Deepstream 自定义检测Yolo v5模型部署
依照四部署yolo v5 环境。
Convert PyTorch model to wts file
- Download repositories
git clone https://github.com/wang-xinyu/tensorrtx.git git clone https://github.com/ultralytics/yolov5.git
- Download latest YoloV5 (YOLOv5s, YOLOv5m, YOLOv5l or YOLOv5x) weights to yolov5 folder (example for YOLOv5s)
wget https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt -P yolov5/
- Copy gen_wts.py file (from tensorrtx/yolov5 folder) to yolov5 (ultralytics) folder
cp tensorrtx/yolov5/gen_wts.py yolov5/gen_wts.py
- 复制检测检测Yolo v5 模型best.pth到yolov5 folder
- Generate wts file
cd yolov5 python3 gen_wts.py best.pt
yolov5s.wts file will be generated in yolov5 folder
【错误】No module named tqdm
pip install tqdm
【错误】No module named seaborn
pip3 install seaborn
因matplotlib无法正常安装导致seaborn无法安装，尝试：
python --version python -m pip install seaborn
再次因matplotlib失败
尝试：
https://blog.csdn.net/LYiiiiiii/article/details/119052823
sudo apt-get install python3-seaborn
再次执行
python3 gen_wts.py best.pt
【错误】no module named numpy.testing.nosetester
发生这种情况的原因是numpy和之间的版本不兼容scipy。numpy在其最新版本中已弃用numpy.testing.nosetester。
尝试
pip install scipy
【报错】No lapack/blas resources found
尝试https://blog.csdn.net/liulangdeshusheng/article/details/52433075
遇到的问题： no lapack/blas resources found 解决方法，安装lapack sudo apt-get install liblapack-dev 然后重新安装scipy，这次遇到了不一样的问题。 遇到的问题： error: library dfftpack has Fortran sources but no Fortran compiler found 解决方法，安装Fortran compiler： sudo apt-get install gfortran
再次执行
python3 gen_wts.py best.pt
生成 best.wts
依照训练的Yolo v5参数对yololayer.h和yolov5.cpp进行修改
https://zhuanlan.zhihu.com/p/365191541
1.查看Yolo v5的训练参数：
https://www.icode9.com/content-3-774443.html
- data文件夹下查看myvoc.yaml文件查找对应类别数
- weights文件夹下查看预训练模型版本-比如yolov5m
2、修改yololayer.h和yolov5.cpp文件，主要修改对应的参数和我们训练时候保持一致，不然会报错
- 对yololayer.h修改类别数
- 对yolov5.cpp主要需要根据显存大小调试batchsize大小，一般设成1就可以
Convert wts file to TensorRT model
根据https://github.com/DanaHan/Yolov5-in-Deepstream-5.0的说明，在Build tensorrtx/yolov5之前还需要：
Important Note:
You should replace yololayer.cu and hardswish.cu file in tensorrtx/yolov5
- Build tensorrtx/yolov5
cd tensorrtx/yolov5 mkdir build cd build cmake .. make
- Move generated yolov5s.wts file to tensorrtx/yolov5 folder (example for YOLOv5s)
cp yolov5/yolov5s.wts tensorrtx/yolov5/build/yolov5s.wts
- Convert to TensorRT model (yolov5s.engine file will be generated in tensorrtx/yolov5/build folder)
sudo ./yolov5 -s best.wts best.engine m
- Note: by default, yolov5 script generate model with batch size = 1 and FP16 mode.
#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32 #define DEVICE 0 // GPU id #define NMS_THRESH 0.4 #define CONF_THRESH 0.5 #define BATCH_SIZE 1
Edit yolov5.cpp file before compile if you want to change this parameters.
We can get ‘best.engine’ and ‘libmyplugin.so’ here for the future use.
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