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English | 简体中文
PaddleSeg Quantitative Model Python Deployment Example
infer.py in this directory can help you quickly complete the inference acceleration of PaddleSeg quantization model deployment on CPU/GPU.
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
# Download sample deployment code.
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/segmentation/paddleseg/quantize/python
# 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.
python infer.py --model PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer_QAT --image cityscapes_demo.png --device cpu --backend paddle