ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

# ubuntu下yolov8 tensorrt模型加速部署【实战】 TensorRT-Alpha基于tensorrt+cuda c++实现模型end2end的gpu加速，支持win10、linux，在2023年已经更新模型：YOLOv8, YOLOv7, YOLOv6, YOLOv5, YOLOv4, YOLOv3, YOLOX, YOLOR,pphumanseg,u2net,EfficientDet。仓库TensorRT-Alpha：https://github.com/FeiYull/TensorRT-Alpha

win10教程:

http://t.csdn.cn/KCoNn

一、加速结果展示

1.1 性能速览

快速看看yolov8n 在移动端RTX2070m(8G)的新能表现：

modelvideo resolutionmodel input sizeGPU Memory-UsageGPU-Utilyolov8n1920x10808x3x640x6401093MiB/7982MiB14%

下图是yolov8n的运行时间开销，单位是ms：

图片[1]-ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

更多TensorRT-Alpha测试录像在B站视频： B站：YOLOv8n B站：YOLOv8s

图片[2]-ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

在这里插入图片描述

1.2精度对齐

下面是左边是python框架推理结果，右边是TensorRT-Alpha推理结果。

图片[3]-ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

yolov8n : Offical( left ) vs Ours( right )

图片[4]-ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

在这里插入图片描述

yolov7-tiny : Offical( left ) vs Ours( right )

图片[5]-ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

在这里插入图片描述

yolov6s : Offical( left ) vs Ours( right )

图片[6]-ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

在这里插入图片描述

yolov5s : Offical( left ) vs Ours( right )

YOLOv4 YOLOv3 YOLOR YOLOX略。

二、Ubuntu18.04环境配置

如果您对tensorrt不是很熟悉，请务必保持下面库版本一致。

2.1 安装工具链和opencv

sudo apt-get update
sudo apt-get install build-essential
sudo apt-get install git
sudo apt-get install gdb
sudo apt-get install cmake

sudo apt-get install libopencv-dev
# pkg-config –modversion opencv

2.2 安装Nvidia相关库

注：Nvidia相关网站需要注册账号。

2.2.1 安装Nvidia显卡驱动

ubuntu-drivers devices
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
sudo apt install nvidia-driver-470-server # for ubuntu18.04
nvidia-smi

2.2.2 安装 cuda11.3

进入链接: https://developer.nvidia.com/cuda-toolkit-archive

选择：CUDA Toolkit 11.3.0(April 2021)选择：[Linux] -> [x86_64] -> [Ubuntu] -> [18.04] -> [runfile(local)]

在网页你能看到下面安装命令，我这里已经拷贝下来：

wget https://developer.download.nvidia.com/compute/cuda/11.3.0/local_installers/cuda_11.3.0_465.19.01_linux.run
sudo sh cuda_11.3.0_465.19.01_linux.run

cuda的安装过程中，需要你在bash窗口手动作一些选择，这里选择如下：

select：[continue] -> [accept] -> 接着按下回车键取消Driver和465.19.01这个选项，如下图(it is important!) -> [Install]

图片[7]-ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

bash窗口提示如下表示安装完成

#===========
#= Summary =
#===========

#Driver: Not Selected
#Toolkit: Installed in /usr/local/cuda-11.3/
#……

把cuda添加到环境变量：

vim ~/.bashrc

把下面拷贝到 .bashrc里面

# cuda v11.3
export PATH=/usr/local/cuda-11.3/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export CUDA_HOME=/usr/local/cuda-11.3

刷新环境变量和验证

source ~/.bashrc
nvcc -V

bash窗口打印如下信息表示cuda11.3安装正常

nvcc: NVIDIA (R) Cuda compiler driver 
Copyright (c) 2005-2021 NVIDIA Corporation 
Built on Sun_Mar_21_19:15:46_PDT_2021 
Cuda compilation tools, release 11.3, V11.3.58 
Build cuda_11.3.r11.3/compiler.29745058_0

2.2.3 安装 cudnn8.2

进入网站：https://developer.nvidia.com/rdp/cudnn-archive选择： Download cuDNN v8.2.0 (April 23rd, 2021), for CUDA 11.x选择： cuDNN Library for Linux (x86_64)你将会下载这个压缩包: “cudnn-11.3-linux-x64-v8.2.0.53.tgz”

# 解压
tar -zxvf cudnn-11.3-linux-x64-v8.2.0.53.tgz

将cudnn的头文件和lib拷贝到cuda11.3的安装目录下：

sudo cp cuda/include/cudnn.h /usr/local/cuda/include/
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64/
sudo chmod a+r /usr/local/cuda/include/cudnn.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*

2.2.4 下载 tensorrt8.4.2.4

本教程中，tensorrt只需要下载\、解压即可，不需要安装。

进入网站： https://developer.nvidia.cn/nvidia-tensorrt-8x-download把这个打勾： I Agree To the Terms of the NVIDIA TensorRT License Agreement选择: TensorRT 8.4 GA Update 1选择: TensorRT 8.4 GA Update 1 for Linux x86_64 and CUDA 11.0, 11.1, 11.2, 11.3, 11.4, 11.5, 11.6 and 11.7 TAR Package你将会下载这个压缩包: “TensorRT-8.4.2.4.Linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz”

# 解压
tar -zxvf TensorRT-8.4.2.4.Linux.x86_64-gnu.cuda-11.6.cudnn8.4.tar.gz
# 快速验证一下tensorrt+cuda+cudnn是否安装正常
cd TensorRT-8.4.2.4/samples/sampleMNIST
make
cd ../../bin/

导出tensorrt环境变量(it is important!)，注：将LD_LIBRARY_PATH:后面的路径换成你自己的！后续编译onnx模型的时候也需要执行下面第一行命令

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/xxx/temp/TensorRT-8.4.2.4/lib
./sample_mnist

bash窗口打印类似如下图的手写数字识别表明cuda+cudnn+tensorrt安装正常

图片[8]-ubuntu下yolov8 tensorrt模型加速部署【实战】-卡咪卡咪哈-一个博客

2.2.5 下载仓库TensorRT-Alpha并设置

git clone https://github.com/FeiYull/tensorrt-alpha

设置您自己TensorRT根目录:

git clone https://github.com/FeiYull/tensorrt-alpha
cd tensorrt-alpha/cmake
vim common.cmake
# 在文件common.cmake中的第20行中，设置成你自己的目录，别和我设置一样的路径eg:
# set(TensorRT_ROOT /root/TensorRT-8.4.2.4)

三、YOLOv8模型部署

3.1 获取YOLOv8onnx文件

直接在网盘下载 weiyun or google driver 或者使用如下命令导出onnx:

# yolov8 官方仓库: https://github.com/ultralytics/ultralytics # yolov8 官方教程: https://docs.ultralytics.com/quickstart/ # TensorRT-Alpha will be updated synchronously as soon as possible!r/>
# 安装 yolov8
conda create -n yolov8 python==3.8 -y
conda activate yolov8
pip install ultralytics==8.0.5
pip install onnx==1.12.0r/>
# 下载官方权重(“.pt” file)
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x6.pt

导出 onnx:

# 640
yolo mode=export model=yolov8n.pt format=onnx dynamic=True #simplify=True
yolo mode=export model=yolov8s.pt format=onnx dynamic=True #simplify=True
yolo mode=export model=yolov8m.pt format=onnx dynamic=True #simplify=True
yolo mode=export model=yolov8l.pt format=onnx dynamic=True #simplify=True
yolo mode=export model=yolov8x.pt format=onnx dynamic=True #simplify=True
# 1280
yolo mode=export model=yolov8x6.pt format=onnx dynamic=True #simplify=True

3.2 编译 onnx

# 把你的onnx文件放到这个路径:tensorrt-alpha/data/yolov8
cd tensorrt-alpha/data/yolov8
# 请把LD_LIBRARY_PATH:换成您自己的路径。
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/TensorRT-8.4.2.4/lib
# 640
../../../../TensorRT-8.4.2.4/bin/trtexec –onnx=yolov8n.onnx –saveEngine=yolov8n.trt –buildOnly –minShapes=images:1x3x640x640 –optShapes=images:4x3x640x640 –maxShapes=images:8x3x640x640
../../../../TensorRT-8.4.2.4/bin/trtexec –onnx=yolov8s.onnx –saveEngine=yolov8s.trt –buildOnly –minShapes=images:1x3x640x640 –optShapes=images:4x3x640x640 –maxShapes=images:8x3x640x640
../../../../TensorRT-8.4.2.4/bin/trtexec –onnx=yolov8m.onnx –saveEngine=yolov8m.trt –buildOnly –minShapes=images:1x3x640x640 –optShapes=images:4x3x640x640 –maxShapes=images:8x3x640x640
../../../../TensorRT-8.4.2.4/bin/trtexec –onnx=yolov8l.onnx –saveEngine=yolov8l.trt –buildOnly –minShapes=images:1x3x640x640 –optShapes=images:4x3x640x640 –maxShapes=images:8x3x640x640
../../../../TensorRT-8.4.2.4/bin/trtexec –onnx=yolov8x.onnx –saveEngine=yolov8x.trt –buildOnly –minShapes=images:1x3x640x640 –optShapes=images:4x3x640x640 –maxShapes=images:8x3x640x640
# 1280
../../../../TensorRT-8.4.2.4/bin/trtexec –onnx=yolov8x6.onnx –saveEngine=yolov8x6.trt –buildOnly –minShapes=images:1x3x1280x1280 –optShapes=images:4x3x1280x1280 –maxShapes=images:8x3x1280x1280

你将会的到例如：yolov8n.trt、yolov8s.trt、yolov8m.trt等文件。

3.3 编译运行

git clone https://github.com/FeiYull/tensorrt-alpha
cd tensorrt-alpha/yolov8
mkdir build
cd build
cmake ..
make -j10
# 注: 效果图默认保存在路径 tensorrt-alpha/yolov8/build

# 下面参数解释
# –show 表示可视化结果
# –savePath 表示保存，默认保存在build目录
# –savePath=../ 保存在上一级目录

## 640
# 推理图片
./app_yolov8 –model=../../data/yolov8/yolov8n.trt –size=640 –batch_size=1 –img=../../data/6406407.jpg –show –savePath
./app_yolov8 –model=../../data/yolov8/yolov8n.trt –size=640 –batch_size=8 –video=../../data/people.mp4 –show –savePath

# 推理视频
./app_yolov8 –model=../../data/yolov8/yolov8n.trt –size=640 –batch_size=8 –video=../../data/people.mp4 –show –savePath=../

# 在线推理相机视频
./app_yolov8 –model=../../data/yolov8/yolov8n.trt –size=640 –batch_size=2 –cam_id=0 –show

## 1280
# infer camera
./app_yolov8 –model=../../data/yolov8/yolov8x6.trt –size=1280 –batch_size=2 –cam_id=0 –show

[video(video-zRQTyZp3-1675415874102)(type-bilibili)(url-https://player.bilibili.com/player.html?aid=778153500)(image-https://img-blog.csdnimg.cn/img_convert/4faa0b726044d139a1282d3883c6ed08.jpeg)(title-yolov8 tensorrt cuda模型推理加速部署TensorRT-Alpha《ski facility》)]

[video(video-Ej2C6hgr-1675415817040)(type-bilibili)(url-https://player.bilibili.com/player.html?aid=650665729)(image-https://img-blog.csdnimg.cn/img_convert/04c8cb5b7ebe3fd410e4af53548a668f.jpeg)(title-yolov8 tensorrt cuda模型推理加速部署TensorRT-Alpha《NewYork-Stree》)]

[video(video-xbOkXTEV-1675415667896)(type-bilibili)(url-https://player.bilibili.com/player.html?aid=565721755)(image-https://img-blog.csdnimg.cn/img_convert/a4469f9c7a048393d4fee5b7bba2893d.jpeg)(title-yolov7 tensorrt cuda模型推理加速部署TensorRT-Alpha《Korea-Night》)]