YOLO源码分析之detector.c

guthub地址:https://github.com/pjreddie/darknet/blob/master/

这里参考的网络定义文件如下

// cfg/yolo-voc.cfg[net] # yolo的网络架构要求第一个section必须为[net]或[netowrk]，参见src/parse.c:586# sections->size为cfg中配置的[*]数量，该cfg的size为32batch=64subdivisions=8height=416width=416channels=3momentum=0.9decay=0.0005angle=0saturation = 1.5exposure = 1.5hue=.1learning_rate=0.0001max_batches = 45000policy=stepssteps=100,25000,35000scales=10,.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[reorg]stride=2[route]layers=-1,-3[convolutional]batch_normalize=1size=3stride=1pad=1filters=1024activation=leaky[convolutional]size=1stride=1pad=1filters=125activation=linear[region]anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52bias_match=1classes=20coords=4num=5softmax=1jitter=.2rescore=1object_scale=5noobject_scale=1class_scale=1coord_scale=1absolute=1thresh = .6random=0

下面是detector.c的源代码

#include "network.h"#include "region_layer.h"#include "cost_layer.h"#include "utils.h"#include "parser.h"#include "box.h"#include "demo.h"#include "option_list.h"#ifdef OPENCV#include "opencv2/highgui/highgui_c.h"#endifstatic int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};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", "data/train.list");// 训练自己的数据集时，这里要进行相应的修改    char *backup_directory = option_find_str(options, "backup", "/backup/");// 训练自己的数据集时，这里要进行相应的修改    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] = parse_network_cfg(cfgfile);        if(weightfile){            load_weights(&nets[i], weightfile);        }        if(clear) *nets[i].seen = 0;        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]; //最后一层的索引号是net.n-1，这里net.n是31.所以最后一层region层的索引号是30    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;// [net]层的w    args.h = net.h;    args.paths = paths;    args.n = imgs;    args.m = plist->size; // train_images中的image数目，即训练样本数    args.classes = classes;    args.jitter = jitter;    args.num_boxes = l.max_boxes; // cfg里没有配置该项，这里使用默认值30    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)+100 > net.max_batches) dim = 544;            //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; //这里采用多线程的方式，这里获取的train里重要的成员变量有X{rows=batch, cols=[net].w*[net].h*[net].c}        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);           }           image im = float_to_image(448, 448, 3, train.X.vals[10]);           int k;           for(k = 0; k < l.max_boxes; ++k){           box b = float_to_box(train.y.vals[10] + 1 + k*5);           printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);           draw_bbox(im, b, 8, 1,0,0);           }           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("%d: %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 || (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);}static int get_coco_image_id(char *filename){    char *p = strrchr(filename, '_');    return atoi(p+1);}static void print_cocos(FILE *fp, char *image_path, box *boxes, float **probs, int num_boxes, int classes, int w, int h){    int i, j;    int image_id = get_coco_image_id(image_path);    for(i = 0; i < num_boxes; ++i){        float xmin = boxes[i].x - boxes[i].w/2.;        float xmax = boxes[i].x + boxes[i].w/2.;        float ymin = boxes[i].y - boxes[i].h/2.;        float ymax = boxes[i].y + boxes[i].h/2.;        if (xmin < 0) xmin = 0;        if (ymin < 0) ymin = 0;        if (xmax > w) xmax = w;        if (ymax > h) ymax = h;        float bx = xmin;        float by = ymin;        float bw = xmax - xmin;        float bh = ymax - ymin;        for(j = 0; j < classes; ++j){            if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);        }    }}void print_detector_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h){    int i, j;    for(i = 0; i < total; ++i){        float xmin = boxes[i].x - boxes[i].w/2.;        float xmax = boxes[i].x + boxes[i].w/2.;        float ymin = boxes[i].y - boxes[i].h/2.;        float ymax = boxes[i].y + boxes[i].h/2.;        if (xmin < 0) xmin = 0;        if (ymin < 0) ymin = 0;        if (xmax > w) xmax = w;        if (ymax > h) ymax = h;        for(j = 0; j < classes; ++j){            if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],                    xmin, ymin, xmax, ymax);        }    }}void print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, int total, int classes, int w, int h){    int i, j;    for(i = 0; i < total; ++i){        float xmin = boxes[i].x - boxes[i].w/2.;        float xmax = boxes[i].x + boxes[i].w/2.;        float ymin = boxes[i].y - boxes[i].h/2.;        float ymax = boxes[i].y + boxes[i].h/2.;        if (xmin < 0) xmin = 0;        if (ymin < 0) ymin = 0;        if (xmax > w) xmax = w;        if (ymax > h) ymax = h;        for(j = 0; j < classes; ++j){            int class = j;            if (probs[i][class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class],                    xmin, ymin, xmax, ymax);        }    }}void validate_detector(char *datacfg, char *cfgfile, char *weightfile){    int j;    list *options = read_data_cfg(datacfg);    char *valid_images = option_find_str(options, "valid", "data/train.list");    char *name_list = option_find_str(options, "names", "data/names.list");    char *prefix = option_find_str(options, "results", "results");    char **names = get_labels(name_list);    char *mapf = option_find_str(options, "map", 0);    int *map = 0;    if (mapf) map = read_map(mapf);    network net = parse_network_cfg(cfgfile);    if(weightfile){        load_weights(&net, weightfile);    }    set_batch_network(&net, 1);    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);    srand(time(0));    char *base = "comp4_det_test_";    list *plist = get_paths(valid_images);    char **paths = (char **)list_to_array(plist);    layer l = net.layers[net.n-1];    int classes = l.classes;    char buff[1024];    char *type = option_find_str(options, "eval", "voc");    FILE *fp = 0;    FILE **fps = 0;    int coco = 0;    int imagenet = 0;    if(0==strcmp(type, "coco")){        snprintf(buff, 1024, "%s/coco_results.json", prefix);        fp = fopen(buff, "w");        fprintf(fp, "[\n");        coco = 1;    } else if(0==strcmp(type, "imagenet")){        snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix);        fp = fopen(buff, "w");        imagenet = 1;        classes = 200;    } else {        fps = calloc(classes, sizeof(FILE *));        for(j = 0; j < classes; ++j){            snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]);            fps[j] = fopen(buff, "w");        }    }    box *boxes = calloc(l.w*l.h*l.n, sizeof(box));    float **probs = calloc(l.w*l.h*l.n, sizeof(float *));    for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));    int m = plist->size;    int i=0;    int t;    float thresh = .005;    float nms = .45;    int nthreads = 4;    image *val = calloc(nthreads, sizeof(image));    image *val_resized = calloc(nthreads, sizeof(image));    image *buf = calloc(nthreads, sizeof(image));    image *buf_resized = calloc(nthreads, sizeof(image));    pthread_t *thr = calloc(nthreads, sizeof(pthread_t));    load_args args = {0};    args.w = net.w;    args.h = net.h;    args.type = IMAGE_DATA;    for(t = 0; t < nthreads; ++t){        args.path = paths[i+t];        args.im = &buf[t];        args.resized = &buf_resized[t];        thr[t] = load_data_in_thread(args);    }    time_t start = time(0);    for(i = nthreads; i < m+nthreads; i += nthreads){        fprintf(stderr, "%d\n", i);        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){            pthread_join(thr[t], 0);            val[t] = buf[t];            val_resized[t] = buf_resized[t];        }        for(t = 0; t < nthreads && i+t < m; ++t){            args.path = paths[i+t];            args.im = &buf[t];            args.resized = &buf_resized[t];            thr[t] = load_data_in_thread(args);        }        for(t = 0; t < nthreads && i+t-nthreads < m; ++t){            char *path = paths[i+t-nthreads];            char *id = basecfg(path);            float *X = val_resized[t].data;            network_predict(net, X);            int w = val[t].w;            int h = val[t].h;            get_region_boxes(l, w, h, thresh, probs, boxes, 0, map);            if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);            if (coco){                print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);            } else if (imagenet){                print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);            } else {                print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);            }            free(id);            free_image(val[t]);            free_image(val_resized[t]);        }    }    for(j = 0; j < classes; ++j){        if(fps) fclose(fps[j]);    }    if(coco){        fseek(fp, -2, SEEK_CUR);         fprintf(fp, "\n]\n");        fclose(fp);    }    fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));}void validate_detector_recall(char *cfgfile, char *weightfile){    network net = parse_network_cfg(cfgfile);    if(weightfile){        load_weights(&net, weightfile);    }    set_batch_network(&net, 1);    fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);    srand(time(0));    list *plist = get_paths("data/voc.2007.test");    char **paths = (char **)list_to_array(plist);    layer l = net.layers[net.n-1];    int classes = l.classes;    int j, k;    box *boxes = calloc(l.w*l.h*l.n, sizeof(box));    float **probs = calloc(l.w*l.h*l.n, sizeof(float *));    for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));    int m = plist->size;    int i=0;    float thresh = .001;    float iou_thresh = .5;    float nms = .4;    int total = 0;    int correct = 0;    int proposals = 0;    float avg_iou = 0;    for(i = 0; i < m; ++i){        char *path = paths[i];        image orig = load_image_color(path, 0, 0);        image sized = resize_image(orig, net.w, net.h);        char *id = basecfg(path);        network_predict(net, sized.data);        get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0);        if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);        char labelpath[4096];        find_replace(path, "images", "labels", labelpath);        find_replace(labelpath, "JPEGImages", "labels", labelpath);        find_replace(labelpath, ".jpg", ".txt", labelpath);        find_replace(labelpath, ".JPEG", ".txt", labelpath);        int num_labels = 0;        box_label *truth = read_boxes(labelpath, &num_labels);        for(k = 0; k < l.w*l.h*l.n; ++k){            if(probs[k][0] > thresh){                ++proposals;            }        }        for (j = 0; j < num_labels; ++j) {            ++total;            box t = {truth[j].x, truth[j].y, truth[j].w, truth[j].h};            float best_iou = 0;            for(k = 0; k < l.w*l.h*l.n; ++k){                float iou = box_iou(boxes[k], t);                if(probs[k][0] > thresh && iou > best_iou){                    best_iou = iou;                }            }            avg_iou += best_iou;            if(best_iou > iou_thresh){                ++correct;            }        }        fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);        free(id);        free_image(orig);        free_image(sized);    }}void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh){    list *options = read_data_cfg(datacfg);    char *name_list = option_find_str(options, "names", "data/names.list");    char **names = get_labels(name_list);    image **alphabet = load_alphabet();    network net = parse_network_cfg(cfgfile);    if(weightfile){        load_weights(&net, weightfile);    }    set_batch_network(&net, 1);    srand(2222222);    clock_t time;    char buff[256];    char *input = buff;    int j;    float nms=.4;    while(1){        if(filename){            strncpy(input, filename, 256);        } else {            printf("Enter Image Path: ");            fflush(stdout);            input = fgets(input, 256, stdin);            if(!input) return;            strtok(input, "\n");        }        image im = load_image_color(input,0,0);        image sized = resize_image(im, net.w, net.h);        layer l = net.layers[net.n-1];        box *boxes = calloc(l.w*l.h*l.n, sizeof(box));        float **probs = calloc(l.w*l.h*l.n, sizeof(float *));        for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));        float *X = sized.data;        time=clock();        network_predict(net, X);        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));        get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0);        if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);        draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);        save_image(im, "predictions");        show_image(im, "predictions");        free_image(im);        free_image(sized);        free(boxes);        free_ptrs((void **)probs, l.w*l.h*l.n);#ifdef OPENCV        cvWaitKey(0);        cvDestroyAllWindows();#endif        if (filename) break;    }}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);    int cam_index = find_int_arg(argc, argv, "-c", 0);    int frame_skip = find_int_arg(argc, argv, "-s", 0);    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);    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");    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);    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);    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", 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);    }}