yolov5 2.0的tensorrt加速

# yolov5 The Pytorch implementation is [ultralytics/yolov5](https://github.com/ultralytics/yolov5). I was using [ultralytics/yolov5](https://github.com/ultralytics/yolov5)(The latest version). Just in case the yolov5 model updated. ## How to Run ``` 1. generate yolov5m.wts from pytorch implementation with yolov5.pt git clone https://github.com/AIpakchoi/yolov5_tensorrt.git git clone https://github.com/ultralytics/yolov5.git // download its weights 'yolov5s.pt' cd yolov5 cp ../yolov5_tensorrt/yolov5s/gen_wts.py . python gen_wts.py // a file 'yolov5s.wts' will be generated. 2. put yolov5s.wts into yolov5s, build and run mv yolov5s.wts ../yolov5_tensorrt/yolov5s/ cd ../yolov5_tensorrt/yolov5s mkdir build cd build cmake .. make sudo ./yolov5s -s // serialize model to plan file i.e. 'yolov5s.engine' sudo ./yolov5s -d ../samples // deserialize plan file and run inference, the images in samples will be processed. 3. check the images generated, as follows. _zidane.jpg and _bus.jpg ``` <p align="center"> <img src="https://user-images.githubusercontent.com/15235574/78247927-4d9fac00-751e-11ea-8b1b-704a0aeb3fcf.jpg"> </p> <p align="center"> <img src="https://user-images.githubusercontent.com/15235574/78247970-60b27c00-751e-11ea-88df-41473fed4823.jpg"> </p> ## Config - Input shape defined in yololayer.h - Number of classes defined in yololayer.h - FP16/FP32 can be selected by the macro in yolov5s.cpp - GPU id can be selected by the macro in yolov5s.cpp - NMS thresh in yolov5s.cpp - BBox confidence thresh in yolov5s.cpp - Batch size in yolov5s.cpp