#include "trt_utils.h"
#include <NvInferRuntimeCommon.h>
#include <experimental/filesystem>
#include <fstream>
#include <iomanip>
using namespace nvinfer1;
REGISTER_TENSORRT_PLUGIN(MishPluginCreator);
REGISTER_TENSORRT_PLUGIN(ChunkPluginCreator);
REGISTER_TENSORRT_PLUGIN(HardswishPluginCreator);

cv::Mat blobFromDsImages(const std::vector<DsImage>& inputImages,
                        const int& inputH,
                         const int& inputW)
{
    std::vector<cv::Mat> letterboxStack(inputImages.size());
    for (uint32_t i = 0; i < inputImages.size(); ++i)
    {
        inputImages.at(i).getLetterBoxedImage().copyTo(letterboxStack.at(i));
    }
    return cv::dnn::blobFromImages(letterboxStack, 1.0, cv::Size(inputW, inputH),
                                   cv::Scalar(0.0, 0.0, 0.0),true);
}

static void leftTrim(std::string& s)
{
    s.erase(s.begin(), find_if(s.begin(), s.end(), [](int ch) { return !isspace(ch); }));
}

static void rightTrim(std::string& s)
{
    s.erase(find_if(s.rbegin(), s.rend(), [](int ch) { return !isspace(ch); }).base(), s.end());
}

std::string trim(std::string s)
{
    leftTrim(s);
    rightTrim(s);
    return s;
}

std::string triml(std::string s,const char* t)
{
    s.erase(0, s.find_first_not_of(t));
    return s;
}

std::string trimr(std::string s, const char* t)
{
    s.erase(s.find_last_not_of(t) + 1);
    return s;
}

float clamp(const float val, const float minVal, const float maxVal)
{
    assert(minVal <= maxVal);
    return std::min(maxVal, std::max(minVal, val));
}

bool fileExists(const std::string fileName, bool verbose)
{
    if (!std::experimental::filesystem::exists(std::experimental::filesystem::path(fileName)))
    {
        if (verbose) std::cout << "File does not exist : " << fileName << std::endl;
        return false;
    }
    return true;
}

// BBox convertBBoxNetRes(const float& bx, const float& by, const float& bw, const float& bh,
//                        const uint32_t& stride, const uint32_t& netW, const uint32_t& netH)
// {
//     BBox b;
//     // Restore coordinates to network input resolution
//     float x = bx * stride;
//     float y = by * stride;

//     b.x1 = x - bw / 2;
//     b.x2 = x + bw / 2;

//     b.y1 = y - bh / 2;
//     b.y2 = y + bh / 2;

//     b.x1 = clamp(b.x1, 0, netW);
//     b.x2 = clamp(b.x2, 0, netW);
//     b.y1 = clamp(b.y1, 0, netH);
//     b.y2 = clamp(b.y2, 0, netH);

//     return b;
// }

// void convertBBoxImgRes(const float scalingFactor,
//     const float xOffset,
//     const float yOffset,
//     BBox& bbox)
// {
//         //// Undo Letterbox
//     bbox.x1 -= xOffset;
//     bbox.x2 -= xOffset;
//     bbox.y1 -= yOffset;
//     bbox.y2 -= yOffset;
// //// Restore to input resolution
//     bbox.x1 /= scalingFactor;
//     bbox.x2 /= scalingFactor;
//     bbox.y1 /= scalingFactor;
//     bbox.y2 /= scalingFactor;
//     std::cout << "convertBBoxImgRes" << std::endl;


// }

// void printPredictions(const BBoxInfo& b, const std::string& className)
// {
//     std::cout << " label:" << b.label << "(" << className << ")"
//               << " confidence:" << b.prob << " xmin:" << b.box.x1 << " ymin:" << b.box.y1
//               << " xmax:" << b.box.x2 << " ymax:" << b.box.y2 << std::endl;
// }
//

std::vector<float> loadWeights(const std::string weightsFilePath, const std::string& networkType)
{
    assert(fileExists(weightsFilePath));
    std::cout << "Loading pre-trained weights..." << std::endl;
    std::ifstream file(weightsFilePath, std::ios_base::binary);
    assert(file.good());
    std::string line;
    file.ignore(4);
    char buf[2];
    file.read(buf, 1);
    if ((int)(unsigned char)buf[0] == 1)
    {
        file.ignore(11);
    }
    else if ((int)(unsigned char)buf[0] == 2)
    {
        file.ignore(15);
    }
    else
    {
        std::cout << "Invalid network type" << std::endl;
        assert(0);
    }

    std::vector<float> weights;
    char* floatWeight = new char[4];
    while (!file.eof())
    {
        file.read(floatWeight, 4);
        assert(file.gcount() == 4);
        weights.push_back(*reinterpret_cast<float*>(floatWeight));
        if (file.peek() == std::istream::traits_type::eof()) break;
    }
    std::cout << "Loading complete!" << std::endl;
    delete[] floatWeight;

   // std::cout << "Total Number of weights read : " << weights.size() << std::endl;
    return weights;
}

std::string dimsToString(const nvinfer1::Dims d)
{
    std::stringstream s;
    assert(d.nbDims >= 1);
    for (int i = 0; i < d.nbDims - 1; ++i)
    {
        s << std::setw(4) << d.d[i] << " x";
    }
    s << std::setw(4) << d.d[d.nbDims - 1];

    return s.str();
}

nvinfer1::ILayer* netAddMaxpool(int layerIdx, std::map<std::string, std::string>& block,
                                nvinfer1::ITensor* input, nvinfer1::INetworkDefinition* network)
{
    assert(block.at("type") == "maxpool");
    assert(block.find("size") != block.end());
    assert(block.find("stride") != block.end());

    int size = std::stoi(block.at("size"));
    int stride = std::stoi(block.at("stride"));

    nvinfer1::IPoolingLayer* pool
        = network->addPoolingNd(*input, nvinfer1::PoolingType::kMAX, nvinfer1::DimsHW{size, size});
    assert(pool);
    std::string maxpoolLayerName = "maxpool_" + std::to_string(layerIdx);
    int pad = (size - 1) / 2;
    pool->setPaddingNd(nvinfer1::DimsHW{pad,pad});
    pool->setStrideNd(nvinfer1::DimsHW{stride, stride});
    pool->setName(maxpoolLayerName.c_str());

    return pool;
}

nvinfer1::ILayer* netAddConvLinear(int layerIdx, std::map<std::string, std::string>& block,
                                   std::vector<float>& weights,
                                   std::vector<nvinfer1::Weights>& trtWeights, int& weightPtr,
                                   int& inputChannels, nvinfer1::ITensor* input,
                                   nvinfer1::INetworkDefinition* network)
{
    assert(block.at("type") == "convolutional");
    assert(block.find("batch_normalize") == block.end());
    assert(block.at("activation") == "linear");
    assert(block.find("filters") != block.end());
    assert(block.find("pad") != block.end());
    assert(block.find("size") != block.end());
    assert(block.find("stride") != block.end());

    int filters = std::stoi(block.at("filters"));
    int padding = std::stoi(block.at("pad"));
    int kernelSize = std::stoi(block.at("size"));
    int stride = std::stoi(block.at("stride"));
    int pad;
    if (padding)
        pad = (kernelSize - 1) / 2;
    else
        pad = 0;
    // load the convolution layer bias
    nvinfer1::Weights convBias{nvinfer1::DataType::kFLOAT, nullptr, filters};
    float* val = new float[filters];
    for (int i = 0; i < filters; ++i)
    {
        val[i] = weights[weightPtr];
        weightPtr++;
    }
    convBias.values = val;
    trtWeights.push_back(convBias);
    // load the convolutional layer weights
    int size = filters * inputChannels * kernelSize * kernelSize;
    nvinfer1::Weights convWt{nvinfer1::DataType::kFLOAT, nullptr, size};
    val = new float[size];
    for (int i = 0; i < size; ++i)
    {
        val[i] = weights[weightPtr];
        weightPtr++;
    }
    convWt.values = val;
    trtWeights.push_back(convWt);
    nvinfer1::IConvolutionLayer* conv = network->addConvolution(
        *input, filters, nvinfer1::DimsHW{kernelSize, kernelSize}, convWt, convBias);
    assert(conv != nullptr);
    std::string convLayerName = "conv_" + std::to_string(layerIdx);
    conv->setName(convLayerName.c_str());
    conv->setStride(nvinfer1::DimsHW{stride, stride});
    conv->setPadding(nvinfer1::DimsHW{pad, pad});

    return conv;
}

nvinfer1::ILayer* net_conv_bn_mish(int layerIdx,
    std::map<std::string, std::string>& block,
    std::vector<float>& weights,
    std::vector<nvinfer1::Weights>& trtWeights,
    int& weightPtr,
    int& inputChannels,
    nvinfer1::ITensor* input,
    nvinfer1::INetworkDefinition* network)
{
    assert(block.at("type") == "convolutional");
    assert(block.find("batch_normalize") != block.end());
    assert(block.at("batch_normalize") == "1");
    assert(block.at("activation") == "mish");
    assert(block.find("filters") != block.end());
    assert(block.find("pad") != block.end());
    assert(block.find("size") != block.end());
    assert(block.find("stride") != block.end());

    bool batchNormalize, bias;
    if (block.find("batch_normalize") != block.end())
    {
        batchNormalize = (block.at("batch_normalize") == "1");
        bias = false;
    }
    else
    {
        batchNormalize = false;
        bias = true;
    }
    // all conv_bn_leaky layers assume bias is false
    assert(batchNormalize == true && bias == false);

    int filters = std::stoi(block.at("filters"));
    int padding = std::stoi(block.at("pad"));
    int kernelSize = std::stoi(block.at("size"));
    int stride = std::stoi(block.at("stride"));
    int pad;
    if (padding)
        pad = (kernelSize - 1) / 2;
    else
        pad = 0;
    std::vector<float> bnBiases;
    for (int i = 0; i < filters; ++i)
    {
        bnBiases.push_back(weights[weightPtr]);
        weightPtr++;
    }
    // load BN weights
    std::vector<float> bnWeights;
    for (int i = 0; i < filters; ++i)
    {
        bnWeights.push_back(weights[weightPtr]);
        weightPtr++;
    }
    // load BN running_mean
    std::vector<float> bnRunningMean;
    for (int i = 0; i < filters; ++i)
    {
        bnRunningMean.push_back(weights[weightPtr]);
        weightPtr++;
    }
    // load BN running_var
    std::vector<float> bnRunningVar;
    for (int i = 0; i < filters; ++i)
    {
        // 1e-05 for numerical stability
        bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5f));
        weightPtr++;
    }
    // load Conv layer weights (GKCRS)
    int size = filters * inputChannels * kernelSize * kernelSize;
    nvinfer1::Weights convWt{ nvinfer1::DataType::kFLOAT, nullptr, size };
    float* val = new float[size];
    for (int i = 0; i < size; ++i)
    {
        val[i] = weights[weightPtr];
        weightPtr++;
    }
    convWt.values = val;
    trtWeights.push_back(convWt);
    nvinfer1::Weights convBias{ nvinfer1::DataType::kFLOAT, nullptr, 0 };
    trtWeights.push_back(convBias);
    nvinfer1::IConvolutionLayer* conv = network->addConvolution(
        *input, filters, nvinfer1::DimsHW{ kernelSize, kernelSize }, convWt, convBias);
    assert(conv != nullptr);
    std::string convLayerName = "conv_" + std::to_string(layerIdx);
    conv->setName(convLayerName.c_str());
    conv->setStride(nvinfer1::DimsHW{ stride, stride });
    conv->setPadding(nvinfer1::DimsHW{ pad, pad });

    /***** BATCHNORM LAYER *****/
    /***************************/
    size = filters;
    // create the weights
    nvinfer1::Weights shift{ nvinfer1::DataType::kFLOAT, nullptr, size };
    nvinfer1::Weights scale{ nvinfer1::DataType::kFLOAT, nullptr, size };
    nvinfer1::Weights power{ nvinfer1::DataType::kFLOAT, nullptr, size };
    float* shiftWt = new float[size];
    for (int i = 0; i < size; ++i)
    {
        shiftWt[i]
            = bnBiases.at(i) - ((bnRunningMean.at(i) * bnWeights.at(i)) / bnRunningVar.at(i));
    }
    shift.values = shiftWt;
    float* scaleWt = new float[size];
    for (int i = 0; i < size; ++i)
    {
        scaleWt[i] = bnWeights.at(i) / bnRunningVar[i];
    }
    scale.values = scaleWt;
    float* powerWt = new float[size];
    for (int i = 0; i < size; ++i)
    {
        powerWt[i] = 1.0;
    }
    power.values = powerWt;
    trtWeights.push_back(shift);
    trtWeights.push_back(scale);
    trtWeights.push_back(power);
    // Add the batch norm layers
    nvinfer1::IScaleLayer* bn = network->addScale(
        *conv->getOutput(0), nvinfer1::ScaleMode::kCHANNEL, shift, scale, power);
    assert(bn != nullptr);
    std::string bnLayerName = "batch_norm_" + std::to_string(layerIdx);
    bn->setName(bnLayerName.c_str());
    /***** ACTIVATION LAYER *****/
    /****************************/
    auto creator = getPluginRegistry()->getPluginCreator("Mish_TRT", "1");
    const nvinfer1::PluginFieldCollection* pluginData = creator->getFieldNames();
    nvinfer1::IPluginV2 *pluginObj = creator->createPlugin(("mish" + std::to_string(layerIdx)).c_str(), pluginData);
    nvinfer1::ITensor* inputTensors[] = { bn->getOutput(0) };
    auto mish = network->addPluginV2(&inputTensors[0], 1, *pluginObj);
    return mish;
}

int getNumChannels(nvinfer1::ITensor* t)
{
    nvinfer1::Dims d = t->getDimensions();
    assert(d.nbDims == 3);

    return d.d[0];
}

std::vector<int> split_layer_index(const std::string &s_,const std::string &delimiter_)
{
    std::vector<int> index;
    std::string s = s_;
    size_t pos = 0;
    std::string token;
    while ((pos = s.find(delimiter_)) != std::string::npos)
    {
        token = s.substr(0, pos);
        index.push_back(std::stoi(trim(token)));
        s.erase(0, pos + delimiter_.length());
    }
    index.push_back(std::stoi(trim(s)));
    return index;
}

void printLayerInfo(std::string layerIndex, std::string layerName, std::string layerInput,
                    std::string layerOutput, std::string weightPtr)
{
    std::cout << std::setw(6) << std::left << layerIndex << std::setw(15) << std::left << layerName;
    std::cout << std::setw(20) << std::left << layerInput << std::setw(20) << std::left
              << layerOutput;
    std::cout << std::setw(6) << std::left << weightPtr << std::endl;
}

uint64_t get3DTensorVolume(nvinfer1::Dims inputDims)
{
    assert(inputDims.nbDims == 3);
    return inputDims.d[0] * inputDims.d[1] * inputDims.d[2];
}


nvinfer1::ICudaEngine* loadTRTEngine(const std::string planFilePath, PluginFactory* pluginFactory,
                                     Logger& logger)
{
    // reading the model in memory
    std::cout << "Loading TRT Engine..." << std::endl;
    assert(fileExists(planFilePath));
    std::stringstream trtModelStream;
    trtModelStream.seekg(0, trtModelStream.beg);
    std::ifstream cache(planFilePath,std::ios::binary | std::ios::in);
    assert(cache.good());
    trtModelStream << cache.rdbuf();
    cache.close();

    // calculating model size
    trtModelStream.seekg(0, std::ios::end);
    const auto modelSize = trtModelStream.tellg();
    trtModelStream.seekg(0, std::ios::beg);
    void* modelMem = malloc(modelSize);
    trtModelStream.read((char*) modelMem, modelSize);

    nvinfer1::IRuntime* runtime = nvinfer1::createInferRuntime(logger);
    std::cout << "test................................" << std::endl;
    nvinfer1::ICudaEngine* engine
        = runtime->deserializeCudaEngine(modelMem, modelSize, pluginFactory);
    free(modelMem);
    runtime->destroy();
    std::cout << "Loading Complete!" << std::endl;

    return engine;
}

std::vector<BBoxInfo> nmsAllClasses(const float nmsThresh,
    std::vector<BBoxInfo>& binfo,
    const uint32_t numClasses,
    const std::string &model_type)
{
    std::vector<BBoxInfo> result;
    std::vector<std::vector<BBoxInfo>> splitBoxes(numClasses);
    for (auto& box : binfo)
    {
        splitBoxes.at(box.label).push_back(box);
    }

    for (auto& boxes : splitBoxes)
    {
        boxes = nonMaximumSuppression(nmsThresh, boxes);
        result.insert(result.end(), boxes.begin(), boxes.end());
    }

    return result;
}

std::vector<BBoxInfo> nonMaximumSuppression(const float nmsThresh, std::vector<BBoxInfo> binfo)
{
    auto overlap1D = [](float x1min, float x1max, float x2min, float x2max) -> float
    {
        if (x1min > x2min)
        {
            std::swap(x1min, x2min);
            std::swap(x1max, x2max);
        }
        return x1max < x2min ? 0 : std::min(x1max, x2max) - x2min;
    };
    auto computeIoU = [&overlap1D](BBox& bbox1, BBox& bbox2) -> float
    {
        float overlapX = overlap1D(bbox1.x1, bbox1.x2, bbox2.x1, bbox2.x2);
        float overlapY = overlap1D(bbox1.y1, bbox1.y2, bbox2.y1, bbox2.y2);
        float area1 = (bbox1.x2 - bbox1.x1) * (bbox1.y2 - bbox1.y1);
        float area2 = (bbox2.x2 - bbox2.x1) * (bbox2.y2 - bbox2.y1);
        float overlap2D = overlapX * overlapY;
        float u = area1 + area2 - overlap2D;
        return u == 0 ? 0 : overlap2D / u;
    };

    std::stable_sort(binfo.begin(), binfo.end(),
                     [](const BBoxInfo& b1, const BBoxInfo& b2) { return b1.prob > b2.prob; });
    std::vector<BBoxInfo> out;
    for (auto& i : binfo)
    {
        bool keep = true;
        for (auto& j : out)
        {
            if (keep)
            {
                float overlap = computeIoU(i.box, j.box);
                keep = overlap <= nmsThresh;
            }
            else
                break;
        }
        if (keep) out.push_back(i);
    }
    return out;
}

nvinfer1::ILayer* netAddUpsample(int layerIdx, std::map<std::string, std::string>& block,
                                 std::vector<float>& weights,
                                 std::vector<nvinfer1::Weights>& trtWeights, int& inputChannels,
                                 nvinfer1::ITensor* input, nvinfer1::INetworkDefinition* network)
{
    assert(block.at("type") == "upsample");
    nvinfer1::Dims inpDims = input->getDimensions();
    assert(inpDims.nbDims == 3);
   // assert(inpDims.d[1] == inpDims.d[2]);
    int n_scale = std::stoi(block.at("stride"));

    int c1 = inpDims.d[0];
    float *deval = new float[c1*n_scale*n_scale];
    for (int i = 0; i < c1*n_scale*n_scale; i++)
    {
        deval[i] = 1.0;
    }
    nvinfer1::Weights wts{ DataType::kFLOAT, deval, c1*n_scale*n_scale };
    nvinfer1::Weights bias{ DataType::kFLOAT, nullptr, 0 };
    IDeconvolutionLayer* upsample = network->addDeconvolutionNd(*input, c1, DimsHW{ n_scale, n_scale }, wts, bias);
    upsample->setStrideNd(DimsHW{ n_scale, n_scale });
    upsample->setNbGroups(c1);
    return upsample;

    #if 0
#endif
}

nvinfer1::ILayer * layer_split(const int n_layer_index_,
    nvinfer1::ITensor *input_,
    nvinfer1::INetworkDefinition* network)
{
    auto creator = getPluginRegistry()->getPluginCreator("CHUNK_TRT", "1.0");
    const nvinfer1::PluginFieldCollection* pluginData = creator->getFieldNames();
    nvinfer1::IPluginV2 *pluginObj = creator->createPlugin(("chunk" + std::to_string(n_layer_index_)).c_str(), pluginData);
    auto chunk = network->addPluginV2(&input_, 1, *pluginObj);
    return chunk;
}

nvinfer1::ILayer* netAddConvBNLeaky(int layerIdx,
                                    std::map<std::string, std::string>& block,
                                    std::vector<float>& weights,
                                    std::vector<nvinfer1::Weights>& trtWeights,
                                    int& weightPtr,
                                    int& inputChannels,
                                    nvinfer1::ITensor* input,
                                    nvinfer1::INetworkDefinition* network)
{
    assert(block.at("type") == "convolutional");
    assert(block.find("batch_normalize") != block.end());
    assert(block.at("batch_normalize") == "1");
    assert(block.at("activation") == "leaky");
    assert(block.find("filters") != block.end());
    assert(block.find("pad") != block.end());
    assert(block.find("size") != block.end());
    assert(block.find("stride") != block.end());

    bool batchNormalize, bias;
    if (block.find("batch_normalize") != block.end())
    {
        batchNormalize = (block.at("batch_normalize") == "1");
        bias = false;
    }
    else
    {
        batchNormalize = false;
        bias = true;
    }
    // all conv_bn_leaky layers assume bias is false
    assert(batchNormalize == true && bias == false);

    int filters = std::stoi(block.at("filters"));
    int padding = std::stoi(block.at("pad"));
    int kernelSize = std::stoi(block.at("size"));
    int stride = std::stoi(block.at("stride"));
    int pad;
    if (padding)
        pad = (kernelSize - 1) / 2;
    else
        pad = 0;

    /***** CONVOLUTION LAYER *****/
    /*****************************/
    // batch norm weights are before the conv layer
    // load BN biases (bn_biases)
    std::vector<float> bnBiases;
    for (int i = 0; i < filters; ++i)
    {
        bnBiases.push_back(weights[weightPtr]);
        weightPtr++;
    }
    // load BN weights
    std::vector<float> bnWeights;
    for (int i = 0; i < filters; ++i)
    {
        bnWeights.push_back(weights[weightPtr]);
        weightPtr++;
    }
    // load BN running_mean
    std::vector<float> bnRunningMean;
    for (int i = 0; i < filters; ++i)
    {
        bnRunningMean.push_back(weights[weightPtr]);
        weightPtr++;
    }
    // load BN running_var
    std::vector<float> bnRunningVar;
    for (int i = 0; i < filters; ++i)
    {
        // 1e-05 for numerical stability
        bnRunningVar.push_back(sqrt(weights[weightPtr] + 1.0e-5f));
        weightPtr++;
    }
    // load Conv layer weights (GKCRS)
    int size = filters * inputChannels * kernelSize * kernelSize;
    nvinfer1::Weights convWt{nvinfer1::DataType::kFLOAT, nullptr, size};
    float* val = new float[size];
    for (int i = 0; i < size; ++i)
    {
        val[i] = weights[weightPtr];
        weightPtr++;
    }
    convWt.values = val;
    trtWeights.push_back(convWt);
    nvinfer1::Weights convBias{nvinfer1::DataType::kFLOAT, nullptr, 0};
    trtWeights.push_back(convBias);
    nvinfer1::IConvolutionLayer* conv = network->addConvolution(
        *input,
        filters,
        nvinfer1::DimsHW{kernelSize, kernelSize},
        convWt,
        convBias);
    assert(conv != nullptr);
    std::string convLayerName = "conv_" + std::to_string(layerIdx);
    conv->setName(convLayerName.c_str());
    conv->setStride(nvinfer1::DimsHW{stride, stride});
    conv->setPadding(nvinfer1::DimsHW{pad, pad});

    /***** BATCHNORM LAYER *****/
    /***************************/
    size = filters;
    // create the weights
    nvinfer1::Weights shift{nvinfer1::DataType::kFLOAT, nullptr, size};
    nvinfer1::Weights scale{nvinfer1::DataType::kFLOAT, nullptr, size};
    nvinfer1::Weights power{nvinfer1::DataType::kFLOAT, nullptr, size};
    float* shiftWt = new float[size];
    for (int i = 0; i < size; ++i)
    {
        shiftWt[i]
            = bnBiases.at(i) - ((bnRunningMean.at(i) * bnWeights.at(i)) / bnRunningVar.at(i));
    }
    shift.values = shiftWt;
    float* scaleWt = new float[size];
    for (int i = 0; i < size; ++i)
    {
        scaleWt[i] = bnWeights.at(i) / bnRunningVar[i];
    }
    scale.values = scaleWt;
    float* powerWt = new float[size];
    for (int i = 0; i < size; ++i)
    {
        powerWt[i] = 1.0;
    }
    power.values = powerWt;
    trtWeights.push_back(shift);
    trtWeights.push_back(scale);
    trtWeights.push_back(power);
    // Add the batch norm layers
    nvinfer1::IScaleLayer* bn = network->addScale(
        *conv->getOutput(0), nvinfer1::ScaleMode::kCHANNEL, shift, scale, power);
    assert(bn != nullptr);
    std::string bnLayerName = "batch_norm_" + std::to_string(layerIdx);
    bn->setName(bnLayerName.c_str());
    /***** ACTIVATION LAYER *****/
    /****************************/
    auto leaky = network->addActivation(*bn->getOutput(0),nvinfer1::ActivationType::kLEAKY_RELU);
    leaky->setAlpha(0.1f);
    /*nvinfer1::IPlugin* leakyRELU = nvinfer1::plugin::createPReLUPlugin(0.1);
    assert(leakyRELU != nullptr);
    nvinfer1::ITensor* bnOutput = bn->getOutput(0);
    nvinfer1::IPluginLayer* leaky = network->addPlugin(&bnOutput, 1, *leakyRELU);*/
    assert(leaky != nullptr);
    std::string leakyLayerName = "leaky_" + std::to_string(layerIdx);
    leaky->setName(leakyLayerName.c_str());

    return leaky;
}


std::vector<std::string> loadListFromTextFile(const std::string filename)
{
    assert(fileExists(filename));
    std::vector<std::string> list;

    std::ifstream f(filename);
    if (!f)
    {
        std::cout << "failed to open " << filename;
        assert(0);
    }

    std::string line;
    while (std::getline(f, line))
    {
        if (line.empty())
            continue;

        else
            list.push_back(trim(line));
    }

    return list;
}
std::vector<std::string> loadImageList(const std::string filename, const std::string prefix)
{
    std::vector<std::string> fileList = loadListFromTextFile(filename);
    for (auto& file : fileList)
    {
        if (fileExists(file, false))
            continue;
        else
        {
            std::string prefixed = prefix + file;
            if (fileExists(prefixed, false))
                file = prefixed;
            else
                std::cerr << "WARNING: couldn't find: " << prefixed
                          << " while loading: " << filename << std::endl;
        }
    }
    return fileList;
}