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PaddleSeg Quantitative Model C++ Deployment Example
infer.cc in this directory can help you quickly complete the inference acceleration of PaddleSeg quantization model deployment on CPU.
Deployment Preparations
FastDeploy Environment Preparations
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- For the software and hardware requirements, please refer to FastDeploy Environment Requirements.
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- For the installation of FastDeploy Python whl package, please refer to FastDeploy Python Installation.
Quantized Model Preparations
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- You can directly use the quantized model provided by FastDeploy for deployment.
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- You can use one-click automatical compression tool provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.(Note: The quantized classification model still needs the deploy.yaml file in the FP32 model folder. Self-quantized model folder does not contain this yaml file, you can copy it from the FP32 model folder to the quantized model folder.)
Take the Quantized PP_LiteSeg_T_STDC1_cityscapes Model as an example for Deployment
Run the following commands in this directory to compile and deploy the quantized model. FastDeploy version 0.7.0 or higher is required (x.x.x>=0.7.0).
mkdir build
cd build
# Download pre-compiled FastDeploy libraries. You can choose the appropriate version from `pre-compiled FastDeploy libraries` mentioned above.
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# Download the PP_LiteSeg_T_STDC1_cityscapes quantized model and test images provided by FastDeloy.
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ.tar
tar -xvf PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ.tar
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
# Use Paddle-Inference inference quantization model on CPU.
./infer_demo PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_PTQ cityscapes_demo.png 1