Sdcb.PaddleInference
2.5.0.1
Prefix Reserved
dotnet add package Sdcb.PaddleInference --version 2.5.0.1
NuGet\Install-Package Sdcb.PaddleInference -Version 2.5.0.1
<PackageReference Include="Sdcb.PaddleInference" Version="2.5.0.1" />
paket add Sdcb.PaddleInference --version 2.5.0.1
#r "nuget: Sdcb.PaddleInference, 2.5.0.1"
// Install Sdcb.PaddleInference as a Cake Addin #addin nuget:?package=Sdcb.PaddleInference&version=2.5.0.1 // Install Sdcb.PaddleInference as a Cake Tool #tool nuget:?package=Sdcb.PaddleInference&version=2.5.0.1
PaddleSharp 🌟
💗 .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
). - Paddle2Onnx 🔄 Allow user export
ONNX
model usingC#
.
NuGet Packages/Docker Images 📦
Release notes 📝
Please checkout this page 📄.
Infrastructure packages 🏗️
NuGet Package 💼 | Version 📌 | Description 📚 |
---|---|---|
Sdcb.PaddleInference | Paddle Inference C API .NET binding ⚙️ |
Native packages 🏗️
Any other packages that starts with Sdcb.PaddleInference.runtime
might deprecated.
Baidu packages were downloaded from here: https://www.paddlepaddle.org.cn/inference/master/guides/install/download_lib.html#windows
My Sdcb packages were self compiled.
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:
- https://github.com/PaddlePaddle/Paddle/issues/43874 ❌
- https://github.com/NuGet/Home/issues/11706#issuecomment-1167305006 ❌
But You're good to build your own GPU nuget package using 01-build-native.linq
🛠️.
Note: Linux does not need a native binding NuGet
package like windows(Sdcb.PaddleInference.runtime.win64.mkl
), instead, you can/should based from a Dockerfile🐳 to development:
Paddle Devices
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.TensorRT -
PaddleDevice.Gpu().And(PaddleDevice.TensorRt("shape-info.txt"))
Even faster than raw Gpu but need install TensorRT environment.
Please refer to tensorrt section for more details
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 haveVisual 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: 💾- api-ms-win-core-libraryloader-l1-2-0.dll
- api-ms-win-core-processtopology-obsolete-l1-1-0.dll
- API-MS-Win-Eventing-Provider-L1-1-0.dll
You can download these 3 DLLs here: win7-x64-onnxruntime-missing-dlls.zip ⬇️
How to enable GPU? 🎮
Enable GPU support can significantly improve the throughput and lower the CPU usage. 🚀
Steps to use GPU in Windows:
- (for Windows) Install the package:
Sdcb.PaddleInference.runtime.win64.cuda*
instead ofSdcb.PaddleInference.runtime.win64.mkl
, do not install both. 📦 - Install CUDA from NVIDIA, and configure environment variables to
PATH
orLD_LIBRARY_PATH
(Linux) 🔧 - Install cuDNN from NVIDIA, and configure environment variables to
PATH
orLD_LIBRARY_PATH
(Linux) 🛠️ - Install TensorRT from NVIDIA, and configure environment variables to
PATH
orLD_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! 🎉
TensorRT 🚄
To use TensorRT, just specify PaddleDevice.Gpu().And(PaddleDevice.TensorRt("shape-info.txt"))
instead of PaddleDevice.Gpu()
to make it work. 💡
Please be aware, this shape info text file **.txt
is bound to your model. Different models have different shape info, so if you're using a complex model like Sdcb.PaddleOCR
, you should use different shapes for different models like this:
using PaddleOcrAll all = new(model,
PaddleDevice.Gpu().And(PaddleDevice.TensorRt("det.txt")),
PaddleDevice.Gpu().And(PaddleDevice.TensorRt("cls.txt")),
PaddleDevice.Gpu().And(PaddleDevice.TensorRt("rec.txt")))
{
Enable180Classification = true,
AllowRotateDetection = true,
};
In this case:
DetectionModel
will usedet.txt
🔍180DegreeClassificationModel
will usecls.txt
🔃RecognitionModel
will userec.txt
🔡
NOTE 📝:
The first round of TensorRT
running will generate a shape info **.txt
file in this folder: %AppData%\Sdcb.PaddleInference\TensorRtCache
. It will take around 100 seconds to finish TensorRT cache generation. After that, it should be faster than the general GPU
. 🚀
In this case, if something strange happens (for example, you mistakenly create the same shape-info.txt
file for different models), you can delete this folder to generate TensorRT cache again: %AppData%\Sdcb.PaddleInference\TensorRtCache
. 🗑️
Thanks & Sponsors 🙏
- 崔亮 https://github.com/cuiliang
- 梁现伟
- 深圳-钱文松
- iNeuOS工业互联网操作系统:http://www.ineuos.net
Contact 📞
QQ group of C#/.NET computer vision technical communication (C#/.NET计算机视觉技术交流群): 579060605
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net5.0 was computed. net5.0-windows was computed. net6.0 is compatible. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
.NET Core | netcoreapp2.0 was computed. netcoreapp2.1 was computed. netcoreapp2.2 was computed. netcoreapp3.0 was computed. netcoreapp3.1 was computed. |
.NET Standard | netstandard2.0 is compatible. netstandard2.1 was computed. |
.NET Framework | net45 is compatible. net451 was computed. net452 was computed. net46 was computed. net461 was computed. net462 was computed. net463 was computed. net47 was computed. net471 was computed. net472 was computed. net48 was computed. net481 was computed. |
MonoAndroid | monoandroid was computed. |
MonoMac | monomac was computed. |
MonoTouch | monotouch was computed. |
Tizen | tizen40 was computed. tizen60 was computed. |
Xamarin.iOS | xamarinios was computed. |
Xamarin.Mac | xamarinmac was computed. |
Xamarin.TVOS | xamarintvos was computed. |
Xamarin.WatchOS | xamarinwatchos was computed. |
-
.NETFramework 4.5
- No dependencies.
-
.NETStandard 2.0
- System.Text.Encoding.CodePages (>= 6.0.0)
-
net6.0
- No dependencies.
NuGet packages (10)
Showing the top 5 NuGet packages that depend on Sdcb.PaddleInference:
Package | Downloads |
---|---|
Sdcb.PaddleOCR
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition) |
|
HHO.LV.OCR
Library packed for OCR |
|
Wlkr.SafePaddleOCR
基于PaddleSharp.PaddleOCR设计的线程安全模板,示例: SafePaddleOCR safePaddleOCR = new SafePaddleOCR(); string imgPath = @"DimTechStudio-Logo.png"; var res = safePaddleOCR.Run(imgPath); Console.WriteLine($"res: {res.data.Text}"); |
|
Sdcb.PaddleDetection
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection. |
|
BotSharp.Plugin.PaddleSharp
Package Description |
GitHub repositories (3)
Showing the top 3 popular GitHub repositories that depend on Sdcb.PaddleInference:
Repository | Stars |
---|---|
babalae/better-genshin-impact
📦BetterGI · 更好的原神 - 自动拾取 | 自动剧情 | 全自动钓鱼(AI) | 全自动七圣召唤 | 自动伐木 | 自动刷本 | 自动采集/挖矿/锄地 | 一条龙 - UI Automation Testing Tools For Genshin Impact
|
|
SciSharp/BotSharp
AI Multi-Agent Framework in .NET
|
|
sdcb/PaddleSharp
.NET/C# binding for Baidu paddle inference library and PaddleOCR
|
Version | Downloads | Last updated |
---|---|---|
2.5.0.1 | 19,297 | 8/5/2023 |
2.5.0 | 897 | 8/4/2023 |
2.5.0-preview.3 | 219 | 7/10/2023 |
2.5.0-preview.1 | 136 | 7/6/2023 |
2.4.1.4 | 1,989 | 6/30/2023 |
2.4.1.3 | 3,936 | 6/17/2023 |
2.4.1.3-preview.3 | 92 | 6/16/2023 |
2.4.1.2 | 1,244 | 5/3/2023 |
2.4.1.1 | 827 | 3/31/2023 |
2.4.0 | 3,431 | 12/8/2022 |
2.4.0-rc.2 | 104 | 12/7/2022 |
2.3.2 | 3,111 | 9/10/2022 |
2.3.1 | 3,953 | 8/1/2022 |
2.3.0 | 1,542 | 6/27/2022 |
2.2.2 | 2,705 | 2/18/2022 |