//!
//! 简介： 使用caffe解析器创建MNIST网络并标记输出层
//!
//! 参数： 指向将用MNIST网络填充的网络指针
//!
//! 参数： 指向引擎生成器的生成器指针
//!
void SampleMNIST::constructNetwork(SampleUniquePtr<nvcaffeparser1::ICaffeParser>& parser, SampleUniquePtr<nvinfer1::INetworkDefinition>& network)
{
    const nvcaffeparser1::IBlobNameToTensor* blobNameToTensor = parser->parse(
        mParams.prototxtFileName.c_str(),
        mParams.weightsFileName.c_str(),
        *network,
        nvinfer1::DataType::kFLOAT);

    //输出Tensor标记
    for (auto& s : mParams.outputTensorNames)
    {
        network->markOutput(*blobNameToTensor->find(s.c_str()));
    }

    // 在网络开头添加减均值操作
    nvinfer1::Dims inputDims = network->getInput(0)->getDimensions();
    // 读取均值文件的数据
    mMeanBlob = SampleUniquePtr<nvcaffeparser1::IBinaryProtoBlob>(parser->parseBinaryProto(mParams.meanFileName.c_str()));
    nvinfer1::Weights meanWeights{nvinfer1::DataType::kFLOAT, mMeanBlob->getData(), inputDims.d[1] * inputDims.d[2]};
    // 数据的原始分布是[0,256]
    // 减去均值之后是[-127,127]
    // The preferred method is use scales computed based on a representative data set
    // and apply each one individually based on the tensor. The range here is large enough for the
    // network, but is chosen for example purposes only.
    float maxMean = samplesCommon::getMaxValue(static_cast<const float*>(meanWeights.values), samplesCommon::volume(inputDims));

    auto mean = network->addConstant(nvinfer1::Dims3(1, inputDims.d[1], inputDims.d[2]), meanWeights);
    mean->getOutput(0)->setDynamicRange(-maxMean, maxMean);
    network->getInput(0)->setDynamicRange(-maxMean, maxMean);
    // 执行减均值操作
    auto meanSub = network->addElementWise(*network->getInput(0), *mean->getOutput(0), ElementWiseOperation::kSUB);
    meanSub->getOutput(0)->setDynamicRange(-maxMean, maxMean);
    network->getLayer(0)->setInput(0, *meanSub->getOutput(0));
    // 执行缩放操作
    samplesCommon::setAllTensorScales(network.get(), 127.0f, 127.0f);
    // 最后的网络的输入就是[-1, 1]
}

//!
//! 简介： 创建网络、配置生成器并创建网络引擎
//!
//! 细节： 此函数通过解析caffe模型创建MNIST网络，并构建用于运行MNIST（mEngine）的引擎
//!
//! 返回值： 如果引擎被创建成功，直接返回True
//!
bool SampleMNISTAPI::build()
{
    //加载权重，*.wts文件
    mWeightMap = loadWeights(locateFile(mParams.weightsFile, mParams.dataDirs));

    // 1. Create builder
    //创建一个 IBuilder，传进gLogger参数是为了方便打印信息。
    //builder 这个地方感觉像是使用了建造者模式。
    auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(gLogger.getTRTLogger()));
    if (!builder)
    {
        return false;
    }
    //创建一个 network对象，但是这个network对象只是一个空架子，里面的属性还没有具体的数值。
    auto network = SampleUniquePtr<nvinfer1::INetworkDefinition>(builder->createNetwork());
    if (!network)
    {
        return false;
    }
    //创建一个配置文件解析对象
    auto config = SampleUniquePtr<nvinfer1::IBuilderConfig>(builder->createBuilderConfig());
    if (!config)
    {
        return false;
    }
    //利用C++ API创建网络
    auto constructed = constructNetwork(builder, network, config);
    if (!constructed)
    {
        return false;
    }

    assert(network->getNbInputs() == 1);
    auto inputDims = network->getInput(0)->getDimensions();
    assert(inputDims.nbDims == 3);

    assert(network->getNbOutputs() == 1);
    auto outputDims = network->getOutput(0)->getDimensions();
    assert(outputDims.nbDims == 3);

    return true;
}

// 为网络添加输入
    ITensor* data = network->addInput(
        mParams.inputTensorNames[0].c_str(), DataType::kFLOAT, Dims3{1, mParams.inputH, mParams.inputW});

enum class DataType : int
{
    kFLOAT = 0, //!< FP32 format.
    kHALF = 1,  //!< FP16 format.
    kINT8 = 2,  //!< quantized INT8 format.
    kINT32 = 3  //!< INT32 format.
};

// Create scale layer with default power/shift and specified scale parameter.
    const float scaleParam = 0.0125f;
    const Weights power{DataType::kFLOAT, nullptr, 0};
    const Weights shift{DataType::kFLOAT, nullptr, 0};
    const Weights scale{DataType::kFLOAT, &scaleParam, 1};
    IScaleLayer* scale_1 = network->addScale(*data, ScaleMode::kUNIFORM, shift, scale, power);
    assert(scale_1);

power: 默认为1。
scale: 默认为1。
shift: 默认为0。

class Weights
{
public:
    DataType type;      //!< The type of the weights.
    const void* values; //!< The weight values, in a contiguous array.
    int64_t count;      //!< The number of weights in the array.
};

// Add convolution layer with 20 outputs and a 5x5 filter.
// 添加卷积层
IConvolutionLayer* conv1 = network->addConvolution(*scale_1->getOutput(0), 20, DimsHW{5, 5}, mWeightMap["conv1filter"], mWeightMap["conv1bias"]);
assert(conv1);
//设置步长
conv1->setStride(DimsHW{1, 1});

// Add softmax layer to determine the probability.
 ISoftMaxLayer* prob = network->addSoftMax(*ip2->getOutput(0));
assert(prob);
prob->getOutput(0)->setName(mParams.outputTensorNames[0].c_str());
network->markOutput(*prob->getOutput(0)