_per_video)", | ||
| globals={ | ||
| "video_file": small_video_path, | ||
| "clips_per_video": clips_per_video, | ||
| "sampler": sampler, | ||
| }, | ||
| label="uniform sampling latency for 700KB video", | ||
| sub_label=sampler_name, | ||
| description=f"uniform sampling {clips_per_video} frames", | ||
| ) | ||
| results.append( | ||
| sampler_result.blocked_autorange( | ||
| min_run_time=args.bm_video_speed_min_run_seconds | ||
| ) | ||
| ) | ||
|
|
||
| if args.bm_large_video_speed: | ||
| if sampler_name == "TorchMMSamplerWithTorchVisionBackend": | ||
| continue | ||
| sampler_result = benchmark.Timer( | ||
| stmt="sampler.sample_frames_uniformly(video_file, clips_per_video)", | ||
| globals={ | ||
| "video_file": large_video_path, | ||
| "clips_per_video": clips_per_video, | ||
| "sampler": sampler, | ||
| }, | ||
| label="uniform sampling latency for 50MB video", | ||
| sub_label=sampler_name, | ||
| description=f"uniform sampling {clips_per_video} frames", | ||
| ) | ||
| results.append( | ||
| sampler_result.blocked_autorange( | ||
| min_run_time=args.bm_video_speed_min_run_seconds | ||
| ) | ||
| ) | ||
| VIDEO_PATH = Path(__file__).parent / "../../test/resources/nasa_13013.mp4" | ||
|
|
||
| compare = benchmark.Compare(results) | ||
| compare.print() | ||
| times = bench(sample, num_clips=1, num_exp=30, warmup=2) | ||
| report_stats(times, unit="ms") | ||
| times = bench(sample, num_clips=50, num_exp=30, warmup=2) | ||
| report_stats(times, unit="ms") |
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