yolov2源码分析

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= 2
train  = /home/iair339-04/darknet/scripts/train.txt
valid  = /home/iair339-04/darknet/scripts/2007_test.txt
names = data/kitti.names
backup = 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=8
height=416
width=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

learning_rate=0.001
burn_in=1000
max_batches = 80200
policy=steps
steps=40000,60000
scales=.1,.1

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

#######

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[route]
layers=-9

[convolutional]
batch_normalize=1
size=1
stride=1
pad=1
filters=64
activation=leaky

[reorg]
stride=2

[route]
layers=-1,-4

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=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, 10.0071
bias_match=1
classes=2    #此处修改种类
coords=4
num=5
softmax=1
jitter=.3
rescore=1

object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1

absolute=1
thresh = .6
random=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。