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100 lines
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Markdown
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100 lines
4.0 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PaddleClas C# Deployment Example
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This directory provides example `infer.cs` to fastly finish the deployment of PaddleClas models on CPU/GPU.
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 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)
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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.
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## 1. Download C# package management tool nuget client
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> https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe
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Add nuget program into system variable **PATH**
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## 2. Download model and image for test
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> https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz # (下载后解压缩)
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> https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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## 3. Compile example code
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Open `x64 Native Tools Command Prompt for VS 2019` command tool on Windows, cd to the demo path of ppyoloe and execute commands
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```shell
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cd D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\classification\paddleclas\csharp
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mkdir build && cd build
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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"
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nuget restore
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msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
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```
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For more information about how to use FastDeploy SDK to compile a project with Visual Studio 2019. Please refer to
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- [Using the FastDeploy C++ SDK on Windows Platform](../../../../../docs/en/faq/use_sdk_on_windows.md)
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## 4. Execute compiled program
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fastdeploy.dll and related dynamic libraries are required by the program. FastDeploy provide a script to copy all required dll to your program path.
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```shell
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cd D:\Download\fastdeploy-win-x64-gpu-x.x.x
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fastdeploy_init.bat install %cd% D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\classification\paddleclas\csharp\build\Release
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```
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Then you can run your program and test the model with image
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```shell
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cd Release
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# CPU inference
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infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
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# GPU inference
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infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
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```
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## PaddleClas C# Interface
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### Model Class
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```c#
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fastdeploy.vision.classification.PaddleClasModel(
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string model_file,
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string params_file,
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string config_file
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fastdeploy.RuntimeOption runtime_option = null,
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fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
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```
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> PaddleClasModel initilization.
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> **Params**
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>> * **model_file**(str): Model file path
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>> * **params_file**(str): Parameter file path
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>> * **config_file**(str): Configuration file path, which is the deployment yaml file exported by PaddleClas
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>> * **runtime_option**(RuntimeOption): Backend inference configuration. null by default, which is the default configuration
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>> * **model_format**(ModelFormat): Model format. Paddle format by default
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#### Predict Function
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```c#
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fastdeploy.ClassifyResult Predict(OpenCvSharp.Mat im)
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```
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> Model prediction interface. Input images and output results directly.
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>
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> **Params**
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>
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>> * **im**(Mat): Input images in HWC or BGR format
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>
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> **Return**
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>
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>> * **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
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- [Model Description](../../)
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- [Python Deployment](../python)
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- [Vision Model prediction results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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