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105 lines
3.9 KiB
Markdown
Executable File
105 lines
3.9 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PaddleSeg C# Deployment Example
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This directory provides `infer.cs` to finish the deployment of PaddleSeg 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/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz # (Decompress it)
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> https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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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\segmentation\paddleseg\cpu-gpu\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\segmentation\paddleseg\cpu-gpu\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 PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 0
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# GPU inference
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infer_demo PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer cityscapes_demo.png 1
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```
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## PaddleSeg C# Interface
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### Model Class
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```c#
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fastdeploy.vision.segmentation.PaddleSeg(
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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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> PaddleSeg initialization
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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): Config file path
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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.
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#### Predict Function
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```c#
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fastdeploy.SegmentationResult 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**: Segmentation prediction results, refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for SegmentationResult
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## Other Documents
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- [PPSegmentation Model Description](../../)
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- [PaddleSeg Python Deployment](../python)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)
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