FastDeploy/examples/vision/segmentation/paddleseg/semantic_segmentation/cpu-gpu/csharp

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PaddleSeg C# Deployment Example

This directory provides infer.cs to finish the deployment of PaddleSeg 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/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz # (Decompress it) https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png

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\segmentation\paddleseg\cpu-gpu\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\segmentation\paddleseg\cpu-gpu\csharp\build\Release

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

cd Release
# CPU inference
infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 0
# GPU inference
infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 1

PaddleSeg C# Interface

Model Class

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

PaddleSeg initialization

Params

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

Predict Function

fastdeploy.SegmentationResult 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: Segmentation prediction results, refer to Vision Model Prediction Results for SegmentationResult

Other Documents

• PPSegmentation Model Description
• PaddleSeg Python Deployment
• Model Prediction Results
• How to switch the model inference backend engine