💗 .NET Wrapper for PaddleInference C API, support Windows(x64) 💻, NVIDIA Cuda 10.2+ based GPU 🎮 and Linux(Ubuntu-22.04 x64) 🐧, currently contained following main components:
PaddleOCR 📖 support 14 OCR languages model download on-demand, allow rotated text angle detection, 180 degree text detection, also support table recognition 📊.
PaddleDetection 🎯 support PPYolo detection model and PicoDet model 🏹.
RotationDetection 🔄 use Baidu's official text_image_orientation_infer model to detect text picture's rotation angle(0, 90, 180, 270).
We recommend 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.
The remaining packages cover various CUDA combinations (GPU acceleration), supporting three CUDA versions:
CUDA 11.8: Supports 10–40 series NVIDIA GPUs
CUDA 12.6: Supports 10–40 series NVIDIA GPUs
CUDA 12.9: Supports 10–50 series NVIDIA GPUs
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)
Linux OS packages(preview):
Package
Version 📌
Description
Sdcb.PaddleInference.runtime.linux-loongarch64
Loongnix GCC 8.2 Loongarch64
Sdcb.PaddleInference.runtime.linux64.mkl.gcc82
Linux-x64 GCC 8.2(tested in Ubuntu 22.04)
Be aware, as the Linux operating system cannot modify the value of LD_LIBRARY_PATH at runtime. If dependent dynamic libraries (such as libcommon.so) are loaded before the main dynamic library (such as libpaddle_inference_c.so), and also due to protobuf errors reported: https://github.com/PaddlePaddle/Paddle/issues/62670
Therefore, all NuGet packages for Linux operating systems are in a preview state, and I'm unable to resolve this issue. Currently, if you are using the NuGet package on Linux, you need to manually specify the LD_LIBRARY_PATH environment variable before running the program, using the following commands:
For x64 CPUs:
export LD_LIBRARY_PATH=/<program directory>/bin/Debug/net8.0/runtimes/linux-x64/native:$LD_LIBRARY_PATH
For Loongson 5000 or above CPUs (linux-loongarch64):
export LD_LIBRARY_PATH=/<program directory>/bin/Debug/net8.0/runtimes/linux-loongarch64/native:$LD_LIBRARY_PATH
Some of packages already deprecated(Version <= 2.5.0):
All Windows packages were compiled manually by me.
Baidu official GPU packages are too large(>1.5GB) to publish to nuget.org, there is a limitation of 250MB when upload to Github, there is some related issues to this:
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.
FAQ ❓
Why my code runs good in my windows machine, but DllNotFoundException in other machine: 💻
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: <img width="830" alt="image" src="https://user-images.githubusercontent.com/1317141/193706883-6a71ea83-65d9-448b-afee-2d25660430a1.png">
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: 💾
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! 🎉
