95bea3e7fa
* create psenet create psenet with weight from tensorflow * delete some useless code * repalce tab with 4 blanks * fix network bug, rewrite post-processing pse algorithm * update readme * update readme * add RepVGG
51 lines
985 B
Markdown
51 lines
985 B
Markdown
# RepVGG
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RepVGG models from
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"RepVGG: Making VGG-style ConvNets Great Again" <https://arxiv.org/pdf/2101.03697.pdf>
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For the Pytorch implementation, you can refer to [DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
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# How to run
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1. generate wts file.
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```
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git clone https://github.com/DingXiaoH/RepVGG.git
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cd ReoVGG
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```
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You may convert a trained model into the inference-time structure with
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```
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python convert.py [weights file of the training-time model to load] [path to save] -a [model name]
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```
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For example,
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```
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python convert.py RepVGG-B2-train.pth RepVGG-B2-deploy.pth -a RepVGG-B2
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```
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Then copy `gen_wts.py` to `RepVGG` and generate .wts file, for example
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```
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python gen_wts.py -w RepVGG-B2-deploy.pth -s RepVGG-B2.wts
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```
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2. build and run
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```
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cd tensorrtx/repvgg
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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 ./repvgg -s RepVGG-B2 // serialize model to plan file i.e. 'RepVGG-B2.engine'
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sudo ./repvgg -d RepVGG-B2 // deserialize plan file and run inference
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```
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