RepVGG models from "RepVGG: Making VGG-style ConvNets Great Again" https://arxiv.org/pdf/2101.03697.pdf
For the Pytorch implementation, you can refer to DingXiaoH/RepVGG
- generate wts file.
git clone https://github.com/DingXiaoH/RepVGG.git
cd ReoVGG
You may convert a trained model into the inference-time structure with
python convert.py [weights file of the training-time model to load] [path to save] -a [model name]
For example,
python convert.py RepVGG-B2-train.pth RepVGG-B2-deploy.pth -a RepVGG-B2
Then copy gen_wts.py to RepVGG and generate .wts file, for example
python gen_wts.py -w RepVGG-B2-deploy.pth -s RepVGG-B2.wts
- build and run
cd tensorrtx/repvgg
mkdir build
cd build
cmake ..
make
sudo ./repvgg -s RepVGG-B2 // serialize model to plan file i.e. 'RepVGG-B2.engine'
sudo ./repvgg -d RepVGG-B2 // deserialize plan file and run inference