.NET/C# binding for Baidu paddle inference library and PaddleOCR
.NET/C# binding for Baidu paddle inference library and PaddleOCR
.NET Wrapper for PaddleInference C API, support Windows(x64) , NVIDIA Cuda 11.8+ based GPU and Linux(Ubuntu-22.04 x64) , currently contained following main components:
text_image_orientation_infer model to detect text picture's rotation angle(0, 90, 180, 270).PaddleNLP Lac Chinese segmenter model, supports tagging/customized words.ONNX model using C#.Please checkout this page .
| NuGet Package | Version | Description |
|---|---|---|
| Sdcb.PaddleInference | Paddle Inference C API .NET binding ⚙️ |
Package Selection Guide:
Sdcb.PaddleInference.runtime.win64.mkl for most users. It offers the best balance between performance and package size. Please note that this package does not support GPU acceleration, making it suitable for most general scenarios.openblas-noavx is tailored for older CPUs that do not support the AVX2 instruction set.Important:
Not all GPU packages are suitable for every card. Please refer to the following GPU-to-sm suffix mapping:
sm Suffix |
Supported GPU Series |
|---|---|
| sm61 | GTX 10 Series |
| sm75 | RTX 20 Series (and GTX 16xx series such as GTX 1660) |
| sm86 | RTX 30 Series |
| sm89 | RTX 40 Series |
| sm120 | RTX 50 Series (supported by CUDA 12.9 only) |
Any other packages that starts with Sdcb.PaddleInference.runtime might deprecated.
All packages were compiled manually by me, with some code patches from here: https://github.com/sdcb/PaddleSharp/blob/master/build/capi.patch
Mkldnn - PaddleDevice.Mkldnn()
Based on Mkldnn, generally fast
Openblas - PaddleDevice.Openblas()
Based on openblas, slower, but dependencies file smaller and consume lesser memory
Onnx - PaddleDevice.Onnx()
Based on onnxruntime, is also pretty fast and consume less memory
Gpu - PaddleDevice.Gpu()
Much faster but relies on NVIDIA GPU and CUDA
If you wants to use GPU, you should refer to FAQ How to enable GPU? section, CUDA/cuDNN/TensorRT need to be installed manually.
Please ensure the latest Visual C++ Redistributable was installed in Windows (typically it should automatically installed if you have Visual Studio installed) ️
Otherwise, it will fail with the following error (Windows only):
DllNotFoundException: Unable to load DLL 'paddle_inference_c' or one of its dependencies (0x8007007E)If it's Unable to load DLL OpenCvSharpExtern.dll or one of its dependencies, then most likely the Media Foundation is not installed in the Windows Server 2012 R2 machine:
Many old CPUs do not support AVX instructions, please ensure your CPU supports AVX, or download the x64-noavx-openblas DLLs and disable Mkldnn: PaddleDevice.Openblas()
If you're using Win7-x64, and your CPU does support AVX2, then you might also need to extract the following 3 DLLs into C:\Windows\System32 folder to make it run:
You can download these 3 DLLs here: win7-x64-onnxruntime-missing-dlls.zip ⬇️
Enable GPU support can significantly improve the throughput and lower the CPU usage.
Steps to use GPU in Windows:
Sdcb.PaddleInference.runtime.win64.cu120* instead of Sdcb.PaddleInference.runtime.win64.mkl, do not install both. PATH or LD_LIBRARY_PATH (Linux) PATH or LD_LIBRARY_PATH (Linux) ️PATH or LD_LIBRARY_PATH (Linux) ⚙️You can refer to this blog page for GPU in Windows: 关于PaddleSharp GPU使用 常见问题记录
If you're using Linux, you need to compile your own OpenCvSharp4 environment following the docker build scripts and the CUDA/cuDNN/TensorRT configuration tasks.
After these steps are completed, you can try specifying PaddleDevice.Gpu() in the paddle device configuration parameter, then enjoy the performance boost!
QQ group of C#/.NET computer vision technical communication (C#/.NET计算机视觉技术交流群): 579060605
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