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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](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)  
- 2.  Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)

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

```shell
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](../../../../../docs/en/faq/use_sdk_on_windows.md)

## 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.

```shell
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

```shell
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

```c#
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

```c#
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](../../../../../docs/api/vision_results/) for the description of ClassifyResult

- [Model Description](../../)
- [Python Deployment](../python)
- [Vision Model prediction results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)