【TensorRT】TensorRT C# API 项目介绍：基于C#与TensorRT部署深度学习模型（下篇）_c# 调用 深度学习模型

4. 接口调用

4.1 创建并配置C#项目

  首先创建一个简单的C#项目，然后添加项目配置。

  首先是添加TensorRT C# API 项目引用，如下图所示，添加上文中C#项目生成的dll文件即可。

  接下来添加OpenCvSharp，此处通过NuGet Package安装即可，此处主要安装以下两个程序包即可：

  配置好项目后，项目的配置文件如下所示：

<Project Sdk="Microsoft.NET.Sdk">

  <PropertyGroup>
    <OutputType>Exe</OutputType>
    <TargetFramework>net6.0</TargetFramework>
    <RootNamespace>TensorRT_CSharp_API_demo</RootNamespace>
    <ImplicitUsings>enable</ImplicitUsings>
    <Nullable>enable</Nullable>
  </PropertyGroup>

  <ItemGroup>
    <PackageReference Include="OpenCvSharp4.Extensions" Version="4.9.0.20240103" />
    <PackageReference Include="OpenCvSharp4.Windows" Version="4.9.0.20240103" />
  </ItemGroup>

  <ItemGroup>
    <Reference Include="TensorRtSharp">
      <HintPath>E:\GitSpace\TensorRT-CSharp-API\src\TensorRtSharp\bin\Release\net6.0\TensorRtSharp.dll</HintPath>
    </Reference>
  </ItemGroup>

</Project>
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4.2 添加推理代码

  此处演示一个简单的图像分类项目，以Yolov8-cls项目为例：

static void Main(string[] args)
{
    Nvinfer predictor = new Nvinfer("E:\\Model\\yolov8\\yolov8s-cls_2.engine");
    Dims InputDims = predictor.GetBindingDimensions("images");
    int BatchNum = InputDims.d[0];

    Mat image1 = Cv2.ImRead("E:\\ModelData\\image\\demo_4.jpg");
    Mat image2 = Cv2.ImRead("E:\\ModelData\\image\\demo_5.jpg");

    List<Mat> images = new List<Mat>() { image1, image2 };
    for (int begImgNo = 0; begImgNo < images.Count; begImgNo += BatchNum)
    {
        DateTime start = DateTime.Now;
        int endImgNo = Math.Min(images.Count, begImgNo + BatchNum);
        int batchNum = endImgNo - begImgNo;
        List<Mat> normImgBatch = new List<Mat>();
        int imageLen = 3 * 224 * 224;
        float[] inputData = new float[2 * imageLen];
        for (int ino = begImgNo; ino < endImgNo; ino++)
        {
            Mat input_mat = CvDnn.BlobFromImage(images[ino], 1.0 / 255.0, new OpenCvSharp.Size(224, 224), 0, true, false);
            float[] data = new float[imageLen];
            Marshal.Copy(input_mat.Ptr(0), data, 0, imageLen);
            Array.Copy(data, 0, inputData, ino * imageLen, imageLen);
        }
        predictor.LoadInferenceData("images", inputData);

        DateTime end = DateTime.Now;
        Console.WriteLine("[ INFO ] Input image data processing time: " + (end - start).TotalMilliseconds + " ms.");
        predictor.infer();
        start = DateTime.Now;
        predictor.infer();
        end = DateTime.Now;
        Console.WriteLine("[ INFO ] Model inference time: " + (end - start).TotalMilliseconds + " ms.");
        start = DateTime.Now;

        float[] outputData = predictor.GetInferenceResult("output0");
        for (int i = 0; i < batchNum; ++i)
        {
            Console.WriteLine(string.Format("\n[ INFO ] Classification Top {0} result : \n", 10));
            Console.WriteLine("[ INFO ] classid probability");
            Console.WriteLine("[ INFO ] ------- -----------");
            float[] data = new float[1000];
            Array.Copy(outputData, i * 1000, data, 0, 1000);
            List<int> sortResult = Argsort(new List<float>(data));
            for (int j = 0; j < 10; ++j)
            {
                string msg = "";
                msg += ("index: " + sortResult[j] + "\t");
                msg += ("score: " + data[sortResult[j]] + "\t");
                Console.WriteLine("[ INFO ] " + msg);
            }
        }
        end = DateTime.Now;
        Console.WriteLine("[ INFO ] Inference result processing time: " + (end - start).TotalMilliseconds + " ms.");
    }


}

public static List<int> Argsort(List<float> array)
{
    int arrayLen = array.Count;
    List<float[]> newArray = new List<float[]> { };
    for (int i = 0; i < arrayLen; i++)
    {
        newArray.Add(new float[] { array[i], i });
    }
    newArray.Sort((a, b) => b[0].CompareTo(a[0]));
    List<int> arrayIndex = new List<int>();
    foreach (float[] item in newArray)
    {
        arrayIndex.Add((int)item[1]);
    }
    return arrayIndex;
}
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4.3 项目演示

  配置好项目并编写好代码后，运行该项目，项目输出如下所示：

[03/31/2024-22:27:44] [I] [TRT] Loaded engine size: 15 MiB
[03/31/2024-22:27:44] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in engine deserialization: CPU +0, GPU +12, now: CPU 0, GPU 12 (MiB)
[03/31/2024-22:27:44] [I] [TRT] [MemUsageChange] TensorRT-managed allocation in IExecutionContext creation: CPU +0, GPU +4, now: CPU 0, GPU 16 (MiB)
[03/31/2024-22:27:44] [W] [TRT] CUDA lazy loading is not enabled. Enabling it can significantly reduce device memory usage and speed up TensorRT initialization. See "Lazy Loading" section of CUDA documentation https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#lazy-loading
[ INFO ] Input image data processing time: 6.6193 ms.
[ INFO ] Model inference time: 1.1434 ms.

[ INFO ] Classification Top 10 result :

[ INFO ] classid probability
[ INFO ] ------- -----------
[ INFO ] index: 386     score: 0.87328124
[ INFO ] index: 385     score: 0.082506955
[ INFO ] index: 101     score: 0.04416279
[ INFO ] index: 51      score: 3.5818E-05
[ INFO ] index: 48      score: 4.2115275E-06
[ INFO ] index: 354     score: 3.5188648E-06
[ INFO ] index: 474     score: 5.789438E-07
[ INFO ] index: 490     score: 5.655325E-07
[ INFO ] index: 343     score: 5.1091644E-07
[ INFO ] index: 340     score: 4.837259E-07

[ INFO ] Classification Top 10 result :

[ INFO ] classid probability
[ INFO ] ------- -----------
[ INFO ] index: 293     score: 0.89423335
[ INFO ] index: 276     score: 0.052870292
[ INFO ] index: 288     score: 0.021361532
[ INFO ] index: 290     score: 0.009259541
[ INFO ] index: 275     score: 0.0066174944
[ INFO ] index: 355     score: 0.0025512716
[ INFO ] index: 287     score: 0.0024535337
[ INFO ] index: 210     score: 0.00083151844
[ INFO ] index: 184     score: 0.0006893527
[ INFO ] index: 272     score: 0.00054959994
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  通过上面输出可以看出，模型推理仅需1.1434ms，大大提升了模型的推理速度。

5. 总结

  在本项目中，我们开发了TensorRT C# API 2.0版本，重新封装了推理接口。并结合分类模型部署流程向大家展示了TensorRT C# API 的使用方式，方便大家快速上手使用。

  为了方便各位开发者使用，此处开发了配套的演示项目，主要是基于Yolov8开发的目标检测、目标分割、人体关键点识别、图像分类以及旋转目标识别，由于时间原因，还未开发配套的技术文档，此处先行提供给大家项目源码，大家可以根据自己需求使用：

• Yolov8 Det 目标检测项目源码：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom/Yolov8Det.cs
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• Yolov8 Seg 目标分割项目源码：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom/Yolov8Seg.cs
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• Yolov8 Pose 人体关键点识别项目源码：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom/Yolov8Pose.cs
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• Yolov8 Cls 图像分类项目源码：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom/Yolov8Cls.cs
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• Yolov8 Obb 旋转目标识别项目源码：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom/Yolov8Obb.cs
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  同时对本项目开发的案例进行了时间测试，以下时间只是程序运行一次的时间，测试环境为：

• CPU：i7-165G7

• CUDA型号：12.2

• Cudnn：8.9.3

• TensorRT：8.6.1.6

  最后如果各位开发者在使用中有任何问题，欢迎大家与我联系。