4. 接口應(yīng)用

關(guān)于該項(xiàng)目的調(diào)用方式在上一篇文章中已經(jīng)進(jìn)行了詳細(xì)介紹，具體使用可以參考《最新發(fā)布！TensorRT C# API ：基于C#與TensorRT部署深度學(xué)習(xí)模型》，下面結(jié)合Yolov8-cls模型詳細(xì)介紹一下更新的接口使用方法。

4.1 創(chuàng)建并配置C#項(xiàng)目

? 首先創(chuàng)建一個(gè)簡單的C#項(xiàng)目，然后添加項(xiàng)目配置。

? 首先是添加TensorRT C# API 項(xiàng)目引用，如下圖所示，添加上文中C#項(xiàng)目生成的dll文件即可。

? 接下來添加OpenCvSharp，此處通過NuGet Package安裝即可，此處主要安裝以下兩個(gè)程序包即可：

? 配置好項(xiàng)目后，項(xiàng)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>

4.2 添加推理代碼

? 此處演示一個(gè)簡單的圖像分類項(xiàng)目，以Yolov8-cls項(xiàng)目為例：

(1) 轉(zhuǎn)換engine模型

動(dòng)態(tài)輸入的模型在進(jìn)行格式轉(zhuǎn)換時(shí)，需要指定模型推理形狀至此的范圍，minShapes表示模型推理支持的最小形狀，optShapes表示模型推理支持的最佳形狀，maxShapes表示模型推理支持的最大形狀，模型轉(zhuǎn)換需要消耗較多時(shí)間，最終轉(zhuǎn)換成功后會(huì)在模型同級(jí)目錄下生成相同名字的.engine文件。

Dims minShapes = new Dims(1, 3, 224, 224);
Dims optShapes = new Dims(10, 3, 224, 224);
Dims maxShapes = new Dims(20, 3, 224, 224);
Nvinfer.OnnxToEngine(onnxPath, 20, "images", minShapes, optShapes, maxShapes);

(2) 定義模型預(yù)測方法

下面代碼是定義的Yolov8-cls模型的預(yù)測方法，該方法支持動(dòng)態(tài)Bath輸入模型推理，可以根據(jù)用戶輸入圖片數(shù)量，自動(dòng)設(shè)置輸入Bath，然后進(jìn)行推理。

下面代碼與上一篇文章中的代碼差異主要是增加了predictor.SetBindingDimensions("images", new Dims(batchNum, 3, 224, 224));這一句代碼。同時(shí)在初始化時(shí)，設(shè)置最大支持20Bath，這與上文模型轉(zhuǎn)換時(shí)設(shè)置的一致。

public class Yolov8Cls
{
    public Dims InputDims;
    public int BatchNum;
    private Nvinfer predictor;
    public Yolov8Cls(string enginePath)
    {
        predictor = new Nvinfer(enginePath, 20);
        InputDims = predictor.GetBindingDimensions("images");
    }
    public void Predict(List<Mat> images)
    {
        BatchNum = images.Count;
        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[BatchNum * 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.SetBindingDimensions("images", new Dims(batchNum, 3, 224, 224));
            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("[ INFO ] Classification Top {0} result : ", 2));
                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 < 2; ++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.\n");
        }
    }
    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;
    }
}

(3) 預(yù)測方法調(diào)用

下面是上述定義的預(yù)測方法，為了測試不同Bath性能，此處讀取了多張圖片，并分別預(yù)測不同張數(shù)圖片，如下所示：

Yolov8Cls yolov8Cls = new Yolov8Cls("E:\\Model\\yolov8\\yolov8s-cls_b.engine");
Mat image1 = Cv2.ImRead("E:\\ModelData\\image\\demo_4.jpg");
Mat image2 = Cv2.ImRead("E:\\ModelData\\image\\demo_5.jpg");
Mat image3 = Cv2.ImRead("E:\\ModelData\\image\\demo_6.jpg");
Mat image4 = Cv2.ImRead("E:\\ModelData\\image\\demo_7.jpg");
Mat image5 = Cv2.ImRead("E:\\ModelData\\image\\demo_8.jpg");

yolov8Cls.Predict(new List<Mat> { image1, image2 });

yolov8Cls.Predict(new List<Mat> { image1, image2, image3 });

yolov8Cls.Predict(new List<Mat> { image1, image2, image3, image4 });

yolov8Cls.Predict(new List<Mat> { image1, image2, image3, image4, image5 });

4.3 項(xiàng)目演示

? 配置好項(xiàng)目并編寫好代碼后，運(yùn)行該項(xiàng)目，項(xiàng)目輸出如下所示：

[ INFO ] Input image data processing time: 5.5277 ms.
[ INFO ] Model inference time: 1.3685 ms.
[ INFO ] Classification Top 2 result :
[ INFO ] index: 386     score: 0.8754883
[ INFO ] index: 385     score: 0.08013916
[ INFO ] Classification Top 2 result :
[ INFO ] index: 293     score: 0.89160156
[ INFO ] index: 276     score: 0.05480957
[ INFO ] Inference result processing time: 3.0823 ms.

[ INFO ] Input image data processing time: 2.7356 ms.
[ INFO ] Model inference time: 1.4435 ms.
[ INFO ] Classification Top 2 result :
[ INFO ] index: 386     score: 0.8754883
[ INFO ] index: 385     score: 0.08013916
[ INFO ] Classification Top 2 result :
[ INFO ] index: 293     score: 0.89160156
[ INFO ] index: 276     score: 0.05480957
[ INFO ] Classification Top 2 result :
[ INFO ] index: 14      score: 0.99853516
[ INFO ] index: 88      score: 0.0006980896
[ INFO ] Inference result processing time: 1.5137 ms.

[ INFO ] Input image data processing time: 3.7277 ms.
[ INFO ] Model inference time: 1.5285 ms.
[ INFO ] Classification Top 2 result :
[ INFO ] index: 386     score: 0.8754883
[ INFO ] index: 385     score: 0.08013916
[ INFO ] Classification Top 2 result :
[ INFO ] index: 293     score: 0.89160156
[ INFO ] index: 276     score: 0.05480957
[ INFO ] Classification Top 2 result :
[ INFO ] index: 14      score: 0.99853516
[ INFO ] index: 88      score: 0.0006980896
[ INFO ] Classification Top 2 result :
[ INFO ] index: 294     score: 0.96533203
[ INFO ] index: 269     score: 0.0124435425
[ INFO ] Inference result processing time: 2.7328 ms.

[ INFO ] Input image data processing time: 4.063 ms.
[ INFO ] Model inference time: 1.6947 ms.
[ INFO ] Classification Top 2 result :
[ INFO ] index: 386     score: 0.8754883
[ INFO ] index: 385     score: 0.08013916
[ INFO ] Classification Top 2 result :
[ INFO ] index: 293     score: 0.89160156
[ INFO ] index: 276     score: 0.05480957
[ INFO ] Classification Top 2 result :
[ INFO ] index: 14      score: 0.99853516
[ INFO ] index: 88      score: 0.0006980896
[ INFO ] Classification Top 2 result :
[ INFO ] index: 294     score: 0.96533203
[ INFO ] index: 269     score: 0.0124435425
[ INFO ] Classification Top 2 result :
[ INFO ] index: 127     score: 0.9008789
[ INFO ] index: 128     score: 0.07745361
[ INFO ] Inference result processing time: 3.5664 ms.

? 通過上面輸出可以看出，不同Bath模型推理時(shí)間在1.3685~1.6947ms，大大提升了模型的推理速度。

5. 總結(jié)

? 在本項(xiàng)目中，我們擴(kuò)展了TensorRT C# API 接口，使其支持動(dòng)態(tài)輸入模型。并結(jié)合分類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。

? 為了方便各位開發(fā)者使用，此處開發(fā)了配套的演示項(xiàng)目，主要是基于Yolov8開發(fā)的目標(biāo)檢測、目標(biāo)分割、人體關(guān)鍵點(diǎn)識(shí)別、圖像分類以及旋轉(zhuǎn)目標(biāo)識(shí)別，并且支持動(dòng)態(tài)輸入模型，用戶可以同時(shí)推理任意張圖像。

• Yolov8 Det 目標(biāo)檢測項(xiàng)目源碼：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom_dynamic/Yolov8Det.cs

• Yolov8 Seg 目標(biāo)分割項(xiàng)目源碼：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom_dynamic/Yolov8Seg.cs

• Yolov8 Pose 人體關(guān)鍵點(diǎn)識(shí)別項(xiàng)目源碼：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom_dynamic/Yolov8Pose.cs

• Yolov8 Cls 圖像分類項(xiàng)目源碼：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom_dynamic/Yolov8Cls.cs

• Yolov8 Obb 旋轉(zhuǎn)目標(biāo)識(shí)別項(xiàng)目源碼：

https://github.com/guojin-yan/TensorRT-CSharp-API-Samples/blob/master/model_samples/yolov8_custom_dynamic/Yolov8Obb.cs

? 同時(shí)對(duì)本項(xiàng)目開發(fā)的案例進(jìn)行了時(shí)間測試，以下時(shí)間只是程序運(yùn)行一次的時(shí)間，測試環(huán)境為：

• CPU：i7-165G7

• CUDA型號(hào)：12.2

• Cudnn：8.9.3

• TensorRT：8.6.1.6

? 最后如果各位開發(fā)者在使用中有任何問題，歡迎大家與我聯(lián)系。文章來源地址http://www.zghlxwxcb.cn/news/detail-854738.html

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