YoloV5在tensorRT上加速（Ubuntu）(C++)

• 1.软件安装
• • 1.1 opencv安装
  • 1.2 Tensorrt安装
• 2.编译tensorrtx/yolov5
• 3. INT8量化

默认已经安装好了cuda、cudnn
我的cuda为11.1，cudnn为适配的版本

https://github.com/opencv/opencv/releases

tar xvf opencv-3.4.4.tar.gz
cd opencv-3.4.4
cmake .
make
sudo make install

https://developer.nvidia.com/nvidia-tensorrt-7x-download

解压压缩包

tar xvf TensorRT-7.2.3.4.Ubuntu-18.04.x86_64-gnu.cuda-11.1.cudnn8.1.tar.gz

环境变量设置

vim ~/.bashrc

export TR_PATH=/home/zc/yp/lib/TensorRT-7.2.3.4
export PATH=$PATH:$TR_PATH/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TR_PATH/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TR_PATH/targets/x86_64-linux-gnu/lib

source ~/.bashrc

cd TensorRT-7.x.x.x/python
pip install tensorrt-7.x.x.x-cp3x-none-linux_x86_64.whl

cd TensorRT-7.x.x.x/graphsurgeon
pip install graphsurgeon-0.4.1-py2.py3-none-any.whl

cd TensorRT-7.x.x.x/uff
pip install uff-0.7.5-py2.py3-none-any.whl

进入到tensorrt目录下，将下列文件夹复制到对于系统文件夹

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

安装pycuda

pip install pycuda

测试TensorRT

https://github.com/wang-xinyu/tensorrtx/tree/yolov5-v5.0/yolov5

python版本下（未做tensorRT加速）

将coco_calib.zip解压到build目录下

cd build
make clean
cmake ..
make

序列化模型

./yolov5 -s yolov5s.wts yolov5s.engine s

测试

./yolov5 -d yolov5s.engine ../samples