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2.4 KiB
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2.4 KiB
Executable File
English | 简体中文
PaddleClas Quantitative Model C++ Deployment Example
infer.cc in this directory can help you quickly complete the inference acceleration of PaddleClas 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 inference_cls.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-YOLOE-l Model as an example for Deployment, FastDeploy version 0.7.0 or higher is required (x.x.x>=0.7.0)
Run the following commands in this directory to compile and deploy the quantized model.
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 ResNet50_Vd quantized model and test images provided by FastDeloy.
wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar
tar -xvf resnet50_vd_ptq.tar
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
# Use ONNX Runtime inference quantization model on CPU.
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 0
# Use TensorRT inference quantization model on GPU.
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 1
# Use Paddle-TensorRT inference quantization model on GPU.
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 2