History

BaofengZan 37f03a77c5 fixed some issues (#400 ) * 1 动态输入 2 图像预处理和后处理和pytorch同步 3 代码格式统一 * 重新梳理代码 * tab 替换为4 space * HRNet-Semantic-Segmentation * update result img * update readme * Synchronize * Modify some bugs		2021-02-07 17:41:16 +08:00
..
CMakeLists.txt	Fix a bug(wrong CMakeLists syntax) (#377 )	2021-01-28 12:58:42 +08:00
common.hpp	fixed some issues (#400 )	2021-02-07 17:41:16 +08:00
dbnet.cpp	DBNet update (#318 )	2020-12-04 11:38:12 +08:00
logging.h	update readme	2020-11-06 22:54:15 +08:00
README.md	DBNet update (#318 )	2020-12-04 11:38:12 +08:00
utils.h	update readme	2020-11-06 22:54:15 +08:00

README.md

DBNet

The Pytorch implementation is DBNet.

How to Run

1. generate .wts
Download code and model from DBNet and config your environments.

In tools/predict.py, set save_wts as True, and run, the .wts will be generated.

onnx can also be exported, just need to set onnx as True.

cmake and make

mkdir build
cd build
cmake ..
make
sudo ./dbnet -s             // serialize model to plan file i.e. 'DBNet.engine'
sudo ./dbnet -d  ../samples // deserialize plan file and run inference, the images in samples will be processed.

For windows

https://github.com/BaofengZan/DBNet-TensorRT

Todo

1. ~~In common.hpp, the following two functions can be merged.~~

ILayer* convBnLeaky(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname, bool bias = true)

ILayer* convBnLeaky2(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname, bool bias = true)

1. The postprocess method here should be optimized, which is a little different from pytorch side.
1. ~~The input image here is resized to 640x640 directly, while the pytorch side is using letterbox method.~~