Exemplo n.º 1
0
void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
{
    int i, j;
    network net = parse_network_cfg(filename);
    set_batch_network(&net, 1);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    srand(time(0));

    list *options = read_data_cfg(datacfg);

    char *label_list = option_find_str(options, "labels", "data/labels.list");
    char *valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);

    char **labels = get_labels(label_list);
    list *plist = get_paths(valid_list);

    char **paths = (char **)list_to_array(plist);
    int m = plist->size;
    free_list(plist);

    float avg_acc = 0;
    float avg_topk = 0;
    int *indexes = calloc(topk, sizeof(int));

    int size = net.w;
    for(i = 0; i < m; ++i){
        int class = -1;
        char *path = paths[i];
        for(j = 0; j < classes; ++j){
            if(strstr(path, labels[j])){
                class = j;
                break;
            }
        }
        image im = load_image_color(paths[i], 0, 0);
        image resized = resize_min(im, size);
        resize_network(&net, resized.w, resized.h);
        //show_image(im, "orig");
        //show_image(crop, "cropped");
        //cvWaitKey(0);
        float *pred = network_predict(net, resized.data);
        if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1, 1);

        free_image(im);
        free_image(resized);
        top_k(pred, classes, topk, indexes);

        if(indexes[0] == class) avg_acc += 1;
        for(j = 0; j < topk; ++j){
            if(indexes[j] == class) avg_topk += 1;
        }

        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
    }
}
Exemplo n.º 2
0
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename)
{
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    srand(2222222);

    list *options = read_data_cfg(datacfg);

    char *name_list = option_find_str(options, "names", 0);
    if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
    int top = option_find_int(options, "top", 1);

    int i = 0;
    char **names = get_labels(name_list);
    clock_t time;
    int *indexes = calloc(top, sizeof(int));
    char buff[256];
    char *input = buff;
    int size = net.w;
    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 r = resize_min(im, size);
        resize_network(&net, r.w, r.h);
        printf("%d %d\n", r.w, r.h);

        float *X = r.data;
        time=clock();
        float *predictions = network_predict(net, X);
        top_predictions(net, top, indexes);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        for(i = 0; i < top; ++i){
            int index = indexes[i];
            printf("%s: %f\n", names[index], predictions[index]);
        }
        if(r.data != im.data) free_image(r);
        free_image(im);
        if (filename) break;
    }
}
Exemplo n.º 3
0
void test_writing(char *cfgfile, char *weightfile, char *filename)
{
    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;
    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);
        resize_network(net, im.w, im.h);
        printf("%d %d %d\n", im.h, im.w, im.c);
        float *X = im.data;
        time=clock();
        network_predict(net, X);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        image pred = get_network_image(net);

        image upsampled = resize_image(pred, im.w, im.h);
        image thresh = threshold_image(upsampled, .5);
        pred = thresh;

        show_image(pred, "prediction");
        show_image(im, "orig");
#ifdef OPENCV
        cvWaitKey(0);
        cvDestroyAllWindows();
#endif

        free_image(upsampled);
        free_image(thresh);
        free_image(im);
        if (filename) break;
    }
}
Exemplo n.º 4
0
void test_tag(char *cfgfile, char *weightfile, char *filename)
{
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    set_batch_network(&net, 1);
    srand(2222222);
    int i = 0;
    char **names = get_labels("data/tags.txt");
    clock_t time;
    int indexes[10];
    char buff[256];
    char *input = buff;
    int size = net.w;
    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 r = resize_min(im, size);
        resize_network(&net, r.w, r.h);
        printf("%d %d\n", r.w, r.h);

        float *X = r.data;
        time=clock();
        float *predictions = network_predict(net, X);
        top_predictions(net, 10, indexes);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        for(i = 0; i < 10; ++i){
            int index = indexes[i];
            printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
        }
        if(r.data != im.data) free_image(r);
        free_image(im);
        if (filename) break;
    }
}
Exemplo n.º 5
0
void test_super(char *cfgfile, char *weightfile, char *filename)
{
    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;
    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);
        resize_network(&net, im.w, im.h);
        printf("%d %d\n", im.w, im.h);

        float *X = im.data;
        time=clock();
        network_predict(net, X);
        image out = get_network_image(net);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
        save_image(out, "out");

        free_image(im);
        if (filename) break;
    }
}
Exemplo n.º 6
0
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");
    char *backup_directory = option_find_str(options, "backup", "/backup/");

    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];

    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;
            int dim = (args.w <= args.h ? args.w : args.h);
            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);
           }
           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);
}
Exemplo n.º 7
0
void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm)
{
    //scale_image(orig, 2);
    //translate_image(orig, -1);
    net->n = max_layer + 1;

    int dx = rand()%16 - 8;
    int dy = rand()%16 - 8;
    int flip = rand()%2;

    image crop = crop_image(orig, dx, dy, orig.w, orig.h);
    image im = resize_image(crop, (int)(orig.w * scale), (int)(orig.h * scale));
    if(flip) flip_image(im);

    resize_network(net, im.w, im.h);
    layer_t last = net->layers[net->n-1];
    //net->layers[net->n - 1].activation = LINEAR;

    image delta = make_image(im.w, im.h, im.c);

    NETWORK_STATE(state);

#ifdef GPU
    state.input = cuda_make_array(im.data, im.w*im.h*im.c);
    state.delta = cuda_make_array(im.data, im.w*im.h*im.c);

    forward_network_gpu(*net, state);
    copy_ongpu(last.outputs, last.output_gpu, 1, last.delta_gpu, 1);

    cuda_pull_array(last.delta_gpu, last.delta, last.outputs);
    calculate_loss(last.delta, last.delta, last.outputs, thresh);
    cuda_push_array(last.delta_gpu, last.delta, last.outputs);

    backward_network_gpu(*net, state);

    cuda_pull_array(state.delta, delta.data, im.w*im.h*im.c);
    cuda_free(state.input);
    cuda_free(state.delta);
#else
    state.input = im.data;
    state.delta = delta.data;
    forward_network(*net, state);
    fltcpy(last.delta, last.output, last.outputs);
    calculate_loss(last.output, last.delta, last.outputs, thresh);
    backward_network(*net, state);
#endif

    if(flip) flip_image(delta);
    //normalize_array(delta.data, delta.w*delta.h*delta.c);
    image resized = resize_image(delta, orig.w, orig.h);
    image out = crop_image(resized, -dx, -dy, orig.w, orig.h);

    /*
       image g = grayscale_image(out);
       free_image(out);
       out = g;
     */

    //rate = rate / abs_mean(out.data, out.w*out.h*out.c);

    if(norm) normalize_array(out.data, out.w*out.h*out.c);
    fltaddmul(orig.data, out.data, orig.w * orig.h * orig.c, rate);

    /*
       normalize_array(orig.data, orig.w*orig.h*orig.c);
       scale_image(orig, sqrt(var));
       translate_image(orig, mean);
     */

    //translate_image(orig, 1);
    //scale_image(orig, .5);
    //normalize_image(orig);

    constrain_image(orig);

    free_image(crop);
    free_image(im);
    free_image(delta);
    free_image(resized);
    free_image(out);

}
Exemplo n.º 8
0
void run_nightmare(int argc, char **argv)
{
    srand(0);
    if(argc < 4){
        fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [layer] [options! (optional)]\n", argv[0], argv[1]);
        return;
    }

    char *cfg = argv[2];
    char *weights = argv[3];
    char *input = argv[4];
    int max_layer = atoi(argv[5]);

    int range = find_int_arg(argc, argv, "-range", 1);
    int norm = find_int_arg(argc, argv, "-norm", 1);
    int rounds = find_int_arg(argc, argv, "-rounds", 1);
    int iters = find_int_arg(argc, argv, "-iters", 10);
    int octaves = find_int_arg(argc, argv, "-octaves", 4);
    float zoom = find_float_arg(argc, argv, "-zoom", 1.);
    float rate = find_float_arg(argc, argv, "-rate", .04);
    float thresh = find_float_arg(argc, argv, "-thresh", 1.);
    float rotate = find_float_arg(argc, argv, "-rotate", 0);
    float momentum = find_float_arg(argc, argv, "-momentum", .9);
    float lambda = find_float_arg(argc, argv, "-lambda", .01);
    char *prefix = find_char_arg(argc, argv, "-prefix", 0);
    int reconstruct = find_arg(argc, argv, "-reconstruct");
    int smooth_size = find_int_arg(argc, argv, "-smooth", 1);

    network net = parse_network_cfg(cfg);
    load_weights(&net, weights);
    char *cfgbase = basecfg(cfg);
    char *imbase = basecfg(input);

    set_batch_network(&net, 1);
    image im = load_image_color(input, 0, 0);
    if(0){
        float scale = 1;
        if(im.w > 512 || im.h > 512){
            if(im.w > im.h) scale = 512.0/im.w;
            else scale = 512.0/im.h;
        }
        image resized = resize_image(im, scale*im.w, scale*im.h);
        free_image(im);
        im = resized;
    }

    float *features = 0;
    image update;
    if (reconstruct){
        resize_network(&net, im.w, im.h);

        int zz = 0;
        network_predict(net, im.data);
        image out_im = get_network_image(net);
        image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz);
        //flip_image(crop);
        image f_im = resize_image(crop, out_im.w, out_im.h);
        free_image(crop);
        printf("%d features\n", out_im.w*out_im.h*out_im.c);


        im = resize_image(im, im.w, im.h);
        f_im = resize_image(f_im, f_im.w, f_im.h);
        features = f_im.data;

        int i;
        for(i = 0; i < 14*14*512; ++i){
            features[i] += rand_uniform(-.19, .19);
        }

        free_image(im);
        im = make_random_image(im.w, im.h, im.c);
        update = make_image(im.w, im.h, im.c);

    }

    int e;
    int n;
    for(e = 0; e < rounds; ++e){
        fprintf(stderr, "Iteration: ");
        fflush(stderr);
        for(n = 0; n < iters; ++n){  
            fprintf(stderr, "%d, ", n);
            fflush(stderr);
            if(reconstruct){
                reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
                //if ((n+1)%30 == 0) rate *= .5;
                show_image(im, "reconstruction");
#ifdef OPENCV
                cvWaitKey(10);
#endif
            }else{
                int layer = max_layer + rand()%range - range/2;
                int octave = rand()%octaves;
                optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm);
            }
        }
        fprintf(stderr, "done\n");
        if(0){
            image g = grayscale_image(im);
            free_image(im);
            im = g;
        }
        char buff[256];
        if (prefix){
            sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e);
        }else{
            sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e);
        }
        printf("%d %s\n", e, buff);
        save_image(im, buff);
        //show_image(im, buff);
        //cvWaitKey(0);

        if(rotate){
            image rot = rotate_image(im, rotate);
            free_image(im);
            im = rot;
        }
        image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom);
        image resized = resize_image(crop, im.w, im.h);
        free_image(im);
        free_image(crop);
        im = resized;
    }
}
Exemplo n.º 9
0
void validate_classifier_multi(char *datacfg, char *cfg, char *weights)
{
    int i, j;
    network *net = load_network(cfg, weights, 0);
    set_batch_network(net, 1);
    srand(time(0));

    list *options = read_data_cfg(datacfg);

    char *label_list = option_find_str(options, "labels", "data/labels.list");
    char *valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);

    char **labels = get_labels(label_list);
    list *plist = get_paths(valid_list);
    //int scales[] = {224, 288, 320, 352, 384};
    int scales[] = {224, 256, 288, 320};
    int nscales = sizeof(scales)/sizeof(scales[0]);

    char **paths = (char **)list_to_array(plist);
    int m = plist->size;
    free_list(plist);

    float avg_acc = 0;
    float avg_topk = 0;
    int *indexes = calloc(topk, sizeof(int));

    for(i = 0; i < m; ++i){
        int class = -1;
        char *path = paths[i];
        for(j = 0; j < classes; ++j){
            if(strstr(path, labels[j])){
                class = j;
                break;
            }
        }
        float *pred = calloc(classes, sizeof(float));
        image im = load_image_color(paths[i], 0, 0);
        for(j = 0; j < nscales; ++j){
            image r = resize_max(im, scales[j]);
            resize_network(net, r.w, r.h);
            float *p = network_predict(net, r.data);
            if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1 , 1);
            axpy_cpu(classes, 1, p, 1, pred, 1);
            flip_image(r);
            p = network_predict(net, r.data);
            axpy_cpu(classes, 1, p, 1, pred, 1);
            if(r.data != im.data) free_image(r);
        }
        free_image(im);
        top_k(pred, classes, topk, indexes);
        free(pred);
        if(indexes[0] == class) avg_acc += 1;
        for(j = 0; j < topk; ++j){
            if(indexes[j] == class) avg_topk += 1;
        }

        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
    }
}
Exemplo n.º 10
0
void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
{
    int i;

    float avg_loss = -1;
    char *base = basecfg(cfgfile);
    printf("%s\n", base);
    printf("%d\n", ngpus);
    network **nets = calloc(ngpus, sizeof(network*));

    srand(time(0));
    int seed = rand();
    for(i = 0; i < ngpus; ++i){
        srand(seed);
#ifdef GPU
        cuda_set_device(gpus[i]);
#endif
        nets[i] = load_network(cfgfile, weightfile, clear);
        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);
    list *options = read_data_cfg(datacfg);

    char *backup_directory = option_find_str(options, "backup", "/backup/");
    char *label_list = option_find_str(options, "labels", "data/labels.list");
    char *train_list = option_find_str(options, "train", "data/train.list");
    int classes = option_find_int(options, "classes", 2);

    char **labels = get_labels(label_list);
    list *plist = get_paths(train_list);
    char **paths = (char **)list_to_array(plist);
    printf("%d\n", plist->size);
    int N = plist->size;
    double time;

    load_args args = {0};
    args.w = net->w;
    args.h = net->h;
    args.threads = 32;
    args.hierarchy = net->hierarchy;

    args.min = net->min_ratio*net->w;
    args.max = net->max_ratio*net->w;
    printf("%d %d\n", args.min, args.max);
    args.angle = net->angle;
    args.aspect = net->aspect;
    args.exposure = net->exposure;
    args.saturation = net->saturation;
    args.hue = net->hue;
    args.size = net->w;

    args.paths = paths;
    args.classes = classes;
    args.n = imgs;
    args.m = N;
    args.labels = labels;
    args.type = CLASSIFICATION_DATA;

    data train;
    data buffer;
    pthread_t load_thread;
    args.d = &buffer;
    load_thread = load_data(args);

    int count = 0;
    int epoch = (*net->seen)/N;
    while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
        if(net->random && count++%40 == 0){
            printf("Resizing\n");
            int dim = (rand() % 11 + 4) * 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;
            args.size = dim;
            args.min = net->min_ratio*dim;
            args.max = net->max_ratio*dim;
            printf("%d %d\n", args.min, args.max);

            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 = what_time_is_it_now();

        pthread_join(load_thread, 0);
        train = buffer;
        load_thread = load_data(args);

        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
        time = what_time_is_it_now();

        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 == -1) avg_loss = loss;
        avg_loss = avg_loss*.9 + loss*.1;
        printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
        free_data(train);
        if(*net->seen/N > epoch){
            epoch = *net->seen/N;
            char buff[256];
            sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
            save_weights(net, buff);
        }
        if(get_current_batch(net)%1000 == 0){
            char buff[256];
            sprintf(buff, "%s/%s.backup",backup_directory,base);
            save_weights(net, buff);
        }
    }
    char buff[256];
    sprintf(buff, "%s/%s.weights", backup_directory, base);
    save_weights(net, buff);
    pthread_join(load_thread, 0);

    free_network(net);
    free_ptrs((void**)labels, classes);
    free_ptrs((void**)paths, plist->size);
    free_list(plist);
    free(base);
}
Exemplo n.º 11
0
void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
{
    int i, j;
    network net = parse_network_cfg(filename);
    set_batch_network(&net, 1);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    srand(time(0));

    list *options = read_data_cfg(datacfg);

    char *label_list = option_find_str(options, "labels", "data/labels.list");
    char *valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);

    char **labels = get_labels(label_list);
    list *plist = get_paths(valid_list);
    int scales[] = {192, 224, 288, 320, 352};
    int nscales = sizeof(scales)/sizeof(scales[0]);

    char **paths = (char **)list_to_array(plist);
    int m = plist->size;
    free_list(plist);

    float avg_acc = 0;
    float avg_topk = 0;
    int *indexes = calloc(topk, sizeof(int));

    for(i = 0; i < m; ++i){
        int class = -1;
        char *path = paths[i];
        for(j = 0; j < classes; ++j){
            if(strstr(path, labels[j])){
                class = j;
                break;
            }
        }
        float *pred = calloc(classes, sizeof(float));
        image im = load_image_color(paths[i], 0, 0);
        for(j = 0; j < nscales; ++j){
            image r = resize_min(im, scales[j]);
            resize_network(&net, r.w, r.h);
            float *p = network_predict(net, r.data);
            fltadd(pred, p, classes);
            flip_image(r);
            p = network_predict(net, r.data);
            fltadd(pred, p, classes);
            free_image(r);
        }
        free_image(im);
        top_k(pred, classes, topk, indexes);
        free(pred);
        if(indexes[0] == class) avg_acc += 1;
        for(j = 0; j < topk; ++j){
            if(indexes[j] == class) avg_topk += 1;
        }

        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
    }
}