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Ubuntu+Yolov5+TensortRT加速部署

2022-03-09 21:31:34 作者：互联网

Ubuntu+Yolov5+TensortRT加速部署

0.前序

测试机器Ubuntu18.04 GPU: 3060, CUDA11.4, CUDA10.2均可

1.软件下载

2.软件安装

2.1 驱动安装

2.2 CUDA安装

wget https://developer.download.nvidia.com/compute/cuda/11.4.2/loc
al_installers/cuda_11.4.2_470.57.02_linux.run

sudo sh cuda_11.4.2_470.57.02_linux.run

终端输入

gedit ~/.bashrc

文件后面追加

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.4/lib64
export PATH=$PATH:/usr/local/cuda-11.4/bin
export CUDA_HOME=$CUDA_HOME:/usr/local/cuda-11.4

更新环境变量配置

source ~/.bashrc

至此cuda安装完成，输入nvcc -V 命令查看cuda查看

2.3 CUDNN安装

下载如下

sudo dpkg -i libcudnn8_8.2.4.15-1+cuda11.4_amd64.deb  
sudo dpkg -i libcudnn8-dev_8.2.4.15-1+cuda11.4_amd64.deb  
sudo dpkg -i libcudnn8-samples_8.2.4.15-1+cuda11.4_amd64.deb

验证

ldconfig -v | grep cudnn

解压安装包

tar zxvf cudnn-11.4-linux-x64-v8.2.4.tgz

$ 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 /usr/local/cuda/lib64/libcudnn*

软连接创建

sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.2.4 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.2.4 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.2.4 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.2.4 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.2.4 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.2.4 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
sudo ln -sf /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn.so.8.2.4 /usr/local/cuda-11.4/targets/x86_64-linux/lib/libcudnn.so.8

测试

# Copy the cuDNN sample to a writable path.
cp -r /usr/src/cudnn_samples_v7/ $HOME

# Go to the writable path.
 cd  $HOME/cudnn_samples_v7/mnistCUDNN

#Compile the mnistCUDNN sample.
make clean && make

#Run the mnistCUDNN sample.
 ./mnistCUDNN

If cuDNN is properly installed and running on your Linux system, you will see a message similar to the following:
Test passed!

2.4 Opencv安装

相关下载opencv_contribute3.4.4
Opencv3.4.4解压

tar zxvf opencv-3.4.4.tar.gz

下载QT

apt-get install qt4-default

相关下载详见FFmpeg(GPU)-CUDA11.1-CUDNN8-OpenCV4.2(GPU)-Ubuntu18.04构建docker镜像 -Opencv安装
编译安装命令

cmake -D CMAKE_BUILD_TYPE=RELEASE -D PYTHON_DEFAULT_EXECUTABLE=$(/home/xujunkai/test/python_env/bin/python3 -c "import sys; print(sys.executable)") -D PYTHON3_EXECUTABLE=$(/home/xujunkai/test/python_env/bin/python3 -c "import sys; print(sys.executable)") -D PYTHON3_NUMPY_INCLUDE_DIRS=$(/home/xujunkai/test/python_env/bin/python3 -c "import numpy; print(numpy.get_include())") -D PYTHON3_PACKAGES_PATH=$(/home/xujunkai/test/python_env/bin/python3 -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_OPENGL=ON -D WITH_CUDA=ON -D ENABLE_FAST_MATH=1 -D WITH_FFMPEG=ON -D CUDA_FAST_MATH=1 -D CUDA_NVCC_FLAGS="-D_FORCE_INLINES" -D CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-11.4/ -D WITH_NVCUVID=ON -D CUDA_ARCH_BIN=5.3,6.0,6.1,7.0,7.5 -D CUDA_ARCH_PTX=7.5 -D WITH_CUBLAS=1 -D OPENCV_EXTRA_MODULES_PATH=/home/xujunkai/test/opencv_base/opencv_contrib/modules -D CUDA_nppicom_LIBRARY=stdc++ -D WITH_OPENMP=ON ..

错误：fatal error: linux/videodev.h: 没有那个文件或目录 参照

sudo apt-get install libv4l-dev
cd /usr/include/linux
sudo ln -s ../libv4l1-videodev.h videodev.h

错误: fatal error: dynlink_nvcuvid.h: 没有那个文件或目录 参照
其他错误可参照FFmpeg(GPU)-CUDA11.1-CUDNN8-OpenCV4.2(GPU)-Ubuntu18.04构建docker镜像 -Opencv安装错误解决办法
错误: modules/xfeatures2d/src/boostdesc.cpp:653:20: fatal error: boostdesc_bgm.i: 没有那个文件或目录#include "boostdesc_bgm.i"

boostdesc_bgm 链接提取码：3xqi

将   boostdesc_bgm 内步所有文件放在 opencv_contrib/modules/xfeatures2d/src/  下

安装

make -j8
make install

2.5 Python环境安装

推荐编译安装

2.6 pytorch安装

安装

pip3 install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudi
o==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_sta
ble.html

2.7 yolov5下载

克隆项目

git clone https://github.com/ultralytics/yolov5.git

下载依赖

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt

下载Yolov5s.pt v4.0直通车提取码:vykq
测试

python detect.py --source ./data/images/ --weights weights/yolov5s.pt --conf 0.4

2.8 TensorRT安装

解压

tar zxvf TensorRT-8.2.1.8.Linux.x86_64-gnu.cuda-11.4.cudnn8.2.tar.gz

添加环境变量

export LD_LIBRARY_PATH=TensorRT解压路径/lib:$LD_LIBRARY_PAT

CUDA环境指定

vi ~/.bashrc
export CUDA_INSTALL_DIR=/usr/local/cuda-11.4   
export CUDNN_INSTALL_DIR=/usr/local/cuda-11.4
source ~/.bashrc

复制TensorRT路径下/lib, /include 文件夹对应系统文件夹

sudo cp -r ./lib/* /usr/lib
sudo cp -r ./include/* /usr/include

安装pycuda

pip install pycuda

报错: #include pyconfig.h

apt-get install -y libpython3.6-dev
export CPLUS_INCLUDE_PATH=/usr/local/python3.6m

测试

cd TensorRT-8.2.1.8/python

pip install tensorrt-8.2.1.8-cp36-none-linux_x86_64.whl

# 进入python解释器
import tensorrt
tensorrt.__version__

TensorRT示例代码测试

# TensorRT 为7.X版本需要去data/mnist 执行 python download_pgms.py瞎子啊数据集
cd 路径/TensorRT-8.2.1.8/samples/sampleMNIST
# 编译
make clean
make

运行

cd 路径/TensorRT-8.2.1.8/bin
./sample_mnist

2.9 编译tensorrt/yolov5

下载tensorrtx

git clone https://github.com/wang-xinyu/tensorrtx.git

将tensorrt/yolov5下的gen_wts.py 复制yolov5项目目录下
生成yolov5s.wts文件

python gen_wts.py --weights weights/yolov5s.pt

编译

# 创建build
mkdir build
cd build
# 拷贝 yolov5s.wts 到tensorrtx/yolov5/build
# yololayer.h内CLASS_NUM 可以定义种类类型
cmake ..
make

执行TensorRT加速后的yolov5命令

yolov5 -s yolov5s.wts yolov5s.engine s

标签：Yolov5,lib,11.4,linux,TensortRT,usr,Ubuntu,local,cuda
来源： https://www.cnblogs.com/xujunkai/p/15987098.html