FastDeploy/examples/vision/classification/paddleclas/cpp/README.md

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PaddleClas C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of PaddleClas models on CPU/GPU and GPU accelerated by TensorRT.

Before deployment, two steps require confirmation.

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements.
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library.

Taking ResNet50_vd inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

mkdir build
cd build
# Download FastDeploy precompiled library. Users can choose your appropriate version in the`FastDeploy Precompiled Library` 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 ResNet50_vd model file and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg


# CPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
# GPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
# TensorRT inference on GPU
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2
# IPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 3
# KunlunXin XPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 4

The above command works for Linux or MacOS. Refer to

• How to use FastDeploy C++ SDK in Windows for SDK use-pattern in Windows

PaddleClas C++ Interface

PaddleClas Class

fastdeploy::vision::classification::PaddleClasModel(
        const string& model_file,
        const string& params_file,
        const string& config_file,
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::PADDLE)

PaddleClas model loading and initialization, where model_file and params_file are the Paddle inference files exported from the training model. Refer to Model Export for more information

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path
• config_file(str): Inference deployment configuration file
• runtime_option(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
• model_format(ModelFormat): Model format. Paddle format by default

Predict function

PaddleClasModel::Predict(cv::Mat* im, ClassifyResult* result, int topk = 1)

Model prediction interface. Input images and output results directly.

Parameter

• im: Input images in HWC or BGR format
• result: The classification result, including label_id, and the corresponding confidence. Refer to Visual Model Prediction Results for the description of ClassifyResult
• topk(int): Return the topk classification results with the highest prediction probability. Default 1

• Model Description
• Python Deployment
• Visual Model prediction results
• How to switch the model inference backend engine