common.hpp

#ifndef YOLOV5_COMMON_H_
#define YOLOV5_COMMON_H_

#include <iostream>s
#include <fstream>
#include <map>
#include <sstream>
#include <vector>
#include <opencv2/opencv.hpp>
#include "dirent.h"
#include "NvInfer.h"
#include <chrono>

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

using namespace nvinfer1;

// TensorRT weight files have a simple space delimited format:
// [type] [size] <data x size in hex>
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;
}

IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
    float *gamma = (float*)weightMap[lname + ".weight"].values;
    float *beta = (float*)weightMap[lname + ".bias"].values;
    float *mean = (float*)weightMap[lname + ".running_mean"].values;
    float *var = (float*)weightMap[lname + ".running_var"].values;
    int len = weightMap[lname + ".running_var"].count;

    float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
    for (int i = 0; i < len; i++) {
        scval[i] = gamma[i] / sqrt(var[i] + eps);
    }
    Weights scale{DataType::kFLOAT, scval, len};
    
    float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
    for (int i = 0; i < len; i++) {
        shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
    }
    Weights shift{DataType::kFLOAT, shval, len};

    float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
    for (int i = 0; i < len; i++) {
        pval[i] = 1.0;
    }
    Weights power{DataType::kFLOAT, pval, len};

    weightMap[lname + ".scale"] = scale;
    weightMap[lname + ".shift"] = shift;
    weightMap[lname + ".power"] = power;
    IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
    assert(scale_1);
    return scale_1;
}

ILayer* convBnLeaky(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int outch, int ksize, int s, int g, std::string lname, std::string bnname, bool bias = true) {
	Weights emptywts{DataType::kFLOAT, nullptr, 0};
	int p = ksize / 2;
	IConvolutionLayer* conv1 = nullptr;
	if (bias)
	{
		conv1 = network->addConvolutionNd(input, outch, DimsHW{ ksize, ksize }, weightMap[lname + ".weight"], weightMap[lname + ".bias"]);
	}
	else
	{
		conv1 = network->addConvolutionNd(input, outch, DimsHW{ ksize, ksize }, weightMap[lname + ".weight"], emptywts);
	}
    assert(conv1);
    conv1->setStrideNd(DimsHW{s, s});
    conv1->setPaddingNd(DimsHW{p, p});
    conv1->setNbGroups(g);
	//IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + ".bn", 1e-4);
    IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname.substr(0, lname.find_last_of(".")) + bnname, 1e-5);
    auto lr = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
    lr->setAlpha(0.1);
    return lr;
}


IActivationLayer* basicBlock(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname) {
	Weights emptywts{ DataType::kFLOAT, nullptr, 0 };

	IConvolutionLayer* conv1 = network->addConvolution(input, outch, DimsHW{ 3, 3 }, weightMap[lname + "conv1.weight"], emptywts);
	assert(conv1);
	conv1->setStride(DimsHW{ stride, stride });
	conv1->setPadding(DimsHW{ 1, 1 });

	IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn1", 1e-5);

	IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
	assert(relu1);

	IConvolutionLayer* conv2 = network->addConvolution(*relu1->getOutput(0), outch, DimsHW{ 3, 3 }, weightMap[lname + "conv2.weight"], emptywts);
	assert(conv2);
	conv2->setPadding(DimsHW{ 1, 1 });

	IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "bn2", 1e-5);

	IElementWiseLayer* ew1;
	if (inch != outch) {
		IConvolutionLayer* conv3 = network->addConvolution(input, outch, DimsHW{ 1, 1 }, weightMap[lname + "downsample.0.weight"], emptywts);
		assert(conv3);
		conv3->setStride(DimsHW{ stride, stride });
		IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "downsample.1", 1e-5);
		ew1 = network->addElementWise(*bn3->getOutput(0), *bn2->getOutput(0), ElementWiseOperation::kSUM);
	}
	else {
		ew1 = network->addElementWise(input, *bn2->getOutput(0), ElementWiseOperation::kSUM);
	}
	IActivationLayer* relu2 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
	assert(relu2);
	return relu2;
}

int read_files_in_dir(const char *p_dir_name, std::vector<std::string> &file_names) {
    DIR *p_dir = opendir(p_dir_name);
    if (p_dir == nullptr) {
        return -1;
    }

    struct dirent* p_file = nullptr;
    while ((p_file = readdir(p_dir)) != nullptr) {
        if (strcmp(p_file->d_name, ".") != 0 &&
                strcmp(p_file->d_name, "..") != 0) {
            //std::string cur_file_name(p_dir_name);
            //cur_file_name += "/";
            //cur_file_name += p_file->d_name;
            std::string cur_file_name(p_file->d_name);
            file_names.push_back(cur_file_name);
        }
    }
    closedir(p_dir);
    return 0;
}

#endif