GPU 视频解码器/编码器¶

本教程介绍如何将 NVIDIA 的硬件视频解码器（NVDEC）和编码器（NVENC）与 TorchAudio 结合使用。

使用硬件编码器/解码器可以提高加载和保存某些类型视频的速度。

在 TorchAduio 中使用它们需要使用 NVENC/NVDEC 支持构建的 FFmpeg。Google Colab 预装了这样的 FFmpeg，所以你可以在 Google Colab 上运行本教程。

使用 nightly 版本更新 PyTorch 和 TorchAudio¶

在 PyTorch 2.0 发布之前，我们需要使用 PyTorch 和 TorchAudio 的夜间版本。

[2]:

%%bash
pip uninstall -y -q torch torchaudio torchvision torchtext
pip install --progress-bar off --pre torch torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu117 2> /dev/null

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://download.pytorch.org/whl/nightly/cu117
Collecting torch
  Downloading https://download.pytorch.org/whl/nightly/cu117/torch-2.0.0.dev20230209%2Bcu117-cp38-cp38-linux_x86_64.whl (1837.7 MB)
Collecting torchaudio
  Downloading https://download.pytorch.org/whl/nightly/cu117/torchaudio-2.0.0.dev20230208%2Bcu117-cp38-cp38-linux_x86_64.whl (4.4 MB)
Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from torch) (3.9.0)
Collecting pytorch-triton==2.0.0+0d7e753227
  Downloading https://download.pytorch.org/whl/nightly/pytorch_triton-2.0.0%2B0d7e753227-cp38-cp38-linux_x86_64.whl (18.7 MB)
Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0)
Requirement already satisfied: networkx in /usr/local/lib/python3.8/dist-packages (from torch) (3.0)
Requirement already satisfied: sympy in /usr/local/lib/python3.8/dist-packages (from torch) (1.7.1)
Requirement already satisfied: cmake in /usr/local/lib/python3.8/dist-packages (from pytorch-triton==2.0.0+0d7e753227->torch) (3.22.6)
Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.8/dist-packages (from sympy->torch) (1.2.1)
Installing collected packages: pytorch-triton, torch, torchaudio
Successfully installed pytorch-triton-2.0.0+0d7e753227 torch-2.0.0.dev20230209+cu117 torchaudio-2.0.0.dev20230208+cu117

安装第三方库并下载资源¶

[8]:

%%bash
pip3 install --progress-bar off boto3 2> /dev/null

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting boto3
  Downloading boto3-1.26.67-py3-none-any.whl (132 kB)
Collecting botocore<1.30.0,>=1.29.67
  Downloading botocore-1.29.67-py3-none-any.whl (10.4 MB)
Collecting jmespath<2.0.0,>=0.7.1
  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)
Collecting s3transfer<0.7.0,>=0.6.0
  Downloading s3transfer-0.6.0-py3-none-any.whl (79 kB)
Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.8/dist-packages (from botocore<1.30.0,>=1.29.67->boto3) (2.8.2)
Collecting urllib3<1.27,>=1.25.4
  Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.30.0,>=1.29.67->boto3) (1.15.0)
Installing collected packages: urllib3, jmespath, botocore, s3transfer, boto3
  Attempting uninstall: urllib3
    Found existing installation: urllib3 1.24.3
    Uninstalling urllib3-1.24.3:
      Successfully uninstalled urllib3-1.24.3
Successfully installed boto3-1.26.67 botocore-1.29.67 jmespath-1.0.1 s3transfer-0.6.0 urllib3-1.26.14

[10]:

%%bash
wget -q -O input.mp4 "https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4"

检查可用的 GPU¶

[1]:

%%bash
nvidia-smi

Thu Feb  9 15:54:05 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   58C    P0    28W /  70W |      0MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

检查 FFmpeg 安装¶

让我们进行快速的健全性检查，以确认我们构建的 FFmpeg 可以正常工作。

[55]:

%%bash
ffmpeg -hide_banner -decoders | grep -i cuvid

 V..... h264_cuvid           Nvidia CUVID H264 decoder (codec h264)
 V..... hevc_cuvid           Nvidia CUVID HEVC decoder (codec hevc)
 V..... mjpeg_cuvid          Nvidia CUVID MJPEG decoder (codec mjpeg)
 V..... mpeg1_cuvid          Nvidia CUVID MPEG1VIDEO decoder (codec mpeg1video)
 V..... mpeg2_cuvid          Nvidia CUVID MPEG2VIDEO decoder (codec mpeg2video)
 V..... mpeg4_cuvid          Nvidia CUVID MPEG4 decoder (codec mpeg4)
 V..... vc1_cuvid            Nvidia CUVID VC1 decoder (codec vc1)
 V..... vp8_cuvid            Nvidia CUVID VP8 decoder (codec vp8)
 V..... vp9_cuvid            Nvidia CUVID VP9 decoder (codec vp9)

[56]:

%%bash
ffmpeg -hide_banner -encoders | grep -i nvenc

 V..... h264_nvenc           NVIDIA NVENC H.264 encoder (codec h264)
 V..... nvenc                NVIDIA NVENC H.264 encoder (codec h264)
 V..... nvenc_h264           NVIDIA NVENC H.264 encoder (codec h264)
 V..... nvenc_hevc           NVIDIA NVENC hevc encoder (codec hevc)
 V..... hevc_nvenc           NVIDIA NVENC hevc encoder (codec hevc)

以下命令从远程服务器获取视频，使用 NVDEC （cuvid）解码并使用 NVENC 重新编码。如果此命令不起作用，则 FFmpeg 安装存在问题，TorchAudio 也无法使用它们。

[5]:

%%bash
ffmpeg -hide_banner -y -vsync 0 -hwaccel cuvid -hwaccel_output_format cuda -c:v h264_cuvid -resize 360x240 -i "https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4" -c:a copy -c:v h264_nvenc -b:v 5M test.mp4

Input #0, mov,mp4,m4a,3gp,3g2,mj2, from 'https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp42iso2avc1mp41
    encoder         : Lavf58.76.100
  Duration: 00:03:26.04, start: 0.000000, bitrate: 1294 kb/s
    Stream #0:0(eng): Video: h264 (High) (avc1 / 0x31637661), yuv420p(tv, bt709), 960x540 [SAR 1:1 DAR 16:9], 1156 kb/s, 29.97 fps, 29.97 tbr, 30k tbn, 59.94 tbc (default)
    Metadata:
      handler_name    : ?Mainconcept Video Media Handler
    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      handler_name    : #Mainconcept MP4 Sound Media Handler
Stream mapping:
  Stream #0:0 -> #0:0 (h264 (h264_cuvid) -> h264 (h264_nvenc))
  Stream #0:1 -> #0:1 (copy)
Press [q] to stop, [?] for help
Output #0, mp4, to 'test.mp4':
  Metadata:
    major_brand     : mp42
    minor_version   : 512
    compatible_brands: mp42iso2avc1mp41
    encoder         : Lavf58.29.100
    Stream #0:0(eng): Video: h264 (h264_nvenc) (Main) (avc1 / 0x31637661), cuda, 360x240 [SAR 1:1 DAR 3:2], q=-1--1, 5000 kb/s, 29.97 fps, 30k tbn, 29.97 tbc (default)
    Metadata:
      handler_name    : ?Mainconcept Video Media Handler
      encoder         : Lavc58.54.100 h264_nvenc
    Side data:
      cpb: bitrate max/min/avg: 0/0/5000000 buffer size: 10000000 vbv_delay: -1
    Stream #0:1(eng): Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      handler_name    : #Mainconcept MP4 Sound Media Handler
frame= 6175 fps=1673 q=8.0 Lsize=   41278kB time=00:03:26.17 bitrate=1640.1kbits/s speed=55.9x
video:37869kB audio:3234kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.424815%

检查 TorchAudio / FFmpeg 集成¶

[6]:

import torch
import torchaudio

print(torch.__version__)
print(torchaudio.__version__)

from torchaudio.io import StreamReader, StreamWriter

2.0.0.dev20230209+cu117
2.0.0.dev20230208+cu117

[61]:

from torchaudio.utils import ffmpeg_utils

print("Library versions:")
print(ffmpeg_utils.get_versions())
print("\nBuild config:")
print(ffmpeg_utils.get_build_config())
print("\nDecoders:")
print([k for k in ffmpeg_utils.get_video_decoders().keys() if "cuvid" in k])
print("\nEncoders:")
print([k for k in ffmpeg_utils.get_video_encoders().keys() if "nvenc" in k])

Library versions:
{'libavutil': (56, 31, 100), 'libavcodec': (58, 54, 100), 'libavformat': (58, 29, 100), 'libavfilter': (7, 57, 100), 'libavdevice': (58, 8, 100)}

Build config:
--prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared

Decoders:
['h264_cuvid', 'hevc_cuvid', 'mjpeg_cuvid', 'mpeg1_cuvid', 'mpeg2_cuvid', 'mpeg4_cuvid', 'vc1_cuvid', 'vp8_cuvid', 'vp9_cuvid']

Encoders:
['h264_nvenc', 'nvenc', 'nvenc_h264', 'nvenc_hevc', 'hevc_nvenc']

GPU 编码和解码基准测试¶

现在 FFmpeg 和生成的库已准备就绪，我们使用 TorchAudio 测试 NVDEC/NVENC。有关 TorchAudio 的流式处理 API 的基础知识，请参阅 Media I/O 教程。

[7]:

import time
import matplotlib.pyplot as plt
import pandas as pd

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)

对 NVDEC 进行基准测试`StreamReader`¶

首先，我们测试硬件解码，然后从多个位置（本地文件、网络文件、AWS S3）获取视频，并使用 NVDEC 对它们进行解码。

[9]:

import boto3
from botocore import UNSIGNED
from botocore.config import Config

print(boto3.__version__)

1.26.67

首先，我们定义将用于测试的函数。

Funcion 从头到尾对给定的源进行解码，并报告经过的时间，并返回一个图像 frmae 作为样本。test_decode

[11]:

result = torch.zeros((4, 2))
samples = [[None, None] for _ in range(4)]


def test_decode(src, config, i_sample):
  print("=" * 40)
  print("* Configuration:", config)
  print("* Source:", src)
  print("=" * 40)

  t0 = time.monotonic()
  s = StreamReader(src)
  s.add_video_stream(5, **config)

  num_frames = 0
  for i, (chunk, ) in enumerate(s.stream()):
    if i == 0:
      print(' - Chunk:', chunk.shape, chunk.device, chunk.dtype)
    if i == i_sample:
      sample = chunk[0]
    num_frames += chunk.shape[0]
  elapsed = time.monotonic() - t0

  print()
  print(f" - Processed {num_frames} frames.")
  print(f" - Elapsed: {elapsed} seconds.")
  print()

  return elapsed, sample

从本地文件解码 MP4¶

对于第一个测试，我们比较了 CPU 和 NVDEC 解码 250MB MP4 视频所需的时间。

[12]:

local_src = "input.mp4"

i_sample = 520

中央处理器¶

[13]:

cpu_conf = {
    "decoder": "h264",  # CPU decoding
}

elapsed, sample = test_decode(local_src, cpu_conf, i_sample)

========================================
* Configuration: {'decoder': 'h264'}
* Source: input.mp4
========================================
 - Chunk: torch.Size([5, 3, 540, 960]) cpu torch.uint8

 - Processed 6175 frames.
 - Elapsed: 43.65067997 seconds.

[14]:

result[0, 0] = elapsed
samples[0][0] = sample

CUDA 的¶

[15]:

cuda_conf = {
    "decoder": "h264_cuvid",  # Use CUDA HW decoder
    "hw_accel": "cuda:0",  # Then keep the memory on CUDA:0
}

elapsed, sample = test_decode(local_src, cuda_conf, i_sample)

========================================
* Configuration: {'decoder': 'h264_cuvid', 'hw_accel': 'cuda:0'}
* Source: input.mp4
========================================
 - Chunk: torch.Size([5, 3, 540, 960]) cuda:0 torch.uint8

 - Processed 6175 frames.
 - Elapsed: 5.754925530000008 seconds.

[16]:

result[0, 1] = elapsed
samples[0][1] = sample

从网络解码 MP4¶

让我们对通过网络动态检索的源运行相同的测试。

[17]:

network_src = "https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4"
i_sample = 750

中央处理器¶

[18]:

elapsed, sample = test_decode(network_src, cpu_conf, i_sample)

========================================
* Configuration: {'decoder': 'h264'}
* Source: https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4
========================================
 - Chunk: torch.Size([5, 3, 540, 960]) cpu torch.uint8

 - Processed 6175 frames.
 - Elapsed: 33.74701378400002 seconds.

[19]:

result[1, 0] = elapsed
samples[1][0] = sample

CUDA 的¶

[20]:

elapsed, sample = test_decode(network_src, cuda_conf, i_sample)

========================================
* Configuration: {'decoder': 'h264_cuvid', 'hw_accel': 'cuda:0'}
* Source: https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4
========================================
 - Chunk: torch.Size([5, 3, 540, 960]) cuda:0 torch.uint8

 - Processed 6175 frames.
 - Elapsed: 15.769149663000007 seconds.

[21]:

result[1, 1] = elapsed
samples[1][1] = sample

直接从 S4 解码 MP3¶

使用类似文件的对象输入，我们可以获取存储在 AWS S3 上的视频并对其进行解码，而无需将其保存在本地文件系统上。

[22]:

bucket = "pytorch"
key = "torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.mp4"

s3_client = boto3.client("s3", config=Config(signature_version=UNSIGNED))
i_sample = 115

定义 Helper 类¶

StreamReader 支持具有 method 的类文件对象。除此之外，如果类文件对象具有 method，则 StreamReader 会尝试使用它来更可靠地检测媒体格式。readseek

但是，的 S3 客户端响应对象的 seek 方法仅引发错误，以告知用户不支持 seek 操作。因此，我们用一个没有 method 的类来包装它。这样，StreamReader 就不会尝试使用该方法。boto3seekseek

注意

由于流式处理的性质，当使用没有 seek 方法的类文件对象时，不支持某些格式。例如，MP4 格式在文件的开头或结尾包含元数据。如果元数据位于末尾，则 StreamReader 无法解码流。seek

[23]:

# Wrapper to hide the native `seek` method of boto3, which
# only raises an error.
class UnseekableWrapper:
  def __init__(self, obj):
    self.obj = obj

  def read(self, n):
    return self.obj.read(n)

  def __str__(self):
    return str(self.obj)

中央处理器¶

[24]:

response = s3_client.get_object(Bucket=bucket, Key=key)
src = UnseekableWrapper(response["Body"])
elapsed, sample = test_decode(src, cpu_conf, i_sample)

========================================
* Configuration: {'decoder': 'h264'}
* Source: <botocore.response.StreamingBody object at 0x7fcc9b0b83a0>
========================================
 - Chunk: torch.Size([5, 3, 540, 960]) cpu torch.uint8

 - Processed 6175 frames.
 - Elapsed: 29.465254898000012 seconds.

[25]:

result[2, 0] = elapsed
samples[2][0] = sample

CUDA 的¶

[26]:

response = s3_client.get_object(Bucket=bucket, Key=key)
src = UnseekableWrapper(response["Body"])
elapsed, sample = test_decode(src, cuda_conf, i_sample)

========================================
* Configuration: {'decoder': 'h264_cuvid', 'hw_accel': 'cuda:0'}
* Source: <botocore.response.StreamingBody object at 0x7fcc9b0b8730>
========================================
 - Chunk: torch.Size([5, 3, 540, 960]) cuda:0 torch.uint8

 - Processed 6175 frames.
 - Elapsed: 3.475249672000018 seconds.

[27]:

result[2, 1] = elapsed
samples[2][1] = sample

解码和调整大小¶

在下一个测试中，我们添加预处理。NVDEC 支持多种预处理方案，这些方案也在所选硬件上执行。对于 CPU，我们通过 FFmpeg 的 filter graph 应用相同类型的软件预处理。

[28]:

i_sample = 1085

中央处理器¶

[29]:

cpu_conf = {
    "decoder": "h264",  # CPU decoding
    "filter_desc": "scale=360:240",  # Software filter
}

elapsed, sample = test_decode(local_src, cpu_conf, i_sample)

========================================
* Configuration: {'decoder': 'h264', 'filter_desc': 'scale=360:240'}
* Source: input.mp4
========================================
 - Chunk: torch.Size([5, 3, 240, 360]) cpu torch.uint8

 - Processed 6175 frames.
 - Elapsed: 17.26762169899996 seconds.

[30]:

result[3, 0] = elapsed
samples[3][0] = sample

CUDA 的¶

[31]:

cuda_conf = {
    "decoder": "h264_cuvid",  # Use CUDA HW decoder
    "decoder_option": {
        "resize": "360x240",  # Then apply HW preprocessing (resize)
    },
    "hw_accel": "cuda:0",  # Then keep the memory on CUDA:0
}

elapsed, sample = test_decode(local_src, cuda_conf, i_sample)

========================================
* Configuration: {'decoder': 'h264_cuvid', 'decoder_option': {'resize': '360x240'}, 'hw_accel': 'cuda:0'}
* Source: input.mp4
========================================
 - Chunk: torch.Size([5, 3, 240, 360]) cuda:0 torch.uint8

 - Processed 6175 frames.
 - Elapsed: 15.233130482000035 seconds.

[32]:

result[3, 1] = elapsed
samples[3][1] = sample

结果¶

下表总结了使用 CPU 和 NVDEC 解码同一介质所花费的时间。我们看到 NVDEC 的加速速度显著加快。

[33]:

res = pd.DataFrame(
    result.numpy(),
    index=["Decoding (local file)", "Decoding (network file)", "Decoding (file-like object, S3)", "Decoding + Resize"],
    columns=["CPU", "NVDEC"],
)
print(res)

                                       CPU      NVDEC
Decoding (local file)            43.650681   5.754926
Decoding (network file)          33.747013  15.769150
Decoding (file-like object, S3)  29.465256   3.475250
Decoding + Resize                17.267622  15.233130

以下代码显示了 CPU 解码和 NVDEC 生成的一些帧。它们产生的结果看似相同。

[34]:

def yuv_to_rgb(img):
  img = img.cpu().to(torch.float)
  y = img[..., 0, :, :]
  u = img[..., 1, :, :]
  v = img[..., 2, :, :]

  y /= 255
  u = u / 255 - 0.5
  v = v / 255 - 0.5

  r = y + 1.14 * v
  g = y + -0.396 * u - 0.581 * v
  b = y + 2.029 * u

  rgb = torch.stack([r, g, b], -1)
  rgb = (rgb * 255).clamp(0, 255).to(torch.uint8)
  return rgb.numpy()

[35]:

f, axs = plt.subplots(4, 2, figsize=[12.8, 19.2])
for i in range(4):
  for j in range(2):
    axs[i][j].imshow(yuv_to_rgb(samples[i][j]))
    axs[i][j].set_title(
        f"{'CPU' if j == 0 else 'NVDEC'}{' with resize' if i == 3 else ''}")
plt.plot(block=False)

[35]:

[]

_images/hw_acceleration_tutorial_60_1.png

对 NVENC 进行基准测试`StreamWriter`¶

接下来，我们使用 StreamWriter 和 NVENC 对编码速度进行基准测试。

[36]:

def test_encode(data, dst, **config):
  print("=" * 40)
  print("* Configuration:", config)
  print("* Destination:", dst)
  print("=" * 40)

  t0 = time.monotonic()
  s = StreamWriter(dst)
  s.add_video_stream(**config)
  with s.open():
    s.write_video_chunk(0, data)
  elapsed = time.monotonic() - t0

  print()
  print(f" - Processed {len(data)} frames.")
  print(f" - Elapsed: {elapsed} seconds.")
  print()
  return elapsed

result = torch.zeros((3, 3))

我们用于生成测试数据。StreamReader

[37]:

def get_data(frame_rate, height, width, format, duration=15):
  src = f"testsrc2=rate={frame_rate}:size={width}x{height}:duration={duration}"
  s = StreamReader(src=src, format="lavfi")
  s.add_basic_video_stream(-1, format=format)
  s.process_all_packets()
  video, = s.pop_chunks()
  return video

对 MP4 - 360P 进行编码¶

对于第一个测试，我们比较了 CPU 和 NVENC 编码 15 秒的小分辨率视频所需的时间。

[38]:

pict_config = {
    "height": 360,
    "width": 640,
    "frame_rate": 30000/1001,
    "format": "yuv444p",
}

video = get_data(**pict_config)

中央处理器¶

[39]:

encode_config = {
    "encoder": "libx264",
    "encoder_format": "yuv444p",
}

result[0, 0] = test_encode(video, "360p_cpu.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 360, 'width': 640, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'libx264', 'encoder_format': 'yuv444p'}
* Destination: 360p_cpu.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 6.311792093000008 seconds.

CUDA（来自 CPU Tensor）¶

现在我们测试 NVENC。这一次，数据作为编码的一部分从 CPU 内存发送到 GPU 内存。

[40]:

encode_config = {
    "encoder": "h264_nvenc",  # Use NVENC
    "encoder_format": "yuv444p",
    "encoder_option": {"gpu": "0"},  # Run encoding on the cuda:0 device
}

result[1, 0] = test_encode(video, "360p_cuda.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 360, 'width': 640, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'h264_nvenc', 'encoder_format': 'yuv444p', 'encoder_option': {'gpu': '0'}}
* Destination: 360p_cuda.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 0.5352325430000064 seconds.

CUDA （来自 CUDA Tensor）¶

如果数据已经存在于 CUDA 上，那么我们可以直接将其传递给 GPU 编码器。

[41]:

device = "cuda:0"

encode_config = {
    "encoder": "h264_nvenc",  # GPU Encoder
    "encoder_format": "yuv444p",
    "encoder_option": {"gpu": "0"},  # Run encoding on the cuda:0 device
    "hw_accel": device,  # Data comes from cuda:0 device
}

result[2, 0] = test_encode(video.to(torch.device(device)), "360p_cuda_hw.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 360, 'width': 640, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'h264_nvenc', 'encoder_format': 'yuv444p', 'encoder_option': {'gpu': '0'}, 'hw_accel': 'cuda:0'}
* Destination: 360p_cuda_hw.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 0.7045466739999711 seconds.

编码 MP4 - 720P¶

让我们对具有更高分辨率的视频运行相同的测试。

[42]:

pict_config = {
    "height": 720,
    "width": 1280,
    "frame_rate": 30000/1001,
    "format": "yuv444p",
}

video = get_data(**pict_config)

中央处理器¶

[43]:

encode_config = {
    "encoder": "libx264",
    "encoder_format": "yuv444p",
}

result[0, 1] = test_encode(video, "720p_cpu.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 720, 'width': 1280, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'libx264', 'encoder_format': 'yuv444p'}
* Destination: 720p_cpu.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 14.017578084000036 seconds.

CUDA（来自 CPU Tensor）¶

[44]:

encode_config = {
    "encoder": "h264_nvenc",
    "encoder_format": "yuv444p",
}

result[1, 1] = test_encode(video, "720p_cuda.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 720, 'width': 1280, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'h264_nvenc', 'encoder_format': 'yuv444p'}
* Destination: 720p_cuda.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 0.9797491379999883 seconds.

CUDA （来自 CUDA Tensor）¶

[45]:

device = "cuda:0"

encode_config = {
    "encoder": "h264_nvenc",
    "encoder_format": "yuv444p",
    "encoder_option": {"gpu": "0"},
    "hw_accel": device,
}

result[2, 1] = test_encode(video.to(torch.device(device)), "720p_cuda_hw.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 720, 'width': 1280, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'h264_nvenc', 'encoder_format': 'yuv444p', 'encoder_option': {'gpu': '0'}, 'hw_accel': 'cuda:0'}
* Destination: 720p_cuda_hw.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 0.7259890020000057 seconds.

编码 MP4 - 1080P¶

我们制作了更大的视频。

[46]:

pict_config = {
    "height": 1080,
    "width": 1920,
    "frame_rate": 30000/1001,
    "format": "yuv444p",
}

video = get_data(**pict_config)

中央处理器¶

[47]:

encode_config = {
    "encoder": "libx264",
    "encoder_format": "yuv444p",
}

result[0, 2] = test_encode(video, "1080p_cpu.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 1080, 'width': 1920, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'libx264', 'encoder_format': 'yuv444p'}
* Destination: 1080p_cpu.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 29.441768007999997 seconds.

CUDA（来自 CPU Tensor）¶

[48]:

encode_config = {
    "encoder": "h264_nvenc",
    "encoder_format": "yuv444p",
}

result[1, 2] = test_encode(video, "1080p_cuda.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 1080, 'width': 1920, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'h264_nvenc', 'encoder_format': 'yuv444p'}
* Destination: 1080p_cuda.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 1.5768605540000067 seconds.

CUDA （来自 CUDA Tensor）¶

[49]:

device = "cuda:0"

encode_config = {
    "encoder": "h264_nvenc",
    "encoder_format": "yuv444p",
    "encoder_option": {"gpu": "0"},
    "hw_accel": device,
}

result[2, 2] = test_encode(video.to(torch.device(device)), "1080p_cuda_hw.mp4", **pict_config, **encode_config)

========================================
* Configuration: {'height': 1080, 'width': 1920, 'frame_rate': 29.97002997002997, 'format': 'yuv444p', 'encoder': 'h264_nvenc', 'encoder_format': 'yuv444p', 'encoder_option': {'gpu': '0'}, 'hw_accel': 'cuda:0'}
* Destination: 1080p_cuda_hw.mp4
========================================

 - Processed 450 frames.
 - Elapsed: 1.4002963130000126 seconds.

结果¶

这是结果。

[50]:

labels = ["CPU", "CUDA (from CPU Tensor)", "CUDA (from CUDA Tensor)"]
columns = ["360P", "720P", "1080P"]
res = pd.DataFrame(
    result.numpy(),
    index=labels,
    columns=columns,
)
print(res)

                             360P       720P      1080P
CPU                      6.311792  14.017578  29.441769
CUDA (from CPU Tensor)   0.535233   0.979749   1.576861
CUDA (from CUDA Tensor)  0.704547   0.725989   1.400296

[51]:

plt.plot(result.T)
plt.legend(labels)
plt.xticks([i for i in range(3)], columns)
plt.grid(visible=True, axis='y')

_images/hw_acceleration_tutorial_91_0.png

生成的视频如下所示。

[52]:

from IPython.display import HTML

HTML('''
<div>
  <video width=360 controls autoplay>
    <source src="https://download.pytorch.org/torchaudio/tutorial-assets/streamwriter_360p_cpu.mp4" type="video/mp4">
  </video>
  <video width=360 controls autoplay>
    <source src="https://download.pytorch.org/torchaudio/tutorial-assets/streamwriter_360p_cuda.mp4" type="video/mp4">
  </video>
  <video width=360 controls autoplay>
    <source src="https://download.pytorch.org/torchaudio/tutorial-assets/streamwriter_360p_cuda_hw.mp4" type="video/mp4">
  </video>
</div>
''')

[52]:

结论¶

我们研究了如何构建支持 NVDEC/NVENC 的 FFmpeg 库，并从 TorchAudio 使用它们。NVDEC/NVENC 在保存/加载视频时提供了显着的速度。

GPU 视频解码器/编码器¶

使用 nightly 版本更新 PyTorch 和 TorchAudio¶

安装第三方库并下载资源¶

检查可用的 GPU¶

检查 FFmpeg 安装¶

检查 TorchAudio / FFmpeg 集成¶

GPU 编码和解码基准测试¶

对 NVDEC 进行基准测试StreamReader¶

从本地文件解码 MP4¶

中央处理器¶

CUDA 的¶

从网络解码 MP4¶

中央处理器¶

CUDA 的¶

直接从 S4 解码 MP3¶

定义 Helper 类¶

中央处理器¶

CUDA 的¶

解码和调整大小¶

中央处理器¶

CUDA 的¶

结果¶

对 NVENC 进行基准测试StreamWriter¶

对 MP4 - 360P 进行编码¶

中央处理器¶

CUDA（来自 CPU Tensor）¶

CUDA （来自 CUDA Tensor）¶

编码 MP4 - 720P¶

中央处理器¶

CUDA（来自 CPU Tensor）¶

CUDA （来自 CUDA Tensor）¶

编码 MP4 - 1080P¶

中央处理器¶

CUDA（来自 CPU Tensor）¶

CUDA （来自 CUDA Tensor）¶

结果¶

结论¶

文档

教程

资源

对 NVDEC 进行基准测试`StreamReader`¶

对 NVENC 进行基准测试`StreamWriter`¶