yolo v2文件结构和源码的简单梳理

前言

在2017年6月30日，yolo在github上的源码进行了一次更新，有许多文件的位置都改变了。导致我在使用yolo v2训练自己的数据的时候遇到了许多麻烦（网上给的许多解决方案都不太完美）。在这次经历中，我充分认识到了了解源码对于成功训练自己数据的重要作用，所以我决定要把yolo v2的代码为大家简单梳理一下，便于大家对于yolo官网上训练方法的理解。（因为是代码梳理，所以对源码就不一一解释了）

分析过程

首先我们从yolo的训练命令开始分析（yolo的源码是用c++写的）：

./darknet detector train cfg/voc.data cfg/yolo-voc.cfg darknet19_448.conv.23 

从这里我们可以看出yolo主函数main中的参数argv[]在其中对应的值分别是 argv[0] -> darknet argv[1] -> detector argv[2] -> train .....（剩下的自己看），从这里我们可以看出，yolo主函数main一定在examples/darknet.c中,让我们来看一下主函数：

int main(int argc, char **argv){    //test_resize("data/bad.jpg");    //test_box();    //test_convolutional_layer();    if(argc < 2){        fprintf(stderr, "usage: %s <function>\n", argv[0]);        return 0;    }    gpu_index = find_int_arg(argc, argv, "-i", 0);    if(find_arg(argc, argv, "-nogpu")) {        gpu_index = -1;    }#ifndef GPU    gpu_index = -1;#else    if(gpu_index >= 0){        cuda_set_device(gpu_index);    }#endif    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], "voxel")){        run_voxel(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);    } else if (0 == strcmp(argv[1], "detect")){        float thresh = find_float_arg(argc, argv, "-thresh", .24);        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], "vid")){        run_vid_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], "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], "compare")){        run_compare(argc, argv);    } else if (0 == strcmp(argv[1], "dice")){        run_dice(argc, argv);    } else if (0 == strcmp(argv[1], "writing")){        run_writing(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], "captcha")){        run_captcha(argc, argv);    } 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], "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 {        fprintf(stderr, "Not an option: %s\n", argv[1]);    }    return 0;}

很简单可以看出，主函数就是对于参数argv[1]的一个判断，根据argv[1]的内容来启动不同的程序。让我们继续跟着训练命令走argv[1] = detector时，调用的函数是run_detector，而这个函数在examples/detector.c的最后，让我们再来看看这个函数吧：

void run_detector(int argc, char **argv){    char *prefix = find_char_arg(argc, argv, "-prefix", 0);    float thresh = find_float_arg(argc, argv, "-thresh", .24);    float hier_thresh = find_float_arg(argc, argv, "-hier", .5);    int cam_index = find_int_arg(argc, argv, "-c", 0);    int frame_skip = find_int_arg(argc, argv, "-s", 0);    int avg = find_int_arg(argc, argv, "-avg", 3);    if(argc < 4){        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);        return;    }    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);    char *outfile = find_char_arg(argc, argv, "-out", 0);    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;    }    int clear = find_arg(argc, argv, "-clear");    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);    int fps = find_int_arg(argc, argv, "-fps", 0);    char *datacfg = argv[3];    char *cfg = argv[4];    char *weights = (argc > 5) ? argv[5] : 0;    char *filename = (argc > 6) ? argv[6]: 0;    if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, outfile, fullscreen);    else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);    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);    else if(0==strcmp(argv[2], "recall")) validate_detector_recall(cfg, weights);    else if(0==strcmp(argv[2], "demo")) {        list *options = read_data_cfg(datacfg);        int classes = option_find_int(options, "classes", 2);        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);    }}

在这里 run_detector的主要作用还是在根据argv[]的值执行不同的函数，其他关于gpu啊，threshold啊之类的我们都可以不用管，这里最重要的是argv[2]的值，根据其值的不同，执行不同函数，这里的test_detector,train_detector这些函数在detector.c中都有定义，并且从名字上我们就可以看出这些函数是干什么的。这里我们依旧跟随之前的训练命令，argv[2] = train，这里让我们来看一下train_detector函数（注：这里是我修改过一部分的，不是原来的代码）：

void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear){    list *options = read_data_cfg(datacfg);    char *train_images = option_find_str(options, "train", "scripts/train.txt");//训练集路径    char *backup_directory = option_find_str(options, "backup", "/backup/");//备份训练结果路径    srand(time(0));    char *base = basecfg(cfgfile);    printf("%s\n", base);    float avg_loss = -1;    network *nets = calloc(ngpus, sizeof(network));    srand(time(0));    int seed = rand();    int i;    for(i = 0; i < ngpus; ++i){        srand(seed);#ifdef GPU        cuda_set_device(gpus[i]);#endif        nets[i] = load_network(cfgfile, weightfile, clear);//载入网络        nets[i].learning_rate *= ngpus;    }    srand(time(0));    network net = nets[0];    int imgs = net.batch * net.subdivisions * ngpus;    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);    data train, buffer;    layer l = net.layers[net.n - 1];    int classes = l.classes;    float jitter = l.jitter;    list *plist = get_paths(train_images);    //int N = plist->size;    char **paths = (char **)list_to_array(plist);    load_args args = {0};    args.w = net.w;    args.h = net.h;    args.paths = paths;    args.n = imgs;    args.m = plist->size;    args.classes = classes;    args.jitter = jitter;    args.num_boxes = l.max_boxes;    args.d = &buffer;    args.type = DETECTION_DATA;    args.threads = 8;    args.angle = net.angle;    args.exposure = net.exposure;    args.saturation = net.saturation;    args.hue = net.hue;    pthread_t load_thread = load_data(args);    clock_t time;    int count = 0;    //while(i*imgs < N*120){    while(get_current_batch(net) < net.max_batches){        if(l.random && count++%10 == 0){            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);            for(i = 0; i < ngpus; ++i){                resize_network(nets + i, dim, dim);            }            net = nets[0];        }        time=clock();        pthread_join(load_thread, 0);        train = buffer;        load_thread = load_data(args);        /*        int k;        for(k = 0; k < l.max_boxes; ++k){            box b = float_to_box(train.y.vals[10] + 1 + k*5);            if(!b.x) break;            printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);        }        */        /*        int zz;        for(zz = 0; zz < train.X.cols; ++zz){            image im = float_to_image(net.w, net.h, 3, train.X.vals[zz]);            int k;            for(k = 0; k < l.max_boxes; ++k){                box b = float_to_box(train.y.vals[zz] + k*5);                printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);                draw_bbox(im, b, 1, 1,0,0);            }            show_image(im, "truth11");            cvWaitKey(0);            save_image(im, "truth11");        }        */        printf("Loaded: %lf seconds\n", sec(clock()-time));        time=clock();        float loss = 0;#ifdef GPU        if(ngpus == 1){            loss = train_network(net, train);        } else {            loss = train_networks(nets, ngpus, train, 4);        }#else        loss = train_network(net, train);#endif        if (avg_loss < 0) avg_loss = loss;        avg_loss = avg_loss*.9 + loss*.1;        i = get_current_batch(net);        printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);        if(i%1000==0){#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);        }        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);}

这里我们主要重视的函数是第7行的read_data_cfg，第8行的train_images，第9行的backup_directory和第25行的load_network函数：

read_data_cfg中的参数datacfg在run_detector中可以看出就是arg[3]，在本例中对应的就是voc.data

train_images是用来指定所要训练的图片集的路径的。

backup_directory是用来指定训练出来的权值的路劲的。

而load_network是用来载入所要训练的网络结构和参数的，这里run_detector中可以看出load_network的参数之一cfgfile就是argv[4]，在我们这个例子中也便就是yolo-voc.cfg

这里我们先看一下cfg/voc.data（注：这里是我修改过了的，不是原来的）

classes= 2train  = /home/iair339-04/darknet/scripts/train.txtvalid  = /home/iair339-04/darknet/scripts/2007_test.txtnames = data/kitti.namesbackup = backup

这里可以看出voc.data是用来指定类别数classes，训练集路径train，测试集路径valid和类别名称names和备份文件路径backup的（so easy）。

接下来我们来看一下yolo-voc.cfg文件（注：修改过）

net]# Testing#batch=1#subdivisions=1# Training batch=64 subdivisions=8height=416width=416channels=3momentum=0.9decay=0.0005angle=0saturation = 1.5exposure = 1.5hue=.1learning_rate=0.001burn_in=1000max_batches = 80200policy=stepssteps=40000,60000scales=.1,.1[convolutional]batch_normalize=1filters=32size=3stride=1pad=1activation=leaky[maxpool]size=2stride=2[convolutional]batch_normalize=1filters=64size=3stride=1pad=1activation=leaky[maxpool]size=2stride=2[convolutional]batch_normalize=1filters=128size=3stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=64size=1stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=128size=3stride=1pad=1activation=leaky[maxpool]size=2stride=2[convolutional]batch_normalize=1filters=256size=3stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=128size=1stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=256size=3stride=1pad=1activation=leaky[maxpool]size=2stride=2[convolutional]batch_normalize=1filters=512size=3stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=256size=1stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=512size=3stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=256size=1stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=512size=3stride=1pad=1activation=leaky[maxpool]size=2stride=2[convolutional]batch_normalize=1filters=1024size=3stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=512size=1stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=1024size=3stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=512size=1stride=1pad=1activation=leaky[convolutional]batch_normalize=1filters=1024size=3stride=1pad=1activation=leaky#######[convolutional]batch_normalize=1size=3stride=1pad=1filters=1024activation=leaky[convolutional]batch_normalize=1size=3stride=1pad=1filters=1024activation=leaky[route]layers=-9[convolutional]batch_normalize=1size=1stride=1pad=1filters=64activation=leaky[reorg]stride=2[route]layers=-1,-4[convolutional]batch_normalize=1size=3stride=1pad=1filters=1024activation=leaky[convolutional]size=1stride=1pad=1filters=35#此处修改activation=linear[region]anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071bias_match=1classes=2#此处修改种类coords=4num=5softmax=1jitter=.3rescore=1object_scale=5noobject_scale=1class_scale=1coord_scale=1absolute=1thresh = .6random=1

这里[net]里面是网络的超参数的设置，而之后的便是yolo v2的网络结构了。

结语

这里只是通过训练命令来简单的分析一下yolo v2是怎么工作的，主要是为了让大家训练自己数据时能更加方便，如果想要详细了解yolo源码，还需要大家自己认真阅读源码了。