YOLO源码解析之yolo.c

yolo.c是YOLO模型源码的主文件，该文件包括以下函数：

train_yolo

void train_yolo(char *cfgfile, char *weightfile){    char *train_images = "/data/voc/train.txt";    char *backup_directory = "/home/pjreddie/backup/";    srand(time(0));    data_seed = time(0);    char *base = basecfg(cfgfile);    printf("%s\n", base);    float avg_loss = -1;    network net = parse_network_cfg(cfgfile);    if(weightfile){        load_weights(&net, weightfile);    }    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);    int imgs = net.batch*net.subdivisions;    int i = *net.seen/imgs;    data train, buffer;    layer l = net.layers[net.n - 1];    int side = l.side;    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 = side;    args.d = &buffer;    args.type = REGION_DATA;    pthread_t load_thread = load_data_in_thread(args);    clock_t time;    //while(i*imgs < N*120){    while(get_current_batch(net) < net.max_batches){        i += 1;        time=clock();        pthread_join(load_thread, 0);        train = buffer;        load_thread = load_data_in_thread(args);        printf("Loaded: %lf seconds\n", sec(clock()-time));        time=clock();        float loss = train_network(net, train);        if (avg_loss < 0) avg_loss = loss;        avg_loss = avg_loss*.9 + loss*.1;        printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);        if(i%1000==0 || (i < 1000 && i%100 == 0)){            char buff[256];            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);            save_weights(net, buff);        }        free_data(train);    }    char buff[256];    sprintf(buff, "%s/%s_final.weights", backup_directory, base);    save_weights(net, buff);}

convert_yolo_detections

void convert_yolo_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness){    int i,j,n;    //int per_cell = 5*num+classes;    for (i = 0; i < side*side; ++i){        int row = i / side;        int col = i % side;        for(n = 0; n < num; ++n){            int index = i*num + n;            int p_index = side*side*classes + i*num + n;            float scale = predictions[p_index];            int box_index = side*side*(classes + num) + (i*num + n)*4;            boxes[index].x = (predictions[box_index + 0] + col) / side * w;            boxes[index].y = (predictions[box_index + 1] + row) / side * h;            boxes[index].w = pow(predictions[box_index + 2], (square?2:1)) * w;            boxes[index].h = pow(predictions[box_index + 3], (square?2:1)) * h;            for(j = 0; j < classes; ++j){                int class_index = i*classes;                float prob = scale*predictions[class_index+j];                probs[index][j] = (prob > thresh) ? prob : 0;            }            if(only_objectness){                probs[index][0] = scale;            }        }    }}

print_yolo_dections

void print_yolo_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);        }    }}

validate_yolo

void validate_yolo(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));    char *base = "results/comp4_det_test_";    //list *plist = get_paths("data/voc.2007.test");    list *plist = get_paths("/home/pjreddie/data/voc/2007_test.txt");    //list *plist = get_paths("data/voc.2012.test");    char **paths = (char **)list_to_array(plist);    layer l = net.layers[net.n-1];    int classes = l.classes;    int square = l.sqrt;    int side = l.side;    int j;    FILE **fps = calloc(classes, sizeof(FILE *));    for(j = 0; j < classes; ++j){        char buff[1024];        snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);        fps[j] = fopen(buff, "w");    }    box *boxes = calloc(side*side*l.n, sizeof(box));    float **probs = calloc(side*side*l.n, sizeof(float *));    for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));    int m = plist->size;    int i=0;    int t;    float thresh = .001;    int nms = 1;    float iou_thresh = .5;    int nthreads = 2;    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;            float *predictions = network_predict(net, X);            int w = val[t].w;            int h = val[t].h;            convert_yolo_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes, 0);            if (nms) do_nms_sort(boxes, probs, side*side*l.n, classes, iou_thresh);            print_yolo_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h);            free(id);            free_image(val[t]);            free_image(val_resized[t]);        }    }    fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));}

validata_yolo_recall

void validate_yolo_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));    char *base = "results/comp4_det_test_";    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 square = l.sqrt;    int side = l.side;    int j, k;    FILE **fps = calloc(classes, sizeof(FILE *));    for(j = 0; j < classes; ++j){        char buff[1024];        snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);        fps[j] = fopen(buff, "w");    }    box *boxes = calloc(side*side*l.n, sizeof(box));    float **probs = calloc(side*side*l.n, sizeof(float *));    for(j = 0; j < side*side*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 = 0;    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);        float *predictions = network_predict(net, sized.data);        convert_yolo_detections(predictions, classes, l.n, square, side, 1, 1, thresh, probs, boxes, 1);        if (nms) do_nms(boxes, probs, side*side*l.n, 1, nms);        char *labelpath = find_replace(path, "images", "labels");        labelpath = find_replace(labelpath, "JPEGImages", "labels");        labelpath = find_replace(labelpath, ".jpg", ".txt");        labelpath = find_replace(labelpath, ".JPEG", ".txt");        int num_labels = 0;        box_label *truth = read_boxes(labelpath, &num_labels);        for(k = 0; k < side*side*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 < side*side*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);    }}

test_yolo

void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh){    network net = parse_network_cfg(cfgfile);    if(weightfile){        load_weights(&net, weightfile);    }    detection_layer l = net.layers[net.n-1];    set_batch_network(&net, 1);    srand(2222222);    clock_t time;    char buff[256];    char *input = buff;    int j;    float nms=.5;    box *boxes = calloc(l.side*l.side*l.n, sizeof(box));    float **probs = calloc(l.side*l.side*l.n, sizeof(float *));    for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));    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);        float *X = sized.data;        time=clock();        float *predictions = network_predict(net, X);        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));        convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);        if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);        //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);        draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, voc_labels, 20);        show_image(im, "predictions");        save_image(im, "predictions");        show_image(sized, "resized");        free_image(im);        free_image(sized);#ifdef OPENCV        cvWaitKey(0);        cvDestroyAllWindows();#endif        if (filename) break;    }}

run_yolo

void run_yolo(int argc, char **argv){    int i;    for(i = 0; i < 20; ++i){        char buff[256];        sprintf(buff, "data/labels/%s.png", voc_names[i]);        voc_labels[i] = load_image_color(buff, 0, 0);    }    float thresh = find_float_arg(argc, argv, "-thresh", .2);    int cam_index = find_int_arg(argc, argv, "-c", 0);    if(argc < 4){        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);        return;    }    char *cfg = argv[3];    char *weights = (argc > 4) ? argv[4] : 0;    char *filename = (argc > 5) ? argv[5]: 0;    if(0==strcmp(argv[2], "test")) test_yolo(cfg, weights, filename, thresh);    else if(0==strcmp(argv[2], "train")) train_yolo(cfg, weights);    else if(0==strcmp(argv[2], "valid")) validate_yolo(cfg, weights);    else if(0==strcmp(argv[2], "recall")) validate_yolo_recall(cfg, weights);    else if(0==strcmp(argv[2], "demo")) demo_yolo(cfg, weights, thresh, cam_index, filename);}