本文主要为大家详细介绍了如何使用C++将yolov8 onnx格式转化为tensorrt格式,文中的示例代码讲解详细,感兴趣的小伙伴可以了解一下

使用TensorRT进行加速推理时，需要先将onnx格式转化为tensorrt格式，以下是使用C++来进行转化代码以及对应的CMakeLists.txt文件。

操作系统：ubuntu20.04

代码

// main.cpp
#include <iostream>
#include <memory>
#include <fstream>
#include <assert.h>
#include "NvInfer.h"
#include "NvOnnxParser.h"
#include "common.h"

class Logger : public nvinfer1::ILogger{
    void log(Severity severity, const char* msg) noexcept override {
        // suppress info-level messages
        if (severity <= Severity::kWARNING)
            std::cout << msg << std::endl;
    }
} logger;


int main(int argc, char** argv){
    if(argc !=2){
        std::cerr << "usage: ./build [onnx_file_path]" <<std::endl;
        return -1;
    }

    // 获取onnx文件路径
    char* onnx_file_path = argv[1];

    //================1.创建builder====================
    auto builder = std::unique_ptr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(logger));
    if(!builder){
        std::cerr << "Failed to creater builder" <<std::endl;
        return -1;
    }

    //===============2.创建network=====================
    const auto explicitBatch= 1U <<static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
    auto network = std::unique_ptr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(explicitBatch));
    if (!network){
        std::cout << "Failed to create network" << std::endl;
        return -1;
    }

    // =============创建onnxparser用于解析onnx文件===========
    auto parser = std::unique_ptr<nvonnxparser::IParser>(nvonnxparser::createParser(*network, logger));
    // 调用onnxparser的parseFromFile方法解析onnx文件
    auto parsed = parser->parseFromFile(onnx_file_path, static_cast<int>(nvinfer1::ILogger::Severity::kWARNING));
    if (!parsed){
        std::cout << "Failed to parse onnx file" << std::endl;
        return -1;
    }
    // 配置网络参数
    auto input = network->getInput(0);
    auto profile = builder->createOptimizationProfile();
    profile->setDimensions(input->getName(), nvinfer1::OptProfileSelector::kMIN, nvinfer1::Dims4{1, 3, 960, 960}); // 设置最小尺寸
    profile->setDimensions(input->getName(), nvinfer1::OptProfileSelector::kOPT, nvinfer1::Dims4{1, 3, 960, 960}); // 设置最优尺寸
    profile->setDimensions(input->getName(), nvinfer1::OptProfileSelector::kMAX, nvinfer1::Dims4{1, 3, 960, 960}); // 设置最大尺寸

    //==============创建config配置=====================
    auto config = std::unique_ptr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
    if (!config){
        std::cout << "Failed to create config" << std::endl;
        return -1;
    }

    config->addOptimizationProfile(profile);
    // 设置精度，设置为FP16，设置为INT8需要额外calibrator 
    config->setFlag(nvinfer1::BuilderFlag::kFP16);
    // 设置最大batchsize
    builder->setMaxBatchSize(1);
    // 设置最大工作空间
    config->setMemoryPoolLimit(nvinfer1::MemoryPoolType::kWORKSPACE, 1 << 30);

    //创建流
    auto profileStream = samplesCommon::makeCudaStream();
    if(!profileStream){
        return -1;
    }
    config->setProfileStream(*profileStream);

    // ==========创建engine ==========
    auto plan = std::unique_ptr<nvinfer1::IHostMemory>(builder->buildSerializedNetwork(*network, *config));
    if (!plan){
        std::cout << "Failed to create engine" << std::endl;
        return -1;
    }

    // ========== 5. 序列化保存engine ==========
    std::ofstream engine_file("./yolov8x.engine", std::ios::binary);
    assert(engine_file.is_open() && "Failed to open engine file");
    engine_file.write((char *)plan->data(), plan->size());
    engine_file.close();

    std::cout << "Engine build success!" << std::endl;
    return 0;
}

CMakeLists.txt文件

cmake_minimum_required(VERSION 3.10)
project(TensorRT_Test LANGUAGES CXX CUDA)

set(CMAKE_CUDA_STANDARD 14)
set(CMAKE_CXX_STANDARD 14)

# 添加头文件路径 cuda tensorRT
include_directories(/usr/local/cuda-11.8/include)
include_directories(/xxx/tensorRT/TensorRT-8.6.1.6/include)
include_directories(/xxx/tensorRT/TensorRT-8.6.1.6/samples/common/)

# 添加库文件
link_directories(/usr/local/cuda-11.8/lib64)
link_directories(/xxx/tensorRT/TensorRT-8.6.1.6/lib)

add_executable(build main.cpp)
target_link_libraries(build nvinfer nvonnxparser cudart)

其中：在CMakeLists.txt中cuda及TensorRT的头文件和库文件路径需要改成自己的。

编译

以yolov8为例，使用cmake编译好后会生成build可执行文件，执行以下命令即可等待生成yolov8x.engine文件

./build <onnx_path>