FastDeploy/examples/vision/classification/resnet/cpp

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ResNet C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of ResNet 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 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 the 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 the ResNet50 model file and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50.onnx
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg


# CPU inference
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 0
# GPU inference
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 1
# TensorRT Inference on GPU
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 2

The above command works for Linux or MacOS. Refer to:

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

ResNet C++ Interface

ResNet Class

fastdeploy::vision::classification::ResNet(
        const std::string& model_file,
        const std::string& params_file = "",
        const RuntimeOption& custom_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)

Parameter

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

Predict Function

ResNet::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
• Vision Model prediction results
• How to switch the model inference backend engine