JYPPX.DeploySharp 0.0.4.2

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简体中文| English

📚 Introduction

  DeploySharp is a cross-platform model deployment framework designed for C# developers, offering end-to-end solutions from model loading and configuration management to inference execution. Its modular namespace architecture significantly reduces the complexity of integrating deep learning models into the C# ecosystem.

1. Architecture & Layered Design

• Root namespace DeploySharp serves as a unified entry point for core features (model loading, inference, etc.).
• Modular sub-namespaces (e.g., DeploySharp.Engine) enable clear functional layers.
• Generic class designs support standard data interfaces for tasks like image processing/classification/detection.

2. Multi-Engine Support

• Native integration with OpenVINO (OpenVinoSharp) and ONNX Runtime.
• Compatibility with YOLOv5-v12 models, Anomalib, and other mainstream architectures.

3. Cross-Platform Runtime

• Supports .NET Framework 4.8+ and .NET 6/7/8/9.
• Deep integration with .NET NuGet ecosystem.

4. High-Performance Inference

• Asynchronous operations (System.Threading.Tasks).
• Batch/single-image inference modes.
• Rich pre-/post-processing (ImageSharp/OpenCvSharp).

5. Developer Support

• Bilingual (EN/CN) code comments and documentation.
• log4net logging (error/warning/debug levels).
• Visualization tools and comprehensive code samples.

Licensed under Apache License 2.0. Future updates will expand TensorRT support and optimize heterogenous computing.

🎨Supported Models

<img title="NuGet" src="https://s2.loli.net/2023/08/08/jE6BHu59L4WXQFg.png" alt="" width="40">NuGet Package

Core Managed Libraries

Package	Description	Link
JYPPX.DeploySharp	DeploySharp API core libraries

Native Runtime Libraries

Package	Description	Link
JYPPX.DeploySharp.ImageSharp	An assembly that uses ImageSharp as an image processing tool.
JYPPX.DeploySharp.OpenCvSharp	An assembly that uses OpenCvSharp as an image processing tool.

⚙ Installation

  DeploySharp includes image processing methods such as OpenCvSharp and ImageSharp, as well as support for OpenVINO and ONNX Runtime model deployment engines. Therefore, users can combine them according to their own needs and install the corresponding VNet Package to use them out of the box. The following summarizes some commonly used scenarios for installing VNet Package:

• OpenVINO inference+OpenCvSharp image processing

JYPPX.DeploySharp
JYPPX.DeploySharp.OpenCvSharp

OpenVINO.runtime.win
OpenCvSharp4.runtime.win 

• OpenVINO inference+ImageSharp image processing

JYPPX.DeploySharp
JYPPX.DeploySharp.ImageSharp

OpenVINO.runtime.win

• ONNX Runtime inference+OpenCvSharp image processing

JYPPX.DeploySharp
JYPPX.DeploySharp.OpenCvSharp

OpenCvSharp4.runtime.win 

• **ONNX Runtime inference+ImageSharp image processing **

JYPPX.DeploySharp
JYPPX.DeploySharp.OpenCvSharp

• ONNX Runtime(OpenVINO) inference+ImageSharp image processing

JYPPX.DeploySharp
JYPPX.DeploySharp.ImageSharp

Intel.ML.OnnxRuntime.OpenVino

• ONNX Runtime(DML) inference+ImageSharp image processing

JYPPX.DeploySharp
JYPPX.DeploySharp.ImageSharp

Microsoft.ML.OnnxRuntime.DirectML

• ONNX Runtime(CUDA) inference+ImageSharp image processing

JYPPX.DeploySharp
JYPPX.DeploySharp.ImageSharp

Microsoft.ML.OnnxRuntime.DirectML

  Due to the influence of GPU device model and software version on using CUDA to accelerate ONNX Runtime, it is necessary to download and use according to the official version correspondence provided by ONNX Runtime. Please refer to the following link for the correspondence between ONNX Runtime, CUDA, and cuDNN:

https://runtime.onnx.org.cn/docs/execution-providers/CUDA-ExecutionProvider.html#requirements

  The usage methods listed above can all be installed with just one click through the VNet Package. Similarly, ONNX Runtime also supports more acceleration methods, but users need to build their own code. For the construction process and method, please refer to the official tutorial. The link is:

https://runtime.onnx.org.cn/docs/execution-providers/

🏷 Quick Start

  If you don't know how to use it, use the following code to briefly understand how to use it.

ImageSharp

using DeploySharp.Data;
using DeploySharp.Engine;
using DeploySharp.Model;
using SixLabors.ImageSharp;
using SixLabors.ImageSharp.PixelFormats;
using System;

namespace DeploySharp.ImageSharp.Demo
{
    public class YOLOv5DetDemo
    {
        public static void Run()
        {
			//The model and test images can be downloaded from the QQ group (945057948)
			//Replace the following model path with your own model path
            string modelPath = @"E:\Model\Yolo\yolov5s.onnx";
            //Replace the image path below with your own image path
            string imagePath = @"E:\Data\image\bus.jpg";
            Yolov5DetConfig config = new Yolov5DetConfig(modelPath);
            //config.SetTargetInferenceBackend(InferenceBackend.OnnxRuntime);
            Yolov5DetModel model = new Yolov5DetModel(config);
            var img = Image.Load(imagePath);
            var result = model.Predict(img);
            model.ModelInferenceProfiler.PrintAllRecords();
            var resultImg = Visualize.DrawDetResult(result, img as Image<Rgb24>, new VisualizeOptions(1.0f));
            resultImg.Save(@$"./result_{ModelType.YOLOv5Det.ToString()}.jpg");
        }
    }
}

OpenCvSharp

using OpenCvSharp;
using System.Diagnostics;
using DeploySharp.Model;
using DeploySharp.Data;
using DeploySharp.Engine;
using DeploySharp;
using System.Net.Http.Headers;

namespace DeploySharp.OpenCvSharp.Demo
{
    public class YOLOv5DetDemo
    {
        public static void Run()
        {
			//The model and test images can be downloaded from the QQ group (945057948)
			//Replace the following model path with your own model path
            string modelPath = @"E:\Model\Yolo\yolov5s.onnx";
            //Replace the image path below with your own image path
            string imagePath = @"E:\Data\image\bus.jpg";
            Yolov5DetConfig config = new Yolov5DetConfig(modelPath);
            config.SetTargetInferenceBackend(InferenceBackend.OnnxRuntime);
            Yolov5DetModel model = new Yolov5DetModel(config);
            Mat img = Cv2.ImRead(imagePath);
            var result = model.Predict(img);
            model.ModelInferenceProfiler.PrintAllRecords();
            var resultImg = Visualize.DrawDetResult(result, img, new VisualizeOptions(1.0f));
            Cv2.ImShow("image", resultImg);
            Cv2.WaitKey();
        }
    }
}

💻 Use Cases

  For more application cases, please refer to:

🗂Documentation

  Explore the full API: DeploySharp API Documented

🎖 Contribution

  If you are interested in using Deploy Sharp in C # and are interested in contributing to the open source community, please join us to develop Deploy Sharp together.

  If you have any ideas or improvement strategies for this project, please feel free to contact us for guidance on our work.

<img title="" src="https://user-images.githubusercontent.com/48054808/157835345-f5d24128-abaf-4813-b793-d2e5bdc70e5a.png" alt="" width="40"> License

  The release of this project is certified under the Apache 2.0 license.

  Finally, if any developers have any questions during use, please feel free to contact me.