使用 NVDEC 加速视频解码¶
本教程介绍如何将 Nvidia 的硬件视频解码 （NVDEC） † 与 TorchAudio 结合使用。
注意
本教程是在 Google Colab 中编写的，并根据 Google Colab 的规范量身定制。
请在 Google Colab 中查看此教程。
如果您按照本教程安装 FFmpeg，请相应地调整构建配置。
要将 NVDEC 与 TorchAudio 一起使用，需要以下各项。
带有硬件视频编码器的 Nvidia GPU。
使用 NVDEC 支持编译的 FFmpeg 库。
支持 CUDA 的 PyTorch / TorchAudio。
TorchAudio 的二进制发行版是针对 FFmpeg 4 库编译的，它们包含基于硬件的解码所需的逻辑。
在以下部分中，我们将构建支持 NVDEC 的 FFmpeg 4 库，并通过 TorchAudio 的 API 启用硬件加速。然后，我们将使用 CPU 和 NVDEC 解码同一 MP4 视频所需的时间进行比较。StreamReader
† 有关 NVDEC 和 FFmpeg 的详细信息，请参阅以下文章。
https://docs.nvidia.com/video-technologies/video-codec-sdk/nvdec-video-decoder-api-prog-guide/
https://docs.nvidia.com/video-technologies/video-codec-sdk/ffmpeg-with-nvidia-gpu/#compiling-ffmpeg
https://developer.nvidia.com/blog/nvidia-ffmpeg-transcoding-guide/
检查可用的 GPU¶
[1]:
!nvidia-smi
Thu Jun 2 04:14:27 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| 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 56C P8 9W / 70W | 0MiB / 15109MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
使用 nightly 版本更新 PyTorch 和 TorchAudio¶
在 TorchAudio 0.12 发布之前，我们需要使用 PyTorch 和 TorchAudio 的夜间版本。
[2]:
!pip3 uninstall -y -q torchaudio torch
!pip3 install --progress-bar off --pre torch torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu113 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/cu113
Collecting torch
Downloading https://download.pytorch.org/whl/nightly/cu113/torch-1.13.0.dev20220601%2Bcu113-cp37-cp37m-linux_x86_64.whl (2102.2 MB)
Collecting torchaudio
Downloading https://download.pytorch.org/whl/nightly/cu113/torchaudio-0.12.0.dev20220601%2Bcu113-cp37-cp37m-linux_x86_64.whl (3.8 MB)
Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (4.2.0)
Installing collected packages: torch, torchaudio
Successfully installed torch-1.13.0.dev20220601+cu113 torchaudio-0.12.0.dev20220601+cu113
构建支持 Nvidia NVDEC 的 FFmpeg 库¶
安装 NVIDIA 视频编解码器接头¶
要使用 NVDEC 构建 FFmpeg，我们首先安装 FFmpeg 用于与视频编解码器 SDK 交互的标头。
[3]:
!git clone https://git.videolan.org/git/ffmpeg/nv-codec-headers.git
!cd nv-codec-headers && sudo make install
Cloning into 'nv-codec-headers'...
remote: Enumerating objects: 808, done.
remote: Counting objects: 100% (808/808), done.
remote: Compressing objects: 100% (688/688), done.
remote: Total 808 (delta 436), reused 0 (delta 0)
Receiving objects: 100% (808/808), 154.86 KiB | 396.00 KiB/s, done.
Resolving deltas: 100% (436/436), done.
sed 's#@@PREFIX@@#/usr/local#' ffnvcodec.pc.in > ffnvcodec.pc
install -m 0755 -d '/usr/local/include/ffnvcodec'
install -m 0644 include/ffnvcodec/*.h '/usr/local/include/ffnvcodec'
install -m 0755 -d '/usr/local/lib/pkgconfig'
install -m 0644 ffnvcodec.pc '/usr/local/lib/pkgconfig'
下载 FFmpeg 源码¶
接下来我们下载 FFmpeg 4 的源代码。任何高于 4.1 的版本都应该可以工作。我们在这里使用 4.4.2。
[4]:
!wget -q https://github.com/FFmpeg/FFmpeg/archive/refs/tags/n4.4.2.tar.gz
!tar -xf n4.4.2.tar.gz
!mv FFmpeg-n4.4.2 ffmpeg
安装 FFmpeg 构建和运行时依赖项¶
在后面的测试中，我们使用通过 HTTPS 协议流式传输的 H264 编码的 MP4 视频，因此我们在此处为它们安装库。
[5]:
!apt -qq update
!apt -qq install -y yasm libx264-dev libgnutls28-dev
... Omitted for brevity ...
Setting up libx264-dev:amd64 (2:0.152.2854+gite9a5903-2) ...
Setting up yasm (1.3.0-2build1) ...
Setting up libunbound2:amd64 (1.6.7-1ubuntu2.4) ...
Setting up libp11-kit-dev:amd64 (0.23.9-2ubuntu0.1) ...
Setting up libtasn1-6-dev:amd64 (4.13-2) ...
Setting up libtasn1-doc (4.13-2) ...
Setting up libgnutlsxx28:amd64 (3.5.18-1ubuntu1.5) ...
Setting up libgnutls-dane0:amd64 (3.5.18-1ubuntu1.5) ...
Setting up libgnutls-openssl27:amd64 (3.5.18-1ubuntu1.5) ...
Setting up libgmpxx4ldbl:amd64 (2:6.1.2+dfsg-2) ...
Setting up libidn2-dev:amd64 (2.0.4-1.1ubuntu0.2) ...
Setting up libidn2-0-dev (2.0.4-1.1ubuntu0.2) ...
Setting up libgmp-dev:amd64 (2:6.1.2+dfsg-2) ...
Setting up nettle-dev:amd64 (3.4.1-0ubuntu0.18.04.1) ...
Setting up libgnutls28-dev:amd64 (3.5.18-1ubuntu1.5) ...
Processing triggers for man-db (2.8.3-2ubuntu0.1) ...
Processing triggers for libc-bin (2.27-3ubuntu1.3) ...
/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link
使用 Nvidia CUDA 硬件支持配置 FFmpeg 构建¶
接下来我们配置 FFmpeg 构建。请注意以下事项：
我们提供了类似 和 的标志来启用 NVDEC。有关详细信息，请查看 转码指南† 。
-I/usr/local/cuda/include-L/usr/local/cuda/lib64--enable-nvdec我们还提供具有计算能力 37 的 NVCC 标志。 这是因为默认情况下，配置脚本通过编译针对计算能力 30 的示例代码来验证 NVCC，这对于 CUDA 11 来说太旧了。
许多功能被禁用以减少编译时间。
我们将库安装在 中，这是动态加载程序的活动搜索路径之一。 这样做可以找到生成的库，而无需重新启动当前会话。这可能是一个不需要的位置，例如，当一个人没有使用一次性 VM 时。
/usr/lib/
† NVIDIA FFmpeg 转码指南 https://developer.nvidia.com/blog/nvidia-ffmpeg-transcoding-guide/
[6]:
# NOTE:
# When the configure script of FFmpeg 4 checks nvcc, it uses compute
# capability of 30 (3.0) by default. CUDA 11, however, does not support
# compute capability 30.
# Here, we use 37, which is supported by CUDA 11 and both K80 and T4.
#
# Tesla K80: 37
# NVIDIA T4: 75
%env ccap=37
# NOTE:
# We disable most of the features to speed up compilation
# The necessary components are
# - demuxer: mov
# - decoder: h264
# - gnutls (HTTPS)
!cd ffmpeg && ./configure \
--prefix='/usr/' \
--extra-cflags='-I/usr/local/cuda/include' \
--extra-ldflags='-L/usr/local/cuda/lib64' \
--nvccflags="-gencode arch=compute_${ccap},code=sm_${ccap} -O2" \
--disable-doc \
--disable-static \
--disable-bsfs \
--disable-decoders \
--disable-encoders \
--disable-filters \
--disable-demuxers \
--disable-devices \
--disable-muxers \
--disable-parsers \
--disable-postproc \
--disable-protocols \
--enable-decoder=aac \
--enable-decoder=h264 \
--enable-decoder=h264_cuvid \
--enable-demuxer=mov \
--enable-filter=scale \
--enable-protocol=file \
--enable-protocol=https \
--enable-gnutls \
--enable-shared \
--enable-gpl \
--enable-nonfree \
--enable-cuda-nvcc \
--enable-libx264 \
--enable-libnpp \
--enable-nvenc \
--enable-nvdec
env: ccap=37
install prefix /usr/
source path .
C compiler gcc
C library glibc
ARCH x86 (generic)
big-endian no
runtime cpu detection yes
standalone assembly yes
x86 assembler yasm
MMX enabled yes
MMXEXT enabled yes
3DNow! enabled yes
3DNow! extended enabled yes
SSE enabled yes
SSSE3 enabled yes
AESNI enabled yes
AVX enabled yes
AVX2 enabled yes
AVX-512 enabled yes
XOP enabled yes
FMA3 enabled yes
FMA4 enabled yes
i686 features enabled yes
CMOV is fast yes
EBX available yes
EBP available yes
debug symbols yes
strip symbols yes
optimize for size no
optimizations yes
static no
shared yes
postprocessing support no
network support yes
threading support pthreads
safe bitstream reader yes
texi2html enabled no
perl enabled yes
pod2man enabled yes
makeinfo enabled no
makeinfo supports HTML no
External libraries:
alsa libx264 lzma
bzlib libxcb zlib
gnutls libxcb_shape
iconv libxcb_xfixes
External libraries providing hardware acceleration:
cuda cuvid nvdec
cuda_llvm ffnvcodec nvenc
cuda_nvcc libnpp v4l2_m2m
Libraries:
avcodec avformat swscale
avdevice avutil
avfilter swresample
Programs:
ffmpeg ffprobe
Enabled decoders:
aac hevc vc1
av1 mjpeg vp8
h263 mpeg1video vp9
h264 mpeg2video
h264_cuvid mpeg4
Enabled encoders:
Enabled hwaccels:
av1_nvdec mpeg1_nvdec vp8_nvdec
h264_nvdec mpeg2_nvdec vp9_nvdec
hevc_nvdec mpeg4_nvdec wmv3_nvdec
mjpeg_nvdec vc1_nvdec
Enabled parsers:
h263 mpeg4video vp9
Enabled demuxers:
mov
Enabled muxers:
Enabled protocols:
file tcp
https tls
Enabled filters:
aformat hflip trim
anull null vflip
atrim scale
format transpose
Enabled bsfs:
h264_mp4toannexb null vp9_superframe_split
Enabled indevs:
Enabled outdevs:
License: nonfree and unredistributable
构建和安装 FFmpeg¶
[7]:
!cd ffmpeg && make clean && make -j > /dev/null 2>&1
!cd ffmpeg && make install
INSTALL libavdevice/libavdevice.so
INSTALL libavfilter/libavfilter.so
INSTALL libavformat/libavformat.so
INSTALL libavcodec/libavcodec.so
INSTALL libswresample/libswresample.so
INSTALL libswscale/libswscale.so
INSTALL libavutil/libavutil.so
INSTALL install-progs-yes
INSTALL ffmpeg
INSTALL ffprobe
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。
[8]:
!ffprobe -decoders
ffprobe version 4.4.2 Copyright (c) 2007-2021 the FFmpeg developers
built with gcc 7 (Ubuntu 7.5.0-3ubuntu1~18.04)
configuration: --prefix=/usr/ --extra-cflags=-I/usr/local/cuda/include --extra-ldflags=-L/usr/local/cuda/lib64 --nvccflags='-gencode arch=compute_37,code=sm_37 -O2' --disable-doc --disable-static --disable-bsfs --disable-decoders --disable-encoders --disable-filters --disable-demuxers --disable-devices --disable-muxers --disable-parsers --disable-postproc --disable-protocols --enable-decoder=aac --enable-decoder=h264 --enable-decoder=h264_cuvid --enable-demuxer=mov --enable-filter=scale --enable-protocol=file --enable-protocol=https --enable-gnutls --enable-shared --enable-gpl --enable-nonfree --enable-cuda-nvcc --enable-libx264 --enable-libnpp --enable-nvenc --enable-nvdec
libavutil 56. 70.100 / 56. 70.100
libavcodec 58.134.100 / 58.134.100
libavformat 58. 76.100 / 58. 76.100
libavdevice 58. 13.100 / 58. 13.100
libavfilter 7.110.100 / 7.110.100
libswscale 5. 9.100 / 5. 9.100
libswresample 3. 9.100 / 3. 9.100
Decoders:
V..... = Video
A..... = Audio
S..... = Subtitle
.F.... = Frame-level multithreading
..S... = Slice-level multithreading
...X.. = Codec is experimental
....B. = Supports draw_horiz_band
.....D = Supports direct rendering method 1
------
V....D av1 Alliance for Open Media AV1
V...BD h263 H.263 / H.263-1996, H.263+ / H.263-1998 / H.263 version 2
VFS..D h264 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10
V..... h264_cuvid Nvidia CUVID H264 decoder (codec h264)
VFS..D hevc HEVC (High Efficiency Video Coding)
V....D mjpeg MJPEG (Motion JPEG)
V.S.BD mpeg1video MPEG-1 video
V.S.BD mpeg2video MPEG-2 video
VF..BD mpeg4 MPEG-4 part 2
V....D vc1 SMPTE VC-1
VFS..D vp8 On2 VP8
VFS..D vp9 Google VP9
A....D aac AAC (Advanced Audio Coding)
[9]:
!ffprobe -hide_banner "https://download.pytorch.org/torchaudio/tutorial-assets/stream-api/NASAs_Most_Scientifically_Complex_Space_Observatory_Requires_Precision-MP4_small.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
vendor_id : [0][0][0][0]
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
vendor_id : [0][0][0][0]
使用 TorchAudio 对 NVDEC 进行基准测试¶
现在 FFmpeg 和生成的库已准备就绪，我们使用 TorchAudio 测试 NVDEC。有关 TorchAudio 的流式 API 的基础知识，请参考 流式 API 教程。
注意
如果在导入类 StreamReader 后重新构建 FFmpeg，则需要重新启动会话以激活新构建的 FFmpeg 库。
[10]:
import torch
import torchaudio
print(torch.__version__)
print(torchaudio.__version__)
from torchaudio.io import StreamReader
1.13.0.dev20220601+cu113
0.12.0.dev20220601+cu113
[11]:
!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 boto3-1.24.1-py3-none-any.whl (132 kB)
Collecting botocore<1.28.0,>=1.27.1
Downloading botocore-1.27.1-py3-none-any.whl (8.8 MB)
Collecting s3transfer<0.7.0,>=0.6.0
Downloading s3transfer-0.6.0-py3-none-any.whl (79 kB)
Collecting jmespath<2.0.0,>=0.7.1
Downloading jmespath-1.0.0-py3-none-any.whl (23 kB)
Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.7/dist-packages (from botocore<1.28.0,>=1.27.1->boto3) (2.8.2)
Collecting urllib3<1.27,>=1.25.4
Downloading urllib3-1.26.9-py2.py3-none-any.whl (138 kB)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.28.0,>=1.27.1->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 boto3-1.24.1 botocore-1.27.1 jmespath-1.0.0 s3transfer-0.6.0 urllib3-1.26.9
[12]:
import time
import matplotlib.pyplot as plt
import pandas as pd
import boto3
from botocore import UNSIGNED
from botocore.config import Config
print(boto3.__version__)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
1.24.1
[13]:
!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"
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
[14]:
result = torch.zeros((4, 2))
samples = [[None, None] for _ in range(4)]
def test(src, config, i_sample):
print("=" * 40)
print("* Configuration:", config)
print("* Source:", src)
print("=" * 40)
s = StreamReader(src)
s.add_video_stream(5, **config)
t0 = time.monotonic()
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。
[15]:
local_src = "input.mp4"
cpu_conf = {
"decoder": "h264", # CPU decoding
}
cuda_conf = {
"decoder": "h264_cuvid", # Use CUDA HW decoder
"hw_accel": "cuda:0", # Then keep the memory on CUDA:0
}
i_sample = 520
中央处理器¶
[16]:
elapsed, sample = test(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: 45.752042501000005 seconds.
[17]:
result[0, 0] = elapsed
samples[0][0] = sample
CUDA 的¶
[18]:
elapsed, sample = test(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: 7.458571206999977 seconds.
[19]:
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。
[20]:
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¶
[21]:
elapsed, sample = test(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: 40.36345302500001 seconds.
[22]:
result[1, 0] = elapsed
samples[1][0] = sample
CUDA 的¶
[23]:
elapsed, sample = test(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: 4.222158643999933 seconds.
[24]:
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。
[25]:
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 medi 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
[26]:
# 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¶
[27]:
response = s3_client.get_object(Bucket=bucket, Key=key)
src = UnseekableWrapper(response["Body"])
elapsed, sample = test(src, cpu_conf, i_sample)
========================================
* Configuration: {'decoder': 'h264'}
* Source: <botocore.response.StreamingBody object at 0x7fecbfcb5c90>
========================================
- Chunk: torch.Size([5, 3, 540, 960]) cpu torch.uint8
- Processed 6175 frames.
- Elapsed: 40.16508613600001 seconds.
[28]:
result[2, 0] = elapsed
samples[2][0] = sample
CUDA 的¶
[29]:
response = s3_client.get_object(Bucket=bucket, Key=key)
src = UnseekableWrapper(response["Body"])
elapsed, sample = test(src, cuda_conf, i_sample)
========================================
* Configuration: {'decoder': 'h264_cuvid', 'hw_accel': 'cuda:0'}
* Source: <botocore.response.StreamingBody object at 0x7fecbfc70390>
========================================
- Chunk: torch.Size([5, 3, 540, 960]) cuda:0 torch.uint8
- Processed 6175 frames.
- Elapsed: 4.510979067999983 seconds.
[30]:
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。
[31]:
cpu_conf = {
"decoder": "h264", # CPU decoding
"filter_desc": "scale=360:240", # Software filter
}
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
}
i_sample = 1085
中央处理器¶
[32]:
elapsed, sample = test(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: 18.506949264000013 seconds.
[33]:
result[3, 0] = elapsed
samples[3][0] = sample
CUDA 的¶
[34]:
elapsed, sample = test(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: 4.9442481019999605 seconds.
[35]:
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。
[36]:
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) 45.752041 7.458571
Decoding (network file) 40.363453 4.222158
Decoding (file-like object, S3) 40.165085 4.510979
Decoding + Resize 18.506948 4.944248
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。
[37]:
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()
[38]:
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)
[38]:
[]
结论¶
我们研究了如何构建支持 NVDEC 的 FFmpeg 库，并从 TorchAudio 使用它。NVDEC 提供了显著的加速。