FastDeploy/examples/vision/classification/paddleclas/csharp

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PaddleClas C# Deployment Example

This directory provides example infer.cs to fastly finish 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

Please follow below instructions to compile and test in Windows. FastDeploy version 1.0.4 or above (x.x.x>=1.0.4) is required to support this model.

1. Download C# package management tool nuget client

https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe

Add nuget program into system variable PATH

2. Download model and image for test

https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz # (下载后解压缩) https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg

3. Compile example code

Open x64 Native Tools Command Prompt for VS 2019 command tool on Windows, cd to the demo path of ppyoloe and execute commands

cd D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\classification\paddleclas\csharp

mkdir build && cd build
cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=D:\Download\fastdeploy-win-x64-gpu-x.x.x -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"

nuget restore
msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64

For more information about how to use FastDeploy SDK to compile a project with Visual Studio 2019. Please refer to

• Using the FastDeploy C++ SDK on Windows Platform

4. Execute compiled program

fastdeploy.dll and related dynamic libraries are required by the program. FastDeploy provide a script to copy all required dll to your program path.

cd D:\Download\fastdeploy-win-x64-gpu-x.x.x

fastdeploy_init.bat install %cd% D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\classification\paddleclas\csharp\build\Release

Then you can run your program and test the model with image

cd Release
# CPU inference
infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
# GPU inference
infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1

PaddleClas C# Interface

Model Class

fastdeploy.vision.classification.PaddleClasModel(
        string model_file,
        string params_file,
        string config_file
        fastdeploy.RuntimeOption runtime_option = null,
        fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)

PaddleClasModel initilization.

Params

• model_file(str): Model file path
• params_file(str): Parameter file path
• config_file(str): Configuration file path, which is the deployment yaml file exported by PaddleClas
• runtime_option(RuntimeOption): Backend inference configuration. null by default, which is the default configuration
• model_format(ModelFormat): Model format. Paddle format by default

Predict Function

fastdeploy.ClassifyResult Predict(OpenCvSharp.Mat im)

Model prediction interface. Input images and output results directly.

Params

• im(Mat): Input images in HWC or BGR format

Return

• result(ClassifyResult): The classification result, including label_id, and the corresponding confidence. Refer to Visual Model Prediction Results for the description of ClassifyResult

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