Yolov5 + Tensorrt

生成engine文件

1. generate .wts from pytorch with .pt, or download .wts from model zoo

   git clone -b v5.0 https://github.com/ultralytics/yolov5.git
   git clone https://github.com/wang-xinyu/tensorrtx.git
   // download https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt
   cp {tensorrtx}/yolov5/gen_wts.py {ultralytics}/yolov5
   cd {ultralytics}/yolov5
   python gen_wts.py -w yolov5s.pt -o yolov5s.wts
   // a file 'yolov5s.wts' will be generated.
   

2. build tensorrtx/yolov5 and run

   cd {tensorrtx}/yolov5/
   // update CLASS_NUM in yololayer.h if your model is trained on custom dataset
   mkdir build
   cd build
   cp {ultralytics}/yolov5/yolov5s.wts {tensorrtx}/yolov5/build
   cmake ..
   make
   sudo ./yolov5 -s [.wts] [.engine] [s/m/l/x/s6/m6/l6/x6 or c/c6 gd gw]  // serialize model to plan file
   sudo ./yolov5 -d [.engine] [image folder]  // deserialize and run inference, the images in [image folder] will be processed.
   // For example yolov5s
   sudo ./yolov5 -s yolov5s.wts yolov5s.engine s
   sudo ./yolov5 -d yolov5s.engine ../samples
   // For example Custom model with depth_multiple=0.17, width_multiple=0.25 in yolov5.yaml
   sudo ./yolov5 -s yolov5_custom.wts yolov5.engine c 0.17 0.25
   sudo ./yolov5 -d yolov5.engine ../samples
   
   

模型推理

```
mkdir build
cd build
cmake ..
make -j6
```

注意点

```
1. 设置好engine的路径 ok
2. 图片目前采用文件读取，后续肯定要转换为实时视频流
```