tensorrt跑yolov5模型

背景

作为和libtorch对比，看看到底谁更快些

方法

这里放了github的教学
https://github.com/wang-xinyu/tensorrtx

整体步骤：
1，配置环境，这里主要说一下CUDA和cudatoolkit是不一样的。
配置方法。

2，这步主要模型转换一下，然后在执行,下面是github的教学。

1. generate yolov5s.wts from pytorch with yolov5s.pt, or download .wts from model zoo

git clone https://github.com/wang-xinyu/tensorrtx.git
git clone https://github.com/ultralytics/yolov5.git
// download its weights 'yolov5s.pt'
// copy tensorrtx/yolov5/gen_wts.py into ultralytics/yolov5
// ensure the file name is yolov5s.pt and yolov5s.wts in gen_wts.py
// go to ultralytics/yolov5
python gen_wts.py
// a file 'yolov5s.wts' will be generated.

2. build tensorrtx/yolov5 and run

// put yolov5s.wts into tensorrtx/yolov5
// go to tensorrtx/yolov5
// ensure the macro NET in yolov5.cpp is s
mkdir build
cd build
cmake ..
make
sudo ./yolov5 -s             // serialize model to plan file i.e. 'yolov5s.engine'
sudo ./yolov5 -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

4. optional, load and run the tensorrt model in python

// install python-tensorrt, pycuda, etc.
// ensure the yolov5s.engine and libmyplugins.so have been built
python yolov5_trt.py

3,就可以查看结果了
经过我的对比，我发现还是libtorch更快些。