#include <map>
#include <chrono>
#include <fstream>
#include "NvInfer.h"
#include "logging.h"
#include "cuda_runtime_api.h"


static const int INPUT_H=32;
static const int INPUT_W=32;
static const int BATCH_SIZE=32;
static const int OUTPUT_SIZE=10;
static const int INFER_NUMS=10000;
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";

using namespace nvinfer1;
static Logger gLogger;



#define CHECK(status) \
    do\
    {\
        auto ret = (status);\
        if (ret != 0)\
        {\
            std::cerr << "Cuda failure: " << ret << std::endl;\
            abort();\
        }\
    } while (0)


std::map<std::string, Weights> loadWeights(const std::string file)
{
    std::cout << "Loading weights: " << file << std::endl;
    std::map<std::string, Weights> weightMap;

    // Open weights file
    std::ifstream input(file);
    assert(input.is_open() && "Unable to load weight file.");

    // Read number of weight blobs
    int32_t count;
    input >> count;
    assert(count > 0 && "Invalid weight map file.");

    while (count--)
    {
        Weights wt{DataType::kFLOAT, nullptr, 0};
        uint32_t size;

        // Read name and type of blob
        std::string name;
        input >> name >> std::dec >> size;
        wt.type = DataType::kFLOAT;

        // Load blob
        uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
        for (uint32_t x = 0, y = size; x < y; ++x)
        {
            input >> std::hex >> val[x];
        }
        wt.values = val;
        
        wt.count = size;
        weightMap[name] = wt;
    }

    return weightMap;
}

ICudaEngine* createLenetEngine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt)
{
    //开始定义网络 0U无符号整型0
    INetworkDefinition* network =  builder->createNetworkV2(0U);
    ITensor* input = network->addInput(INPUT_BLOB_NAME, dt, Dims3{1, INPUT_H, INPUT_W});
    assert(input);

    std::map<std::string, Weights> weightMap = loadWeights("../lenet5.wts");//载入权重放入weightMap
    // std::cout<<weightMap["conv1.weight"]<<std::endl;  

    //卷积层
    IConvolutionLayer* conv1 = network->addConvolution(*input, 6, DimsHW{5, 5}, weightMap["conv1.weight"], weightMap["conv1.bias"]);
    //设置步长
    assert(conv1);
    conv1->setStrideNd(DimsHW{1, 1});
    
    //激活层
    IActivationLayer* relu1 = network->addActivation(*conv1->getOutput(0), ActivationType::kRELU);
    assert(relu1);
    
    //pooling层
    IPoolingLayer* pool1 = network->addPoolingNd(*relu1->getOutput(0), PoolingType::kAVERAGE, DimsHW{2, 2});
    assert(pool1);
    pool1->setStrideNd(DimsHW{2, 2});

    //卷积层
    IConvolutionLayer* conv2 = network->addConvolution(*pool1->getOutput(0), 16, DimsHW{5, 5}, weightMap["conv2.weight"], weightMap["conv2.bias"]);
    //设置步长
    assert(conv2);
    conv2->setStrideNd(DimsHW{1, 1});    
    //激活层
    IActivationLayer* relu2 = network->addActivation(*conv2->getOutput(0), ActivationType::kRELU);
    assert(relu2);

    //pooling层
    IPoolingLayer* pool2 = network->addPoolingNd(*relu2->getOutput(0), PoolingType::kAVERAGE, DimsHW{2, 2});
    assert(pool2);
    pool2->setStrideNd(DimsHW{2, 2});

    //全连接
    IFullyConnectedLayer* fc1  = network->addFullyConnected(*pool2->getOutput(0), 120, weightMap["fc1.weight"], weightMap["fc1.bias"]);
    assert(fc1);
    //激活层
    IActivationLayer* relu3 = network->addActivation(*fc1->getOutput(0), ActivationType::kRELU);
    assert(relu3);

    //全连接
    IFullyConnectedLayer* fc2  = network->addFullyConnected(*relu3->getOutput(0), 84, weightMap["fc2.weight"], weightMap["fc2.bias"]);
    assert(fc2);
    //激活层
    IActivationLayer* relu4 = network->addActivation(*fc2->getOutput(0), ActivationType::kRELU);
    assert(relu4);

    //全连接
    IFullyConnectedLayer* fc3  = network->addFullyConnected(*relu4->getOutput(0), OUTPUT_SIZE, weightMap["fc3.weight"], weightMap["fc3.bias"]);
    assert(fc3);
    
    //分类层
    ISoftMaxLayer *prob = network->addSoftMax(*fc3->getOutput(0));
    assert(prob);
    prob->getOutput(0)->setName(OUTPUT_BLOB_NAME);
    network->markOutput(*prob->getOutput(0));

    //构造engine
    builder->setMaxBatchSize(maxBatchSize);
    config->setMaxWorkspaceSize(1<<20);
    ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
    
    //放入engine 所以network可以销毁了
    network->destroy();
    // 释放资源
    for (auto& mem : weightMap)
    {
        free((void*) (mem.second.values));
    }

    return engine;
}
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
{
    //创建builder
    IBuilder* builder = createInferBuilder(gLogger);//网络入口　类似pytorch的model
    IBuilderConfig* config = builder->createBuilderConfig();

    //创建模型　搭建网络层
    ICudaEngine* engine = createLenetEngine(maxBatchSize, builder, config, DataType::kFLOAT);
    assert(engine!=nullptr);

    //序列化engine
    (*modelStream)= engine->serialize();

    //销毁对象   
    engine->destroy();
    builder->destroy();

}

void doInference(IExecutionContext& context, float* input, float *output, int batchSize)
{
    //使用传进来的context恢复engine。
    const ICudaEngine& engine = context.getEngine();
    //输入输出总共有两个，做一下验证
    assert(engine.getNbBindings()==2);
    //void 
    void* buffers[2];
    //获取与这个engine相关的输入输出tensor的索引s
    const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
    const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);

    //为输入输出tensor开辟显存。
    CHECK(cudaMalloc(&buffers[inputIndex], batchSize * INPUT_H * INPUT_W * sizeof(float)));
    CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));

    //创建cuda流，用于管理数据复制，存取，和计算的并发操作
    cudaStream_t stream;
    CHECK(cudaStreamCreate(&stream));

    //从内存到显存，input是读入内存中的数据；buffers[inputIndex]是显存上的存储区域，用于存放输入数据
    CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
    // //启动cuda核，异步执行推理计算
    context.enqueue(batchSize, buffers, stream, nullptr);
   //从显存到内存，buffers[outputIndex]是显存中的存储区，存放模型输出；output是内存中的数据
    CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
    //如果使用了多个cuda流，需要同步
    cudaStreamSynchronize(stream);

    // Release stream and buffers    
    cudaStreamDestroy(stream);
    CHECK(cudaFree(buffers[inputIndex]));
    CHECK(cudaFree(buffers[outputIndex]));

}
int main(int argc, char ** argv)  
{   
    if (argc!=2)
    {   
        std::cerr << "arguments not right!" << std::endl;
        std::cerr << "./lenet -s   // serialize model to plan file" << std::endl;
        std::cerr << "./lenet -d   // deserialize plan file and run inference" << std::endl;
        return -1;
    }
    
    //序列化模型为.engine文件
    if(std::string(argv[1])=="-s")
    {   
        IHostMemory* modelStream{nullptr};//modelStream是一块内存区域，用来保存序列化文件
        APIToModel(1, &modelStream);
        assert(modelStream!=nullptr);
        //变换为.engine文件
        std::ofstream p("lenet.engine");
        if (!p)
        {
            std::cerr<<"can not open plan file"<<std::endl;
            return -1;
        }
        p.write(reinterpret_cast<const char *>(modelStream->data()), modelStream->size());
        // p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
        //销毁对象
        modelStream->destroy();
    }
    else if (std::string(argv[1])=="-d")
    {      
        char *trtModelStream{nullptr};
        size_t size{0};

        std::ifstream file("lenet.engine", std::ios::binary);
        if (file.good()) {
            file.seekg(0, file.end);
            size = file.tellg();
            file.seekg(0, file.beg);
            trtModelStream = new char[size];
            assert(trtModelStream);
            file.read(trtModelStream, size);
            file.close();
        }
        else
        {
        return -1;
        }
        //模拟数据
        float data[INPUT_H*INPUT_W];
        for (int i=0;i<INPUT_W*INPUT_H;i++)
        {
            data[i] = 1.0;
        }
        //创建运行时环境IRuntime对象
        IRuntime* runtime = createInferRuntime(gLogger);
        assert(runtime !=nullptr);
        ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream,size,nullptr);
        assert(engine !=nullptr);

        //创建上下文环境，主要用与inference函数中启动cuda核
        IExecutionContext* context = engine->createExecutionContext();
        assert(context !=nullptr);
        
        //开始推理, 模拟推理1000次，存储推理结果
        float prob[OUTPUT_SIZE];
        auto start = std::chrono::system_clock::now();//开始时间
        for (int i=0;i<INFER_NUMS;i++)
        {               
            // std::cout<<"data[i]:"<<data[i]<<std::endl;
            doInference(*context, data, prob, 1);            
        }
        auto end = std::chrono::system_clock::now();//结束时间
        std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
        
        context->destroy();
        engine->destroy();
        runtime->destroy();

        std::cout<<"prob:";
        for (int i=0;i<OUTPUT_SIZE;i++)
        {         
            std::cout<<prob[i]<<",";
        }   

    }
    else
    {
       return -1;
    }

    return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 2.6)

project(lenet)

add_definitions(-std=c++11)

set(TARGET_NAME "lenet")

option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)

include_directories(${PROJECT_SOURCE_DIR}/include)
# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
# cuda
include_directories(/usr/local/cuda/include)
link_directories(/usr/local/cuda/lib64)
# tensorrt
include_directories(/usr/include/x86_64-linux-gnu)
link_directories(/usr/lib/x86_64-linux-gnu)

#tar包 tensorrt
#include_directories(/red_detection/tensorrt_learn/software/TensorRT-7.0.0.11/include)
#link_directories(/red_detection/tensorrt_learn/software/TensorRT-7.0.0.11/lib)

FILE(GLOB SRC_FILES ${PROJECT_SOURCE_DIR}/lenet.cpp ${PROJECT_SOURCE_DIR}/include/*.h)

add_executable(${TARGET_NAME} ${SRC_FILES})
target_link_libraries(${TARGET_NAME} nvinfer)
target_link_libraries(${TARGET_NAME} cudart)

add_definitions(-O2 -pthread)