C++笔记--基于Tensorrt将Onnx格式转换为engine推理格式

#include 
#include 
#include 

#include "NvInfer.h"
#include "NvOnnxParser.h"
#include "NvinferRuntime.h"

using namespace nvinfer1;
using namespace nvonnxparser;

// 全局创建 ILogger 类型的对象
class Logger : public ILogger
{
	virtual void log(Severity severity, const char* msg) noexcept override
	{
		// suppress info-level messages
		if (severity != Severity::kINFO)
			std::cout << msg << std::endl;
	}
} gLogger;

int onnx2engine(std::string onnx_filename, std::string enginefilePath, int type){


	// 创建builder
	IBuilder* builder = createInferBuilder(gLogger); 

	// 创建network
	nvinfer1::INetworkDefinition* network = builder->createNetworkV2(1U << static_cast(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH));

	// 创建onnx模型解析器
	auto parser = nvonnxparser::createParser(*network, gLogger);

	// 解析模型
	parser->parseFromFile(onnx_filename.c_str(), 2);
	for (int i = 0; i < parser->getNbErrors(); ++i)
	{
		std::cout << parser->getError(i)->desc() << std::endl;
	}
	printf("tensorRT load onnx model sucessful! n"); // 解析模型成功，第一个断点测试


	// 使用builder对象构建engine
	IBuilderConfig* config = builder->createBuilderConfig();
	config->setMaxWorkspaceSize(16 * (1 << 20));  // 设置最大工作空间
	config->setFlag(BuilderFlag::kGPU_FALLBACK);  // 启用GPU回退模式，作用？
	//	config->setFlag(BuilderFlag::kSTRICT_TYPES);  //强制执行xx位的精度计算
	if (type == 1) {
		config->setFlag(nvinfer1::BuilderFlag::kFP16); // 设置精度计算
	}
	if (type == 2) {
		config->setFlag(nvinfer1::BuilderFlag::kINT8);
	}

	IOptimizationProfile* profile = builder->createOptimizationProfile(); //创建优化配置文件
	profile->setDimensions("x", OptProfileSelector::kMIN, Dims4(1, 3, 32, 300)); // 设置输入x的动态维度，最小值
	profile->setDimensions("x", OptProfileSelector::kOPT, Dims4(1, 3, 32, 320)); // 期望输入的最优值
	profile->setDimensions("x", OptProfileSelector::kMAX, Dims4(1, 3, 32, 340)); // 最大值
	config->addOptimizationProfile(profile);


	ICudaEngine* myengine = builder->buildEngineWithConfig(*network, *config); //创建engine  第二个断点测试
	std::cout << "try to save engine file now" << std::endl;
	std::ofstream p(enginefilePath, std::ios::binary);
	if (!p) {
		std::cerr << "could not open plan output file" << std::endl;
		return 0;
	}

	// 序列化
	IHostMemory* modelStream = myengine->serialize(); // 第三个断点测试
	p.write(reinterpret_cast(modelStream->data()), modelStream->size()); // 写入

	modelStream->destroy(); // 销毁
	myengine->destroy();
	network->destroy();
	parser->destroy();
	std::cout << "convert onnx model to TensorRT engine model successfully!" << std::endl; // 转换成功，第四个断点测试

	return 0;
}

int main(int argc, char** argv) {

	onnx2engine("C:/Users/Admin/Desktop/onnx_engin/train90000_test9000.onnx", "C:/Users/Admin/Desktop/onnx_engin/train90000_test9001.engine", 1);
	return 0;
}

参考安装TensorRT和测试1

参考onnx转换engine（安装对应版本cudann和tensorRT）

注释参考
参考安装TensorRT和测试2