86 lines
2.9 KiB
Markdown
86 lines
2.9 KiB
Markdown
# 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/v0.1), 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] // 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)
|
|
|