50 lines
1.5 KiB
Markdown
50 lines
1.5 KiB
Markdown
# DBNet
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The Pytorch implementation is [DBNet](https://github.com/BaofengZan/DBNet.pytorch).
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<p align="center">
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<img src="https://user-images.githubusercontent.com/20653176/89722330-00c36900-da1b-11ea-97f4-c61f9cd196fa.png">
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</p>
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## How to Run
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* 1. generate .wts
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Download code and model from [DBNet](https://github.com/BaofengZan/DBNet.pytorch) and config your environments.
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In tools/predict.py, set `save_wts` as `True`, and run, the .wts will be generated.
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onnx can also be exported, just need to set `onnx` as `True`.
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* 2. cmake and make
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```
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mkdir build
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cd build
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cmake ..
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make
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sudo ./dbnet -s // serialize model to plan file i.e. 'DBNet.engine'
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sudo ./dbnet -d ../samples // deserialize plan file and run inference, the images in samples will be processed.
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```
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## For windows
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https://github.com/BaofengZan/DBNet-TensorRT
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## Todo
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* 1. In common.hpp, the following two functions can be merged.
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```c++
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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)
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```
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```c++
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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)
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```
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* 2. The postprocess method here should be optimized, which is a little different from pytorch side.
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* 3. The input image here is resized to 640x640 directly, while the pytorch side is using `letterbox` method.
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