Darknet框架分析主线

分析主线的确定

Darknet相比当前训练的C/C++主流框架（如Caffe）来讲，具有编译速度快，依赖少，易部署等众多优点，我们先定位到src/darknet.c里面的main函数，这是这个框架实现分类，定位，回归，分割等功能的初始入口。这一节的核心代码如下，注意一下就是run_yolo只提供了yolo目标检测算法的原始实现。而run_detector函数提供了AlexeyAB添加了各种新特性的目标检测算法，所以之后我们会从这个函数跟进去来解析Darknet框架。Darknet提供的其他功能如run_super（高分辨率重建），run_classifier（图像分类），run_char_rnn（RNN文本识别）有兴趣可以自己去读（这个框架用来做目标检测比较好，其他算法建议还是去其它框架实现吧），本系列只讲目标检测。

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], "detector")){
       run_detector(argc, argv);
   } else if (0 == strcmp(argv[1], "detect")){
       float thresh = find_float_arg(argc, argv, "-thresh", .24);
	int ext_output = find_arg(argc, argv, "-ext_output");
       char *filename = (argc > 4) ? argv[4]: 0;
       test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0, 0);
   } 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], "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], "partial")){
       partial(argv[2], argv[3], argv[4], atoi(argv[5]));
   } else if (0 == strcmp(argv[1], "visualize")){
       visualize(argv[2], (argc > 3) ? argv[3] : 0);
   } else if (0 == strcmp(argv[1], "imtest")){
       test_resize(argv[2]);
   } else {
       fprintf(stderr, "Not an option: %s\n", argv[1]);
   }

跟进run_detector

run_detector函数在src/detector.c里面，这个函数首先有很多超参数可以设置，然后我们可以看到这个函数包含了训练验证，测试，计算Anchors，demo展示，计算map值和recall值等功能。由于训练，测试，验证阶段差不多，我们跟进去一个看看就好，至于后面那几个功能是AlexeyAB添加的，之后再逐一解释。

void run_detector(int argc, char **argv)
{
    int dont_show = find_arg(argc, argv, "-dont_show");//展示图像界面
    int benchmark = find_arg(argc, argv, "-benchmark");//评估模型的表现
    int benchmark_layers = find_arg(argc, argv, "-benchmark_layers");
    //if (benchmark_layers) benchmark = 1;
    if (benchmark) dont_show = 1;
    int show = find_arg(argc, argv, "-show");
    int letter_box = find_arg(argc, argv, "-letter_box");//是否对图像做letter-box变换
    int calc_map = find_arg(argc, argv, "-map");//是否计算map值
    int map_points = find_int_arg(argc, argv, "-points", 0);
    check_mistakes = find_arg(argc, argv, "-check_mistakes");//检查数据是否有误
    int show_imgs = find_arg(argc, argv, "-show_imgs");//显示图片
    int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
    int json_port = find_int_arg(argc, argv, "-json_port", -1);
    char *http_post_host = find_char_arg(argc, argv, "-http_post_host", 0);
    int time_limit_sec = find_int_arg(argc, argv, "-time_limit_sec", 0);
    char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
    char *outfile = find_char_arg(argc, argv, "-out", 0);
    char *prefix = find_char_arg(argc, argv, "-prefix", 0);//模型保存的前缀
    float thresh = find_float_arg(argc, argv, "-thresh", .25);    // 置信度
    float iou_thresh = find_float_arg(argc, argv, "-iou_thresh", .5);    // 0.5 for mAP
    float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
    int cam_index = find_int_arg(argc, argv, "-c", 0);//摄像头编号
    int frame_skip = find_int_arg(argc, argv, "-s", 0);//跳帧检测间隔
    int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
    int width = find_int_arg(argc, argv, "-width", -1);// 输入网络的图像宽度
    int height = find_int_arg(argc, argv, "-height", -1);// 输入网络的图像高度
    // extended output in test mode (output of rect bound coords)
    // and for recall mode (extended output table-like format with results for best_class fit)
    int ext_output = find_arg(argc, argv, "-ext_output");
    int save_labels = find_arg(argc, argv, "-save_labels");
    if (argc < 4) {
        fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [cfg] [weights (optional)]\n", argv[0], argv[1]);
        return;
    }
    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);// 多个gpu训练
    int *gpus = 0;
    int gpu = 0;
    int ngpus = 0;
    if (gpu_list) {
        printf("%s\n", gpu_list);
        int len = (int)strlen(gpu_list);
        ngpus = 1;
        int i;
        for (i = 0; i < len; ++i) {
            if (gpu_list[i] == ',') ++ngpus;
        }
        gpus = (int*)xcalloc(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");

char *datacfg = argv[3];//存储训练集，验证集，以及类别对应名字等信息的cfg文件
    char *cfg = argv[4];//要训练的网络cfg文件
    char *weights = (argc > 5) ? argv[5] : 0;//是否有预训练模型
    if (weights)
        if (strlen(weights) > 0)
            if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
    char *filename = (argc > 6) ? argv[6] : 0;
    if (0 == strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show, ext_output, save_labels, outfile, letter_box, benchmark_layers);//执行目标检测模型测试
    else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs, benchmark_layers);//目标检测模型训练
    else if (0 == strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);//目标检测模型验证
    else if (0 == strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);///计算验证集的召回率
    else if (0 == strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh, iou_thresh, map_points, letter_box, NULL);//计算验证集的map值
    else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);//计算验证集的anchors
    else if (0 == strcmp(argv[2], "demo")) {//demo展示
        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);
        if (filename)
            if (strlen(filename) > 0)
                if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
        demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
            mjpeg_port, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host, benchmark, benchmark_layers);

free_list_contents_kvp(options);
        free_list(options);
    }
    else printf(" There isn't such command: %s", argv[2]);

if (gpus && gpu_list && ngpus > 1) free(gpus);
}

跟进train_detector

由于训练，验证和测试阶段代码几乎是差不多的，只不过训练多了一个反向传播的过程。所以我们主要分析一下训练过程，训练过程是一个比较复杂的过程，不过宏观上大致分为解析网络配置文件，加载训练样本图像和标签，开启训练，结束训练保存模型这样一个过程，部分代码如下（我省略了很多代码，因为这一节是框架总览，后面会详细解释的）：

void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers)
{
    // 从options找出训练图片路径信息，如果没找到，默认使用"data/train.list"路径下的图片信息（train.list含有标准的信息格式：<object-class> <x> <y> <width> <height>），
    // 该文件可以由darknet提供的scripts/voc_label.py根据自行在网上下载的voc数据集生成，所以说是默认路径，其实也需要使用者自行调整，也可以任意命名，不一定要为train.list，
    // 甚至可以不用voc_label.py生成，可以自己不厌其烦的制作一个（当然规模应该是很小的，不然太累了。。。）
    // 读入后，train_images将含有训练图片中所有图片的标签以及定位信息
    list *options = read_data_cfg(datacfg);
    char *train_images = option_find_str(options, "train", "data/train.txt");
    char *valid_images = option_find_str(options, "valid", train_images);
    char *backup_directory = option_find_str(options, "backup", "/backup/");

network net_map;
    //如果要计算map
    if (calc_map) {
        FILE* valid_file = fopen(valid_images, "r");
        if (!valid_file) {
            printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg);
            getchar();
            exit(-1);
        }
        else fclose(valid_file);

cuda_set_device(gpus[0]);
        printf(" Prepare additional network for mAP calculation...\n");
        net_map = parse_network_cfg_custom(cfgfile, 1, 1);
        //分类数
        const int net_classes = net_map.layers[net_map.n - 1].classes;

int k;  // free memory unnecessary arrays
        for (k = 0; k < net_map.n - 1; ++k) free_layer_custom(net_map.layers[k], 1);

char *name_list = option_find_str(options, "names", "data/names.list");
        int names_size = 0;
        //获取类别对应的名字
        char **names = get_labels_custom(name_list, &names_size);
        if (net_classes != names_size) {
            printf(" Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
                name_list, names_size, net_classes, cfgfile);
            if (net_classes > names_size) getchar();
        }
        free_ptrs((void**)names, net_map.layers[net_map.n - 1].classes);
    }

srand(time(0));
     // 提取配置文件名称中的主要信息，用于输出打印（并无实质作用），比如提取cfg/yolo.cfg中的yolo，用于下面的输出打印
    char *base = basecfg(cfgfile);
    printf("%s\n", base);
    float avg_loss = -1;
    // 构建网络：用多少块GPU，就会构建多少个相同的网络（不使用GPU时，ngpus=1）
    network* nets = (network*)xcalloc(ngpus, sizeof(network));

//设定随机数种子
    srand(time(0));
    int seed = rand();
    int i;
      // for循环次数为ngpus，使用多少块GPU，就循环多少次（不使用GPU时，ngpus=1，也会循环一次）
    // 这里每一次循环都会构建一个相同的神经网络，如果提供了初始训练参数，也会为每个网络导入相同的初始训练参数
    for (i = 0; i < ngpus; ++i) {
        srand(seed);
#ifdef GPU
        cuda_set_device(gpus[i]);
#endif
		//解析网络配置文件
        nets[i] = parse_network_cfg(cfgfile);
        //测试某一个网络层的相关指标如运行时间
        nets[i].benchmark_layers = benchmark_layers;
        //如果有预训练模型则加载
        if (weightfile) {
            load_weights(&nets[i], weightfile);
        }
        //
        if (clear) *nets[i].seen = 0;
        nets[i].learning_rate *= ngpus;
    }
    ...
}

解析配置文件

截图部分yolov3.cfg网络配置文件如下：

可以看到配置参数大概分为2类：

• 与训练相关的项，以 [net] 行开头的段. 其中包含的参数有: batch_size, width,height,channel,momentum,decay,angle,saturation, exposure,hue,learning_rate,burn_in,max_batches,policy,steps,scales。
• 不同类型的层的配置参数. 如[convolutional], [short_cut], [yolo], [route], [upsample]层等。

在src/parse.c中我们会看到一行代码，net->batch /= net->subdivisions;，也就是说batch_size 在 darknet 内部又被均分为 net->subdivisions份, 成为更小的batch_size。 但是这些小的 batch_size 最终又被汇总, 因此 darknet 中的batch_size = net->batch / net->subdivisions * net->subdivisions。此外，和这个参数相关的计算训练图片数目的时候是这样，int imgs = net->batch * net->subdivisions * ngpus;，这样可以保证imgs可以被subdivisions整除，因此，通常将这个参数设为8的倍数。从这里也可以看出每个gpu或者cpu都会训练batch个样本。

我们知道了参数是什么样子，那么darknet是如何保存这些参数的呢？这就要看下基本数据结构了。