yolov7 tensorrt8.2 生成c++ dll，并用c#调用，win10， cuda11.4.3 ，cudnn8.2

# YOLOv7 The Pytorch implementation is [WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7). The tensorrt code is derived from [QIANXUNZDL123/tensorrtx-yolov7](https://github.com/QIANXUNZDL123/tensorrtx-yolov7) ## Contributors <a href="https://github.com/QIANXUNZDL123"><img src="https://avatars.githubusercontent.com/u/46549527?v=4?s=48" width="40px;" alt=""/></a> <a href="https://github.com/lindsayshuo"><img src="https://avatars.githubusercontent.com/u/45239466?v=4?s=48" width="40px;" alt=""/></a> <a href="https://github.com/wang-xinyu"><img src="https://avatars.githubusercontent.com/u/15235574?s=48&v=4" width="40px;" alt=""/></a> ## Requirements - TensorRT 8.0+ - OpenCV 3.4.0+ ## Different versions of yolov7 Currently, we support yolov7 v0.1 - For yolov7 v0.1, download .pt from [yolov7 release v0.1](https://github.com/WongKinYiu/yolov7/releases/tag/v0.10), then follow how-to-run in current page. ## Config - Choose the model tiny/v7/x/d6/w6/e6/e6e from command line arguments. - Check more configs in [include/config.h](./include/config.h) ## How to Run, yolov7-tiny as example 1. generate .wts from pytorch with .pt, or download .wts from model zoo ``` // download https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt cp {tensorrtx}/yolov7/gen_wts.py {WongKinYiu}/yolov7 cd {WongKinYiu}/yolov7 python gen_wts.py // a file 'yolov7.wts' will be generated. ``` 2. build tensorrtx/yolov7 and run ``` cd {tensorrtx}/yolov7/ // update kNumClass in config.h if your model is trained on custom dataset mkdir build cd build cp {WongKinYiu}/yolov7/yolov7.wts {tensorrtx}/yolov7/build cmake .. make sudo ./yolov7 -s [.wts] [.engine] [t/v7/x/w6/e6/d6/e6e gd gw] // serialize model to plan file sudo ./yolov7 -d [.engine] [image folder] // deserialize and run inference, the images in [image folder] will be processed. // For example yolov7 sudo ./yolov7 -s yolov7.wts yolov7.engine v7 sudo ./yolov7 -d yolov7.engine ../images ``` 3. check the images generated, as follows. _zidane.jpg and _bus.jpg 4. optional, load and run the tensorrt model in python ``` // install python-tensorrt, pycuda, etc. // ensure the yolov7.engine and libmyplugins.so have been built python yolov7_trt.py ``` # INT8 Quantization 1. Prepare calibration images, you can randomly select 1000s images from your train set. For coco, you can also download my calibration images `coco_calib` from [GoogleDrive](https://drive.google.com/drive/folders/1s7jE9DtOngZMzJC1uL307J2MiaGwdRSI?usp=sharing) or [BaiduPan](https://pan.baidu.com/s/1GOm_-JobpyLMAqZWCDUhKg) pwd: a9wh 2. unzip it in yolov7/build 3. set the macro `USE_INT8` in config.h and make 4. serialize the model and test <p align="center"> <img src="https://user-images.githubusercontent.com/15235574/78247927-4d9fac00-751e-11ea-8b1b-704a0aeb3fcf.jpg" height="360px;"> </p> ## More Information See the readme in [home page.](https://github.com/wang-xinyu/tensorrtx)