FastDeploy/examples/vision/classification/paddleclas/c

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PaddleClas C Deployment Example

This directory provides examples that infer.c fast finishes the deployment of PaddleClas models on CPU/GPU.

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 1.0.4 or above (x.x.x>=1.0.4) 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

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

RuntimeOption

FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()

Create a RuntimeOption object, and return a pointer to manipulate it.

Return

• fd_c_runtime_option_wrapper(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.

void FD_C_RuntimeOptionWrapperUseCpu(
     FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)

Enable Cpu inference.

Params

• fd_c_runtime_option_wrapper(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.

void FD_C_RuntimeOptionWrapperUseGpu(
    FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
    int gpu_id)

开启GPU推理

参数

• fd_c_runtime_option_wrapper(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
• gpu_id(int): gpu id

Model

FD_C_PaddleClasModelWrapper* FD_C_CreatePaddleClasModelWrapper(
    const char* model_file, const char* params_file, const char* config_file,
    FD_C_RuntimeOptionWrapper* runtime_option,
    const FD_C_ModelFormat model_format)

Create a PaddleClas model object, and return a pointer to manipulate it.

Params

• model_file(const char*): Model file path
• params_file(const char*): Parameter file path
• config_file(const char*): Configuration file path, which is the deployment yaml file exported by PaddleClas.
• runtime_option(FD_C_RuntimeOptionWrapper*): Backend inference configuration. None by default, which is the default configuration
• model_format(FD_C_ModelFormat): Model format. FD_C_ModelFormat_PADDLE format by default

Return

• fd_c_ppclas_wrapper(FD_C_PaddleClasModelWrapper*): Pointer to manipulate PaddleClas object.

Read and write image

FD_C_Mat FD_C_Imread(const char* imgpath)

Read an image, and return a pointer to cv::Mat.

Params

• imgpath(const char*): image path

Return

• imgmat(FD_C_Mat): pointer to cv::Mat object which holds the image.

FD_C_Bool FD_C_Imwrite(const char* savepath,  FD_C_Mat img);

Write image to a file.

Params

• savepath(const char*): save path
• img(FD_C_Mat): pointer to cv::Mat object

Return

• result(FD_C_Bool): bool to indicate success or failure

Prediction

FD_C_Bool FD_C_PaddleClasModelWrapperPredict(
    __fd_take FD_C_PaddleClasModelWrapper* fd_c_ppclas_wrapper, FD_C_Mat img,
    FD_C_ClassifyResult* fd_c_ppclas_result)

Predict an image, and generate classification result.

Params

• fd_c_ppclas_wrapper(FD_C_PaddleClasModelWrapper*): pointer to manipulate PaddleClas object
• img（FD_C_Mat）: pointer to cv::Mat object, which can be obained by FD_C_Imread interface
• fd_c_ppclas_result (FD_C_ClassifyResult*): The classification result, including label_id, and the corresponding confidence. Refer to Visual Model Prediction Results for the description of ClassifyResult

Result

void FD_C_ClassifyResultStr(
    FD_C_ClassifyResult* fd_c_classify_result,
    char* str_buffer);

print result

Params

• fd_c_classify_result(FD_C_ClassifyResult*): pointer to FD_C_ClassifyResult structure
• str_buffer(char*): used to store result string

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