YOLOv1源码讲解

1、首先就是从darknet.c中的主函数开始运行，darkne.c中含有多种功能函数，包括了目标检测、语义分割等函数，这里主要讲YOLOv1相关代码，也就是目标检测代码，如下所示：

//通过接收外界参数来选择使用哪种功能函数
int main(int argc, char **argv)
{
    //test_resize("data/bad.jpg");
    //test_box();
    //test_convolutional_layer();
    //这里若是输入参数少于2，则输出usage:argv[0] ，这里的argv[0]就是可执行文件地址+文件名
    //如果没有任何命令行参数，则打印一句提示信息：Usage: [应用程序名称] ，即告诉你要指定一个命令行参数。
    if(argc < 2){
        fprintf(stderr, "usage: %s \n", argv[0]);
        return 0;
    }
    //选择GPU的目录，即选择第几个GPU
    gpu_index = find_int_arg(argc, argv, "-i", 0);
    //若是出现-nogpu的参数则说明不使用GPU， gpu_index = -1
    if(find_arg(argc, argv, "-nogpu")) {
        gpu_index = -1;
    }
//条件编译，若是未定义GPU，则gpu_index=-1;若是定义了GPU，但前面没有选择使用第几个，则默认gpu_index=0我
//的默认gpu_index=0，如果前面设置了gpu_index=1，则会对第二个GPU进行配置，但绝不能大于你的GPU目录号，
//比如我有3个GPU，则我的目录号就是0、1、2；不能超出2。至于如何配置，就是CUDA编程的内容，这里不再讲解，
//有兴趣的可以学习一下cuda编程。
#ifndef GPU
    gpu_index = -1;
#else
    if(gpu_index >= 0){
        cuda_set_device(gpu_index);
    }
#endif

	//这里就是根据输入参数来选择需要的功能函数，咱们这里使用的是目标检测功能函数 run_detector，
	//也可以选择run_yolo，这里只是讲解 run_detector函数，其他不再详解
    if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "yolo")){
        run_yolo(argc, argv);
    } else if (0 == strcmp(argv[1], "super")){
        run_super(argc, argv);
    } else if (0 == strcmp(argv[1], "lsd")){
        run_lsd(argc, argv);
    } else if (0 == strcmp(argv[1], "detector")){
        run_detector(argc, argv);//从此处进入run_detector函数
    } else if (0 == strcmp(argv[1], "detect")){
        float thresh = find_float_arg(argc, argv, "-thresh", .5);
        char *filename = (argc > 4) ? argv[4]: 0;
        char *outfile = find_char_arg(argc, argv, "-out", 0);
        int fullscreen = find_arg(argc, argv, "-fullscreen");
        test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, .5, outfile, fullscreen);
    } else if (0 == strcmp(argv[1], "cifar")){
        run_cifar(argc, argv);
    } else if (0 == strcmp(argv[1], "go")){
        run_go(argc, argv);
    } else if (0 == strcmp(argv[1], "rnn")){
        run_char_rnn(argc, argv);
    } else if (0 == strcmp(argv[1], "coco")){
        run_coco(argc, argv);
    } else if (0 == strcmp(argv[1], "classify")){
        predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
    } else if (0 == strcmp(argv[1], "classifier")){
        run_classifier(argc, argv);
    } else if (0 == strcmp(argv[1], "regressor")){
        run_regressor(argc, argv);
    } else if (0 == strcmp(argv[1], "isegmenter")){
        run_isegmenter(argc, argv);
    } else if (0 == strcmp(argv[1], "segmenter")){
        run_segmenter(argc, argv);
    } else if (0 == strcmp(argv[1], "art")){
        run_art(argc, argv);
    } else if (0 == strcmp(argv[1], "tag")){
        run_tag(argc, argv);
    } else if (0 == strcmp(argv[1], "3d")){
        composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
    } else if (0 == strcmp(argv[1], "test")){
        test_resize(argv[2]);
    } else if (0 == strcmp(argv[1], "nightmare")){
        run_nightmare(argc, argv);
    } else if (0 == strcmp(argv[1], "rgbgr")){
        rgbgr_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "reset")){
        reset_normalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "denormalize")){
        denormalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "statistics")){
        statistics_net(argv[2], argv[3]);
    } else if (0 == strcmp(argv[1], "normalize")){
        normalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "rescale")){
        rescale_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "ops")){
        operations(argv[2]);
    } else if (0 == strcmp(argv[1], "speed")){
        speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
    } else if (0 == strcmp(argv[1], "oneoff")){
        oneoff(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "oneoff2")){
        oneoff2(argv[2], argv[3], argv[4], atoi(argv[5]));
    } else if (0 == strcmp(argv[1], "print")){
        print_weights(argv[2], argv[3], atoi(argv[4]));
    } else if (0 == strcmp(argv[1], "partial")){
        partial(argv[2], argv[3], argv[4], atoi(argv[5]));
    } else if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "visualize")){
        visualize(argv[2], (argc > 3) ? argv[3] : 0);
    } else if (0 == strcmp(argv[1], "mkimg")){
        mkimg(argv[2], argv[3], atoi(argv[4]), atoi(argv[5]), atoi(argv[6]), argv[7]);
    } else if (0 == strcmp(argv[1], "imtest")){
        test_resize(argv[2]);
    }
	else if (0 == strcmp(argv[1], "mseg")) {
		run_mseg(argc, argv);
	}
	else {
        fprintf(stderr, "Not an option: %s\n", argv[1]);
    }
    return 0;
}

2、从主函数跳转到run_detector函数

void run_detector(int argc, char **argv)
{
	//检查是否有参数prefix，默认值是0
    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
    //检查是否有参数thresh参数，thresh为输出的阈值，默认值是0.24
    float thresh = find_float_arg(argc, argv, "-thresh", .5);
    //检查是否有参数hier，默认值是0.5,只在yolov2中使用
    float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
    //检查是否有参数cam_index,默认为0，使用摄像头的时候使用
    int cam_index = find_int_arg(argc, argv, "-c", 0);
    //检查是否有参数frame_skip,默认为0
    int frame_skip = find_int_arg(argc, argv, "-s", 0);
    //检查是否有参数avg,默认为3
    int avg = find_int_arg(argc, argv, "-avg", 3);
    //如果输入参数小于4个，输出正确的命令格式：[应用程序名称][train/test/valid][cfg][weights (optional)]
    if(argc < 4){
        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
        return;
    }
     //检查是否指定GPU运算，这里可以设置多个GPU，如-gpus 0,1,2
    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
    //检测是否有输出文件
    char *outfile = find_char_arg(argc, argv, "-out", 0);
    //为所有的GPU分配空间
    int *gpus = 0;
    int gpu = 0;
    int ngpus = 0;
    if(gpu_list){
        printf("%s\n", gpu_list);
        int len = strlen(gpu_list);
        ngpus = 1;
        int i;
        for(i = 0; i < len; ++i){
            if (gpu_list[i] == ',') ++ngpus;
        }
        gpus = calloc(ngpus, sizeof(int));
        for(i = 0; i < ngpus; ++i){
            gpus[i] = atoi(gpu_list);
            gpu_list = strchr(gpu_list, ',')+1;
        }
    } else {
        gpu = gpu_index;
        gpus = &gpu;
        ngpus = 1;
    }
	//检查clear参数
    int clear = find_arg(argc, argv, "-clear");
    //检测fullscreen参数
    int fullscreen = find_arg(argc, argv, "-fullscreen");
    int width = find_int_arg(argc, argv, "-w", 0);
    int height = find_int_arg(argc, argv, "-h", 0);
    //检查帧率
    int fps = find_int_arg(argc, argv, "-fps", 0);
    //int class = find_int_arg(argc, argv, "-class", 0);
	
    //data文件的路径存为argv数组的第四个元素
    char *datacfg = argv[3];
    //cfg文件的路径存为argv数组的第五个元素
    char *cfg = argv[4];
    //当参数大于5个时，权重为argv数组的第六个元素的内容
    char *weights = (argc > 5) ? argv[5] : 0;
    //当参数大于6个时，权重为argv数组的第七个元素的内容，即示例中需检测图片的路径
    char *filename = (argc > 6) ? argv[6]: 0;
    //根据第三个参数（即argv[2]）的内容，调用不同的函数，并传入datacfg,cfg等参数
    //根据需要选择要进入的函数
    //测试
    if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen);
    //训练
    else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);
    //验证
    else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
    else if(0==strcmp(argv[2], "valid2")) validate_detector_flip(datacfg, cfg, weights, outfile);
    //计算recall
    else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);
    //可视化demo
    else if(0==strcmp(argv[2], "demo")) {
        list *options = read_data_cfg(datacfg);
        int classes = option_find_int(options, "classes", 20);
        char *name_list = option_find_str(options, "names", "data/names.list");
        char **names = get_labels(name_list);
        demo(cfg, weights, thresh, cam_index, filename, names, classes, frame_skip, prefix, avg, hier_thresh, width, height, fps, fullscreen);
    }
    //else if(0==strcmp(argv[2], "extract")) extract_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
    //else if(0==strcmp(argv[2], "censor")) censor_detector(datacfg, cfg, weights, cam_index, filename, class, thresh, frame_skip);
}

3、这里先选择训练train_detector

void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
    list *options = read_data_cfg(datacfg);//读取datacfg文件，具体可以查看博客(https://blog.csdn.net/m0_37799466/article/details/105956850)

	//获取训练图像文件和模型保存地址
    char *train_images = option_find_str(options, "train", "data/train.list");
    char *backup_directory = option_find_str(options, "backup", "/backup/");

    srand(time(0));//用当前时间来设定rand函数所用的随机数产生演算法的种子值
    char *base = basecfg(cfgfile);//获取cfg文件名
    printf("%s\n", base);
    float avg_loss = -1;
    network **nets = calloc(ngpus, sizeof(network));//根据GPU数量申请内存空间，申请后，对空间逐一进行初始化，并设置值为0;

    srand(time(0));
    int seed = rand();
    int i;
    
    for(i = 0; i < ngpus; ++i)
    {
        srand(seed);
#ifdef GPU
        cuda_set_device(gpus[i]);//根据GPU数量，对每个GPU进行cuda编程设置
#endif
        nets[i] = load_network(cfgfile, weightfile, clear);//为每一个GPU加载网络模型，具体参考[博客](https://blog.csdn.net/m0_37799466/article/details/105979739)
        nets[i]->learning_rate *= ngpus;//学习率需要根据GPU数量进行调节，依次在每个GPU上增大GPU数量相同的倍数；
    }
    srand(time(0));
    network *net = nets[0];//令network指针net指向第一个GPU中的网络，如果只有一个GPU则只有nets[0];

    int imgs = net->batch * net->subdivisions * ngpus;//一个batch的图像数量，即所有的GPU上的图像，没有GPU上有subdivisions个min_batch
    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);//输出学习率和衰减率
    data train, buffer;//声明两个data结构体train和buffer，用来存放训练数据
    /*
    typedef struct{
   			 		int w, h;
    				matrix X;
    				matrix y;
    				int shallow;
    				int *num_boxes;
    				box **boxes;
					} data;
	*/

    layer l = net->layers[net->n - 1];//定义一个layer结构体并使用网络最后一层进行初始化

    int classes = l.classes;//类别数
    float jitter = l.jitter;//通过抖动增加噪声来抑制过拟合

    list *plist = get_paths(train_images);//定义list指针plist并令它指向保存训练图像的地址单元
    /*在定义char指针时，其输出的指针地址就是char的字符，而其指针指向单元的变量则是其ASCII表对应的十进制值
    且当指向数组是会将数组后面的字符串都输出打印出来
	list *get_paths(char *filename)
	{
    	char *path;
    	FILE *file = fopen(filename, "r");
    	if(!file) file_error(filename);
    	list *lines = make_list();
    	while((path=fgetl(file))){
        	list_insert(lines, path);
    	}
    	fclose(file);
    	return lines;
	}

	*/
    //int N = plist->size;
    char **paths = (char **)list_to_array(plist);//定义了一个指向char指针的指针，其char指针指向第一个训练图像地址
    /*返回一个指向指针的指针
	void **list_to_array(list *l)
	{
    	void **a = calloc(l->size, sizeof(void*));
    	int count = 0;
    	node *n = l->front;
    	while(n){
        	a[count++] = n->val;
        	n = n->next;
    	}
    	return a;
	}

	*/

    load_args args = get_base_args(net);//定义一个load_args结构体args用来保存训练参数
    /*
    load_args get_base_args(network *net)
	{
    load_args args = {0};定义一个load_args结构体args并初始化为0
    args.w = net->w;设置网络输入图像宽高
    args.h = net->h;
    args.size = net->w;//设置图像尺寸

    args.min = net->min_crop;设置裁剪
    args.max = net->max_crop;
    args.angle = net->angle;设置旋转角度
    args.aspect = net->aspect;设置层
    args.exposure = net->exposure;设置曝光度
    args.center = net->center;设置中心
    args.saturation = net->saturation;设置饱和度
    args.hue = net->hue;设置色调
    return args;
	}
	//load_args结构体
	typedef struct load_args{
    int threads;
    char **paths;
    char *path;
    int n;
    int m;
    char **labels;
    int h;
    int w;
    int out_w;
    int out_h;
    int nh;
    int nw;
    int num_boxes;
    int min, max, size;
    int classes;
    int background;
    int scale;
    int center;
    int coords;
    float jitter;
    float angle;
    float aspect;
    float saturation;
    float exposure;
    float hue;
    data *d;
    image *im;
    image *resized;
    data_type type;
    tree *hierarchy;
	} load_args;
	*/
    
    args.coords = l.coords;//设置定位损失项前项因子
    args.paths = paths;//设置训练图像地址
    args.n = imgs;//设置多少图像更新一次参数
    args.m = plist->size;//所有的训练图像数量
    args.classes = classes;//设置类别
    args.jitter = jitter;//设置噪声
    args.num_boxes = l.max_boxes;//表示一张图像中允许的最大的检测框的数量，yolo1时需要设置为7
    args.d = &buffer;//令指针指向buffer
    args.type = DETECTION_DATA;//设置加载数据方式，yolo1时需要设置为REGION_DATA
    //args.type = INSTANCE_DATA;
    args.threads = 64;//设置线程

    pthread_t load_thread = load_data(args);//加载训练数据，具体参考[博客](https://blog.csdn.net/m0_37799466/article/details/106102059)
   
    double time;//声明时间
    int count = 0;//定义计数并初始化为0
    //while(i*imgs < N*120){
    while(get_current_batch(net) < net->max_batches){
        if(l.random && count++%10 == 0){//yolo1不会执行，yolo3时会使用
            printf("Resizing\n");
            int dim = (rand() % 10 + 10) * 32;
            if (get_current_batch(net)+200 > net->max_batches) dim = 608;
            //int dim = (rand() % 4 + 16) * 32;
            printf("%d\n", dim);
            args.w = dim;
            args.h = dim;

            pthread_join(load_thread, 0);
            train = buffer;
            free_data(train);
            load_thread = load_data(args);

            #pragma omp parallel for
            for(i = 0; i < ngpus; ++i){
                resize_network(nets[i], dim, dim);
            }
            net = nets[0];
        }
        time=what_time_is_it_now();//当前时间
        pthread_join(load_thread, 0);//等待一个线程的结束,线程间同步的操作
        train = buffer;//将buffer保存的训练数据赋给train，所有线程加载的数据，即一个大的batch
        load_thread = load_data(args);//继续加载数据

        

        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);

        time=what_time_is_it_now();
        float loss = 0;//定义损失值并初始化为0
#ifdef GPU
        if(ngpus == 1){//根据GPU数量选择训练函数
            loss = train_network(net, train);//核心函数，训练网络函数，单GPU使用，这里着重讲解这个[博客](https://blog.csdn.net/m0_37799466/article/details/106192940)
        } else {
            loss = train_networks(nets, ngpus, train, 4);//多GPU训练
        }
#else
        loss = train_network(net, train);//使用CPU训练时运行
#endif
        if (avg_loss < 0) avg_loss = loss;//如果平均损失小于0，则将损失值赋给它
        avg_loss = avg_loss*.9 + loss*.1;//平均损失=上批训练数据平均损失值*0.9+这批训练数据损失值*0.1；avg_loss初始值为-1

        i = get_current_batch(net);// 获取当前的迭代次数
        printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, i*imgs);
        if(i%100==0){//每100次保存一次权重
#ifdef GPU
            if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
            char buff[256];
            sprintf(buff, "%s/%s.backup", backup_directory, base);
            save_weights(net, buff);//保存权重文件
        }
        if(i%10000==0 || (i < 1000 && i%100 == 0)){
#ifdef GPU
            if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
            char buff[256];
            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
            save_weights(net, buff);
        }
        free_data(train);
    }
#ifdef GPU
    if(ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
    char buff[256];
    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
    save_weights(net, buff);
}