如何在C#中加载和运行TensorRT模型

要在C#中加载和运行TensorRT模型，您需要使用NVIDIA TensorRT库

1. 安装NVIDIA TensorRT库：首先，您需要从NVIDIA官方网站下载并安装适用于您的操作系统的TensorRT库。请注意，TensorRT库需要NVIDIA GPU以及相应的驱动程序和CUDA工具包。

2. 创建C#绑定：由于TensorRT是用C++编写的，因此您需要为C#创建一个绑定。这可以通过使用P/Invoke或者C++/CLI实现。在这里，我们将使用C++/CLI创建一个简单的绑定。

3. 加载和运行TensorRT模型：使用C#绑定加载和运行TensorRT模型。

以下是一个简单的示例，说明如何在C#中加载和运行TensorRT模型：

using System;
    
using System.Runtime.InteropServices;


namespace TensorRTExample
{

    class Program
    {

        static void Main(string[] args)
        {
    
            // Load the TensorRT library
            IntPtr tensorrtLib = NativeMethods.LoadLibrary("nvinfer");

            if (tensorrtLib == IntPtr.Zero)
            {
    
                Console.WriteLine("Failed to load TensorRT library.");
    
                return;

            }
    

            // Create an inference runtime
            IntPtr runtime = NativeMethods.createInferRuntime(IntPtr.Zero);

            if (runtime == IntPtr.Zero)
            {
    
                Console.WriteLine("Failed to create inference runtime.");
    
                return;

            }
    

            // Load the TensorRT engine from a file
            string engineFilePath = "path/to/your/engine/file";
    
            IntPtr engine = NativeMethods.deserializeCudaEngine(runtime, engineFilePath, IntPtr.Zero);

            if (engine == IntPtr.Zero)
            {
    
                Console.WriteLine("Failed to deserialize CUDA engine.");
    
                return;

            }
    

            // Create an execution context for the engine
            IntPtr context = NativeMethods.createExecutionContext(engine);

            if (context == IntPtr.Zero)
            {
    
                Console.WriteLine("Failed to create execution context.");
    
                return;

            }
    

            // Set up input and output buffers
            int inputIndex = 0;
    
            int outputIndex = 1;
    
            float[] inputData = new float[1 * 3 * 224 * 224];
     // Replace with your input data
            float[] outputData = new float[1000];
     // Replace with your output data

            // Run inference
            NativeMethods.enqueue(context, 1, inputData, outputData, IntPtr.Zero);
    

            // Process the output data
            // ...

            // Clean up
            NativeMethods.destroyExecutionContext(context);
    
            NativeMethods.destroyCudaEngine(engine);
    
            NativeMethods.destroyInferRuntime(runtime);
    
            NativeMethods.FreeLibrary(tensorrtLib);

        }

    }


    internal static class NativeMethods
    {
    
        [DllImport("kernel32.dll")]
        public static extern IntPtr LoadLibrary(string dllToLoad);
    

        [DllImport("kernel32.dll")]
        public static extern IntPtr GetProcAddress(IntPtr hModule, string procedureName);
    

        [DllImport("kernel32.dll")]
        public static extern bool FreeLibrary(IntPtr hModule);
    

        [DllImport("nvinfer.dll")]
        public static extern IntPtr createInferRuntime(IntPtr logger);
    

        [DllImport("nvinfer.dll")]
        public static extern IntPtr deserializeCudaEngine(IntPtr runtime, string engineFilePath, IntPtr pluginFactory);
    

        [DllImport("nvinfer.dll")]
        public static extern IntPtr createExecutionContext(IntPtr engine);
    

        [DllImport("nvinfer.dll")]
        public static extern void destroyExecutionContext(IntPtr context);
    

        [DllImport("nvinfer.dll")]
        public static extern void destroyCudaEngine(IntPtr engine);
    

        [DllImport("nvinfer.dll")]
        public static extern void destroyInferRuntime(IntPtr runtime);
    

        [DllImport("nvinfer.dll")]
        public static extern bool enqueue(IntPtr context, int batchSize, float[] inputData, float[] outputData, IntPtr stream);

    }

}
    

请注意，这个示例仅用于演示目的。在实际应用中，您需要根据您的需求和数据集调整输入和输出数据。

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