模型来源：
算法工程师训练得到的onnx模型

c++对模型的转换：
拿到onnx模型后，通过tensorRT将onnx模型转换为对应的engine模型，注意：训练用的tensorRT版本和c++调用的tensorRT版本必须一致。

如何转换：

1. 算法工程师直接转换为.engine文件进行交付。
2. 自己转换，进入tensorRT安装目录\bin目录下，将onnx模型拷贝到bin目录，地址栏中输入cmd回车弹出控制台窗口，然后输入转换命令，如：

trtexec --onnx=model.onnx --saveEngine=model.engine --workspace=1024 --optShapes=input:1x13x512x640 --fp16

然后回车，等待转换完成，完成后如图所示：

并且在bin目录下生成.engine模型文件。

c++对.engine模型文件的调用和推理
首先将tensorRT对模型的加载及推理进行封装，命名为CTensorRT.cpp,老样子贴代码：

//CTensorRT.cpp
class Logger : public nvinfer1::ILogger {
	void log(Severity severity, const char* msg) noexcept override {
		if (severity <= Severity::kWARNING)
			std::cout << msg << std::endl;
	}
};

Logger logger;
class CtensorRT
{
public:
	CtensorRT() {}
	~CtensorRT() {}

private:
	std::shared_ptr<nvinfer1::IExecutionContext> _context;
	std::shared_ptr<nvinfer1::ICudaEngine> _engine;

	nvinfer1::Dims _inputDims;
	nvinfer1::Dims _outputDims;
public:
	void cudaCheck(cudaError_t ret, std::ostream& err = std::cerr)
	{
		if (ret != cudaSuccess)
		{
			err << "Cuda failure: " << cudaGetErrorString(ret) << std::endl;
			abort();
		}
	}

	bool loadOnnxModel(const std::string& filepath)
	{
		auto builder = std::unique_ptr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(logger));
		if (!builder)
		{
			return false;
		}

		const auto explicitBatch = 1U << static_cast<uint32_t>(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
		auto network = std::unique_ptr<nvinfer1::INetworkDefinition>(builder->createNetworkV2(explicitBatch));
		if (!network)
		{
			return false;
		}

		auto config = std::unique_ptr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
		if (!config)
		{
			return false;
		}

		auto parser = std::unique_ptr<nvonnxparser::IParser>(nvonnxparser::createParser(*network, logger));
		if (!parser)
		{
			return false;
		}

		parser->parseFromFile(filepath.c_str(), static_cast<int32_t>(nvinfer1::ILogger::Severity::kWARNING));
		std::unique_ptr<IHostMemory> plan{ builder->buildSerializedNetwork(*network, *config) };
		if (!plan)
		{
			return false;
		}

		std::unique_ptr<IRuntime> runtime{ createInferRuntime(logger) };
		if (!runtime)
		{
			return false;
		}

		_engine = std::shared_ptr<nvinfer1::ICudaEngine>(runtime->deserializeCudaEngine(plan->data(), plan->size()));
		if (!_engine)
		{
			return false;
		}
		_context = std::shared_ptr<nvinfer1::IExecutionContext>(_engine->createExecutionContext());
		if (!_context)
		{
			return false;
		}

		int nbBindings = _engine->getNbBindings();
		assert(nbBindings == 2); // 输入和输出，一共是2个

		for (int i = 0; i < nbBindings; i++)
		{
			if (_engine->bindingIsInput(i))
				_inputDims = _engine->getBindingDimensions(i);    // (1,3,752,752)
			else
				_outputDims = _engine->getBindingDimensions(i);
		}
		return true;
	}

	bool loadEngineModel(const std::string& filepath)
	{
		std::ifstream file(filepath, std::ios::binary);
		if (!file.good())
		{
			return false;
		}

		std::vector<char> data;
		try
		{
			file.seekg(0, file.end);
			const auto size = file.tellg();
			file.seekg(0, file.beg);

			data.resize(size);
			file.read(data.data(), size);
		}
		catch (const std::exception& e)
		{
			file.close();
			return false;
		}
		file.close();

		auto runtime = std::unique_ptr<nvinfer1::IRuntime>(nvinfer1::createInferRuntime(logger));
		_engine = std::shared_ptr<nvinfer1::ICudaEngine>(runtime->deserializeCudaEngine(data.data(), data.size()));
		if (!_engine)
		{
			return false;
		}

		_context = std::shared_ptr<nvinfer1::IExecutionContext>(_engine->createExecutionContext());
		if (!_context)
		{
			return false;
		}

		int nbBindings = _engine->getNbBindings();
		assert(nbBindings == 2); // 输入和输出，一共是2个

		// 为输入和输出创建空间
		for (int i = 0; i < nbBindings; i++)
		{
			if (_engine->bindingIsInput(i))
				_inputDims = _engine->getBindingDimensions(i);    //得到输入结构
			else
				_outputDims = _engine->getBindingDimensions(i);//得到输出结构
		}
		return true;
	}

	void ONNX2TensorRT(const char* ONNX_file, std::string save_ngine)
	{
		// 1.创建构建器的实例
		nvinfer1::IBuilder* builder = nvinfer1::createInferBuilder(logger);

		// 2.创建网络定义
		uint32_t flag = 1U << static_cast<uint32_t>(nvinfer1::NetworkDefinitionCreationFlag::kEXPLICIT_BATCH);
		nvinfer1::INetworkDefinition* network = builder->createNetworkV2(flag);

		// 3.创建一个 ONNX 解析器来填充网络
		nvonnxparser::IParser* parser = nvonnxparser::createParser(*network, logger);

		// 4.读取模型文件并处理任何错误
		parser->parseFromFile(ONNX_file, static_cast<int32_t>(nvinfer1::ILogger::Severity::kWARNING));
		for (int32_t i = 0; i < parser->getNbErrors(); ++i)
		{
			std::cout << parser->getError(i)->desc() << std::endl;
		}

		// 5.创建一个构建配置，指定 TensorRT 应该如何优化模型
		nvinfer1::IBuilderConfig* config = builder->createBuilderConfig();

		// 7.指定配置后，构建引擎
		nvinfer1::IHostMemory* serializedModel = builder->buildSerializedNetwork(*network, *config);

		// 8.保存TensorRT模型
		std::ofstream p(save_ngine, std::ios::binary);
		p.write(reinterpret_cast<const char*>(serializedModel->data()), serializedModel->size());

		// 9.序列化引擎包含权重的必要副本，因此不再需要解析器、网络定义、构建器配置和构建器，可以安全地删除
		delete parser;
		delete network;
		delete config;
		delete builder;

		// 10.将引擎保存到磁盘，并且可以删除它被序列化到的缓冲区
		delete serializedModel;
	}

	uint32_t getElementSize(nvinfer1::DataType t) noexcept
	{
		switch (t)
		{
		case nvinfer1::DataType::kINT32: return 4;
		case nvinfer1::DataType::kFLOAT: return 4;
		case nvinfer1::DataType::kHALF: return 2;
		case nvinfer1::DataType::kBOOL:
		case nvinfer1::DataType::kINT8: return 1;
		}
		return 0;
	}

	int64_t volume(const nvinfer1::Dims& d)
	{
		return std::accumulate(d.d, d.d + d.nbDims, 1, std::multiplies<int64_t>());
	}

	bool infer(unsigned char* input, int real_input_size, cv::Mat& out_mat)
	{
		tensor_custom::BufferManager buffer(_engine);

		cudaStream_t stream;
		cudaStreamCreate(&stream); // 创建异步cuda流

		int binds = _engine->getNbBindings();
		for (int i = 0; i < binds; i++)
		{
			if (_engine->bindingIsInput(i))
			{
				size_t input_size;
				float* host_buf = static_cast<float*>(buffer.getHostBufferData(i, input_size));
				memcpy(host_buf, input, real_input_size);
				break;
			}
		}

		// 将输入传递到GPU
		buffer.copyInputToDeviceAsync(stream);
		// 异步执行
		bool status = _context->enqueueV2(buffer.getDeviceBindngs().data(), stream, nullptr);
		if (!status)
			return false;

		buffer.copyOutputToHostAsync(stream);
		for (int i = 0; i < binds; i++)
		{
			if (!_engine->bindingIsInput(i))
			{
				size_t output_size;
				float* tmp_out = static_cast<float*>(buffer.getHostBufferData(i, output_size));
				//do your something here
				break;
			}
		}
		cudaStreamSynchronize(stream);
		cudaStreamDestroy(stream);
		return true;
	}
};

调用方式

int main()
{
	vector<int> dims = { 1,13,512,640 };
	vector<float> vall;
	for (int i=0;i<13;i++)
	{
		string file = "D:\\xxx\\" + to_string(i) + ".png";
		cv::Mat mt = imread(file, IMREAD_GRAYSCALE);
		cv::resize(mt, mt, cv::Size(640,512));
		mt.convertTo(mt, CV_32F, 1.0 / 255);
		cv::Mat shape_xr = mt.reshape(1, mt.total() * mt.channels());
		std::vector<float> vec_xr = mt.isContinuous() ? shape_xr : shape_xr.clone();
		vall.insert(vall.end(), vec_xr.begin(), vec_xr.end());
	}
	cv::Mat mt_4d(4, &dims[0], CV_32F, vall.data());

	string engine_model_file = "model.engine";
	CtensorRT cTensor;
	if (cTensor.loadEngineModel(engine_model_file))
	{
		cv::Mat out_mat;
		if (!cTensor.infer(mt_4d.data, vall.size() * 4, out_mat))
			std::cout << "infer error!" << endl;
		else
			cv::imshow("out", out_mat);
	}
	else
		std::cout << "load model file failed!" << endl;
	cv::waitKey(0);
	return 0;
}