darknet/src/data.c

#include "data.h"
#include "utils.h"
#include "image.h"
#include "cuda.h"

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

pthread_mutex_t mutex = PTHREAD_MUTEX_INITIALIZER;

list *get_paths(char *filename)
{
    char *path;
    FILE *file = fopen(filename, "r");
    if(!file) file_error(filename);
    list *lines = make_list();
    while((path=fgetl(file))){
        list_insert(lines, path);
    }
    fclose(file);
    return lines;
}

/*
char **get_random_paths_indexes(char **paths, int n, int m, int *indexes)
{
    char **random_paths = calloc(n, sizeof(char*));
    int i;
    pthread_mutex_lock(&mutex);
    for(i = 0; i < n; ++i){
        int index = rand()%m;
        indexes[i] = index;
        random_paths[i] = paths[index];
        if(i == 0) printf("%s\n", paths[index]);
    }
    pthread_mutex_unlock(&mutex);
    return random_paths;
}
*/

char **get_random_paths(char **paths, int n, int m)
{
    char **random_paths = calloc(n, sizeof(char*));
    int i;
    pthread_mutex_lock(&mutex);
    for(i = 0; i < n; ++i){
        int index = rand()%m;
        random_paths[i] = paths[index];
        //if(i == 0) printf("%s\n", paths[index]);
    }
    pthread_mutex_unlock(&mutex);
    return random_paths;
}

char **find_replace_paths(char **paths, int n, char *find, char *replace)
{
    char **replace_paths = calloc(n, sizeof(char*));
    int i;
    for(i = 0; i < n; ++i){
        char replaced[4096];
        find_replace(paths[i], find, replace, replaced);
        replace_paths[i] = copy_string(replaced);
    }
    return replace_paths;
}

matrix load_image_paths_gray(char **paths, int n, int w, int h)
{
    int i;
    matrix X;
    X.rows = n;
    X.vals = calloc(X.rows, sizeof(float*));
    X.cols = 0;

    for(i = 0; i < n; ++i){
        image im = load_image(paths[i], w, h, 3);

        image gray = grayscale_image(im);
        free_image(im);
        im = gray;

        X.vals[i] = im.data;
        X.cols = im.h*im.w*im.c;
    }
    return X;
}

matrix load_image_paths(char **paths, int n, int w, int h)
{
    int i;
    matrix X;
    X.rows = n;
    X.vals = calloc(X.rows, sizeof(float*));
    X.cols = 0;

    for(i = 0; i < n; ++i){
        image im = load_image_color(paths[i], w, h);
        X.vals[i] = im.data;
        X.cols = im.h*im.w*im.c;
    }
    return X;
}

matrix load_image_augment_paths(char **paths, int n, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
{
    int i;
    matrix X;
    X.rows = n;
    X.vals = calloc(X.rows, sizeof(float*));
    X.cols = 0;

    for(i = 0; i < n; ++i){
        image im = load_image_color(paths[i], 0, 0);
        image crop = random_augment_image(im, angle, aspect, min, max, size);
        int flip = rand()%2;
        if (flip) flip_image(crop);
        random_distort_image(crop, hue, saturation, exposure);

        /*
        show_image(im, "orig");
        show_image(crop, "crop");
        cvWaitKey(0);
        */
        free_image(im);
        X.vals[i] = crop.data;
        X.cols = crop.h*crop.w*crop.c;
    }
    return X;
}


box_label *read_boxes(char *filename, int *n)
{
    box_label *boxes = calloc(1, sizeof(box_label));
    FILE *file = fopen(filename, "r");
    if(!file) file_error(filename);
    float x, y, h, w;
    int id;
    int count = 0;
    while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
        boxes = realloc(boxes, (count+1)*sizeof(box_label));
        boxes[count].id = id;
        boxes[count].x = x;
        boxes[count].y = y;
        boxes[count].h = h;
        boxes[count].w = w;
        boxes[count].left   = x - w/2;
        boxes[count].right  = x + w/2;
        boxes[count].top    = y - h/2;
        boxes[count].bottom = y + h/2;
        ++count;
    }
    fclose(file);
    *n = count;
    return boxes;
}

void randomize_boxes(box_label *b, int n)
{
    int i;
    for(i = 0; i < n; ++i){
        box_label swap = b[i];
        int index = rand()%n;
        b[i] = b[index];
        b[index] = swap;
    }
}

void correct_boxes(box_label *boxes, int n, float dx, float dy, float sx, float sy, int flip)
{
    int i;
    for(i = 0; i < n; ++i){
        if(boxes[i].x == 0 && boxes[i].y == 0) {
            boxes[i].x = 999999;
            boxes[i].y = 999999;
            boxes[i].w = 999999;
            boxes[i].h = 999999;
            continue;
        }
        boxes[i].left   = boxes[i].left  * sx - dx;
        boxes[i].right  = boxes[i].right * sx - dx;
        boxes[i].top    = boxes[i].top   * sy - dy;
        boxes[i].bottom = boxes[i].bottom* sy - dy;

        if(flip){
            float swap = boxes[i].left;
            boxes[i].left = 1. - boxes[i].right;
            boxes[i].right = 1. - swap;
        }

        boxes[i].left =  constrain(0, 1, boxes[i].left);
        boxes[i].right = constrain(0, 1, boxes[i].right);
        boxes[i].top =   constrain(0, 1, boxes[i].top);
        boxes[i].bottom =   constrain(0, 1, boxes[i].bottom);

        boxes[i].x = (boxes[i].left+boxes[i].right)/2;
        boxes[i].y = (boxes[i].top+boxes[i].bottom)/2;
        boxes[i].w = (boxes[i].right - boxes[i].left);
        boxes[i].h = (boxes[i].bottom - boxes[i].top);

        boxes[i].w = constrain(0, 1, boxes[i].w);
        boxes[i].h = constrain(0, 1, boxes[i].h);
    }
}

void fill_truth_swag(char *path, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
{
    char labelpath[4096];
    find_replace(path, "images", "labels", labelpath);
    find_replace(labelpath, "JPEGImages", "labels", labelpath);
    find_replace(labelpath, ".jpg", ".txt", labelpath);
    find_replace(labelpath, ".JPG", ".txt", labelpath);
    find_replace(labelpath, ".JPEG", ".txt", labelpath);

    int count = 0;
    box_label *boxes = read_boxes(labelpath, &count);
    randomize_boxes(boxes, count);
    correct_boxes(boxes, count, dx, dy, sx, sy, flip);
    float x,y,w,h;
    int id;
    int i;

    for (i = 0; i < count && i < 30; ++i) {
        x =  boxes[i].x;
        y =  boxes[i].y;
        w =  boxes[i].w;
        h =  boxes[i].h;
        id = boxes[i].id;

        if (w < .0 || h < .0) continue;

        int index = (4+classes) * i;

        truth[index++] = x;
        truth[index++] = y;
        truth[index++] = w;
        truth[index++] = h;

        if (id < classes) truth[index+id] = 1;
    }
    free(boxes);
}

void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int flip, float dx, float dy, float sx, float sy)
{
    char labelpath[4096];
    find_replace(path, "images", "labels", labelpath);
    find_replace(labelpath, "JPEGImages", "labels", labelpath);

    find_replace(labelpath, ".jpg", ".txt", labelpath);
    find_replace(labelpath, ".png", ".txt", labelpath);
    find_replace(labelpath, ".JPG", ".txt", labelpath);
    find_replace(labelpath, ".JPEG", ".txt", labelpath);
    int count = 0;
    box_label *boxes = read_boxes(labelpath, &count);
    randomize_boxes(boxes, count);
    correct_boxes(boxes, count, dx, dy, sx, sy, flip);
    float x,y,w,h;
    int id;
    int i;

    for (i = 0; i < count; ++i) {
        x =  boxes[i].x;
        y =  boxes[i].y;
        w =  boxes[i].w;
        h =  boxes[i].h;
        id = boxes[i].id;

        if (w < .005 || h < .005) continue;

        int col = (int)(x*num_boxes);
        int row = (int)(y*num_boxes);

        x = x*num_boxes - col;
        y = y*num_boxes - row;

        int index = (col+row*num_boxes)*(5+classes);
        if (truth[index]) continue;
        truth[index++] = 1;

        if (id < classes) truth[index+id] = 1;
        index += classes;

        truth[index++] = x;
        truth[index++] = y;
        truth[index++] = w;
        truth[index++] = h;
    }
    free(boxes);
}

void fill_truth_detection(char *path, int num_boxes, float *truth, int classes, int flip, float dx, float dy, float sx, float sy)
{
    char labelpath[4096];
    find_replace(path, "images", "labels", labelpath);
    find_replace(labelpath, "JPEGImages", "labels", labelpath);

    find_replace(labelpath, "raw", "labels", labelpath);
    find_replace(labelpath, ".jpg", ".txt", labelpath);
    find_replace(labelpath, ".png", ".txt", labelpath);
    find_replace(labelpath, ".JPG", ".txt", labelpath);
    find_replace(labelpath, ".JPEG", ".txt", labelpath);
    int count = 0;
    box_label *boxes = read_boxes(labelpath, &count);
    randomize_boxes(boxes, count);
    correct_boxes(boxes, count, dx, dy, sx, sy, flip);
    if(count > num_boxes) count = num_boxes;
    float x,y,w,h;
    int id;
    int i;

    for (i = 0; i < count; ++i) {
        x =  boxes[i].x;
        y =  boxes[i].y;
        w =  boxes[i].w;
        h =  boxes[i].h;
        id = boxes[i].id;

        if ((w < .001 || h < .001)) continue;

        truth[i*5+0] = x;
        truth[i*5+1] = y;
        truth[i*5+2] = w;
        truth[i*5+3] = h;
        truth[i*5+4] = id;
    }
    free(boxes);
}

#define NUMCHARS 37

void print_letters(float *pred, int n)
{
    int i;
    for(i = 0; i < n; ++i){
        int index = max_index(pred+i*NUMCHARS, NUMCHARS);
        printf("%c", int_to_alphanum(index));
    }
    printf("\n");
}

void fill_truth_captcha(char *path, int n, float *truth)
{
    char *begin = strrchr(path, '/');
    ++begin;
    int i;
    for(i = 0; i < strlen(begin) && i < n && begin[i] != '.'; ++i){
        int index = alphanum_to_int(begin[i]);
        if(index > 35) printf("Bad %c\n", begin[i]);
        truth[i*NUMCHARS+index] = 1;
    }
    for(;i < n; ++i){
        truth[i*NUMCHARS + NUMCHARS-1] = 1;
    }
}

data load_data_captcha(char **paths, int n, int m, int k, int w, int h)
{
    if(m) paths = get_random_paths(paths, n, m);
    data d = {0};
    d.shallow = 0;
    d.X = load_image_paths(paths, n, w, h);
    d.y = make_matrix(n, k*NUMCHARS);
    int i;
    for(i = 0; i < n; ++i){
        fill_truth_captcha(paths[i], k, d.y.vals[i]);
    }
    if(m) free(paths);
    return d;
}

data load_data_captcha_encode(char **paths, int n, int m, int w, int h)
{
    if(m) paths = get_random_paths(paths, n, m);
    data d = {0};
    d.shallow = 0;
    d.X = load_image_paths(paths, n, w, h);
    d.X.cols = 17100;
    d.y = d.X;
    if(m) free(paths);
    return d;
}

void fill_truth(char *path, char **labels, int k, float *truth)
{
    int i;
    memset(truth, 0, k*sizeof(float));
    int count = 0;
    for(i = 0; i < k; ++i){
        if(strstr(path, labels[i])){
            truth[i] = 1;
            ++count;
        }
    }
    if(count != 1 && (k != 1 || count != 0)) printf("Too many or too few labels: %d, %s\n", count, path);
}

void fill_hierarchy(float *truth, int k, tree *hierarchy)
{
    int j;
    for(j = 0; j < k; ++j){
        if(truth[j]){
            int parent = hierarchy->parent[j];
            while(parent >= 0){
                truth[parent] = 1;
                parent = hierarchy->parent[parent];
            }
        }
    }
    int i;
    int count = 0;
    for(j = 0; j < hierarchy->groups; ++j){
        //printf("%d\n", count);
        int mask = 1;
        for(i = 0; i < hierarchy->group_size[j]; ++i){
            if(truth[count + i]){
                mask = 0;
                break;
            }
        }
        if (mask) {
            for(i = 0; i < hierarchy->group_size[j]; ++i){
                truth[count + i] = SECRET_NUM;
            }
        }
        count += hierarchy->group_size[j];
    }
}

matrix load_regression_labels_paths(char **paths, int n)
{
    matrix y = make_matrix(n, 1);
    int i;
    for(i = 0; i < n; ++i){
        char labelpath[4096];
        find_replace(paths[i], "images", "targets", labelpath);
        find_replace(labelpath, "JPEGImages", "targets", labelpath);
        find_replace(labelpath, ".jpg", ".txt", labelpath);
        find_replace(labelpath, ".png", ".txt", labelpath);
        
        FILE *file = fopen(labelpath, "r");
        fscanf(file, "%f", &(y.vals[i][0]));
        fclose(file);
    }
    return y;
}

matrix load_labels_paths(char **paths, int n, char **labels, int k, tree *hierarchy)
{
    matrix y = make_matrix(n, k);
    int i;
    for(i = 0; i < n && labels; ++i){
        fill_truth(paths[i], labels, k, y.vals[i]);
        if(hierarchy){
            fill_hierarchy(y.vals[i], k, hierarchy);
        }
    }
    return y;
}

matrix load_tags_paths(char **paths, int n, int k)
{
    matrix y = make_matrix(n, k);
    int i;
    int count = 0;
    for(i = 0; i < n; ++i){
        char label[4096];
        find_replace(paths[i], "imgs", "labels", label);
        find_replace(label, "_iconl.jpeg", ".txt", label);
        FILE *file = fopen(label, "r");
        if(!file){
            find_replace(label, "labels", "labels2", label);
            file = fopen(label, "r");
            if(!file) continue;
        }
        ++count;
        int tag;
        while(fscanf(file, "%d", &tag) == 1){
            if(tag < k){
                y.vals[i][tag] = 1;
            }
        }
        fclose(file);
    }
    printf("%d/%d\n", count, n);
    return y;
}

char **get_labels(char *filename)
{
    list *plist = get_paths(filename);
    char **labels = (char **)list_to_array(plist);
    free_list(plist);
    return labels;
}

void free_data(data d)
{
    if(!d.shallow){
        free_matrix(d.X);
        free_matrix(d.y);
    }else{
        free(d.X.vals);
        free(d.y.vals);
    }
}

data load_data_region(int n, char **paths, int m, int w, int h, int size, int classes, float jitter, float hue, float saturation, float exposure)
{
    char **random_paths = get_random_paths(paths, n, m);
    int i;
    data d = {0};
    d.shallow = 0;

    d.X.rows = n;
    d.X.vals = calloc(d.X.rows, sizeof(float*));
    d.X.cols = h*w*3;


    int k = size*size*(5+classes);
    d.y = make_matrix(n, k);
    for(i = 0; i < n; ++i){
        image orig = load_image_color(random_paths[i], 0, 0);

        int oh = orig.h;
        int ow = orig.w;

        int dw = (ow*jitter);
        int dh = (oh*jitter);

        int pleft  = rand_uniform(-dw, dw);
        int pright = rand_uniform(-dw, dw);
        int ptop   = rand_uniform(-dh, dh);
        int pbot   = rand_uniform(-dh, dh);

        int swidth =  ow - pleft - pright;
        int sheight = oh - ptop - pbot;

        float sx = (float)swidth  / ow;
        float sy = (float)sheight / oh;

        int flip = rand()%2;
        image cropped = crop_image(orig, pleft, ptop, swidth, sheight);

        float dx = ((float)pleft/ow)/sx;
        float dy = ((float)ptop /oh)/sy;

        image sized = resize_image(cropped, w, h);
        if(flip) flip_image(sized);
        random_distort_image(sized, hue, saturation, exposure);
        d.X.vals[i] = sized.data;

        fill_truth_region(random_paths[i], d.y.vals[i], classes, size, flip, dx, dy, 1./sx, 1./sy);

        free_image(orig);
        free_image(cropped);
    }
    free(random_paths);
    return d;
}

data load_data_compare(int n, char **paths, int m, int classes, int w, int h)
{
    if(m) paths = get_random_paths(paths, 2*n, m);
    int i,j;
    data d = {0};
    d.shallow = 0;

    d.X.rows = n;
    d.X.vals = calloc(d.X.rows, sizeof(float*));
    d.X.cols = h*w*6;

    int k = 2*(classes);
    d.y = make_matrix(n, k);
    for(i = 0; i < n; ++i){
        image im1 = load_image_color(paths[i*2],   w, h);
        image im2 = load_image_color(paths[i*2+1], w, h);

        d.X.vals[i] = calloc(d.X.cols, sizeof(float));
        memcpy(d.X.vals[i],         im1.data, h*w*3*sizeof(float));
        memcpy(d.X.vals[i] + h*w*3, im2.data, h*w*3*sizeof(float));

        int id;
        float iou;

        char imlabel1[4096];
        char imlabel2[4096];
        find_replace(paths[i*2],   "imgs", "labels", imlabel1);
        find_replace(imlabel1, "jpg", "txt", imlabel1);
        FILE *fp1 = fopen(imlabel1, "r");

        while(fscanf(fp1, "%d %f", &id, &iou) == 2){
            if (d.y.vals[i][2*id] < iou) d.y.vals[i][2*id] = iou;
        }

        find_replace(paths[i*2+1], "imgs", "labels", imlabel2);
        find_replace(imlabel2, "jpg", "txt", imlabel2);
        FILE *fp2 = fopen(imlabel2, "r");

        while(fscanf(fp2, "%d %f", &id, &iou) == 2){
            if (d.y.vals[i][2*id + 1] < iou) d.y.vals[i][2*id + 1] = iou;
        }

        for (j = 0; j < classes; ++j){
            if (d.y.vals[i][2*j] > .5 &&  d.y.vals[i][2*j+1] < .5){
                d.y.vals[i][2*j] = 1;
                d.y.vals[i][2*j+1] = 0;
            } else if (d.y.vals[i][2*j] < .5 &&  d.y.vals[i][2*j+1] > .5){
                d.y.vals[i][2*j] = 0;
                d.y.vals[i][2*j+1] = 1;
            } else {
                d.y.vals[i][2*j]   = SECRET_NUM;
                d.y.vals[i][2*j+1] = SECRET_NUM;
            }
        }
        fclose(fp1);
        fclose(fp2);

        free_image(im1);
        free_image(im2);
    }
    if(m) free(paths);
    return d;
}

data load_data_swag(char **paths, int n, int classes, float jitter)
{
    int index = rand()%n;
    char *random_path = paths[index];

    image orig = load_image_color(random_path, 0, 0);
    int h = orig.h;
    int w = orig.w;

    data d = {0};
    d.shallow = 0;
    d.w = w;
    d.h = h;

    d.X.rows = 1;
    d.X.vals = calloc(d.X.rows, sizeof(float*));
    d.X.cols = h*w*3;

    int k = (4+classes)*30;
    d.y = make_matrix(1, k);

    int dw = w*jitter;
    int dh = h*jitter;

    int pleft  = rand_uniform(-dw, dw);
    int pright = rand_uniform(-dw, dw);
    int ptop   = rand_uniform(-dh, dh);
    int pbot   = rand_uniform(-dh, dh);

    int swidth =  w - pleft - pright;
    int sheight = h - ptop - pbot;

    float sx = (float)swidth  / w;
    float sy = (float)sheight / h;

    int flip = rand()%2;
    image cropped = crop_image(orig, pleft, ptop, swidth, sheight);

    float dx = ((float)pleft/w)/sx;
    float dy = ((float)ptop /h)/sy;

    image sized = resize_image(cropped, w, h);
    if(flip) flip_image(sized);
    d.X.vals[0] = sized.data;

    fill_truth_swag(random_path, d.y.vals[0], classes, flip, dx, dy, 1./sx, 1./sy);

    free_image(orig);
    free_image(cropped);

    return d;
}

data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, int classes, float jitter, float hue, float saturation, float exposure)
{
    char **random_paths = get_random_paths(paths, n, m);
    int i;
    data d = {0};
    d.shallow = 0;

    d.X.rows = n;
    d.X.vals = calloc(d.X.rows, sizeof(float*));
    d.X.cols = h*w*3;

    d.y = make_matrix(n, 5*boxes);
    for(i = 0; i < n; ++i){
        image orig = load_image_color(random_paths[i], 0, 0);
        image sized = make_image(w, h, orig.c);
        fill_image(sized, .5);
        
        float dw = jitter * orig.w;
        float dh = jitter * orig.h;

        float new_ar = (orig.w + rand_uniform(-dw, dw)) / (orig.h + rand_uniform(-dh, dh));
        float scale = rand_uniform(.25, 2);

        float nw, nh;
        
        if(new_ar < 1){
            nh = scale * h;
            nw = nh * new_ar;
        } else {
            nw = scale * w;
            nh = nw / new_ar;
        }

        float dx = rand_uniform(0, w - nw);
        float dy = rand_uniform(0, h - nh);

        place_image(orig, nw, nh, dx, dy, sized);

        random_distort_image(sized, hue, saturation, exposure);
        int flip = rand()%2;
        if(flip) flip_image(sized);
        d.X.vals[i] = sized.data;


        fill_truth_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, -dx/w, -dy/h, nw/w, nh/h);

        free_image(orig);
    }
    free(random_paths);
    return d;
}

void *load_thread(void *ptr)
{
    //printf("Loading data: %d\n", rand());
    load_args a = *(struct load_args*)ptr;
    if(a.exposure == 0) a.exposure = 1;
    if(a.saturation == 0) a.saturation = 1;
    if(a.aspect == 0) a.aspect = 1;

    if (a.type == OLD_CLASSIFICATION_DATA){
        *a.d = load_data_old(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
    } else if (a.type == REGRESSION_DATA){
        *a.d = load_data_regression(a.paths, a.n, a.m, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
    } else if (a.type == CLASSIFICATION_DATA){
        *a.d = load_data_augment(a.paths, a.n, a.m, a.labels, a.classes, a.hierarchy, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
    } else if (a.type == SUPER_DATA){
        *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale);
    } else if (a.type == WRITING_DATA){
        *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
    } else if (a.type == REGION_DATA){
        *a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
    } else if (a.type == DETECTION_DATA){
        *a.d = load_data_detection(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes, a.jitter, a.hue, a.saturation, a.exposure);
    } else if (a.type == SWAG_DATA){
        *a.d = load_data_swag(a.paths, a.n, a.classes, a.jitter);
    } else if (a.type == COMPARE_DATA){
        *a.d = load_data_compare(a.n, a.paths, a.m, a.classes, a.w, a.h);
    } else if (a.type == IMAGE_DATA){
        *(a.im) = load_image_color(a.path, 0, 0);
        *(a.resized) = resize_image(*(a.im), a.w, a.h);
    } else if (a.type == LETTERBOX_DATA){
        *(a.im) = load_image_color(a.path, 0, 0);
        *(a.resized) = letterbox_image(*(a.im), a.w, a.h);
    } else if (a.type == TAG_DATA){
        *a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
    }
    free(ptr);
    return 0;
}

pthread_t load_data_in_thread(load_args args)
{
    pthread_t thread;
    struct load_args *ptr = calloc(1, sizeof(struct load_args));
    *ptr = args;
    if(pthread_create(&thread, 0, load_thread, ptr)) error("Thread creation failed");
    return thread;
}

void *load_threads(void *ptr)
{
    int i;
    load_args args = *(load_args *)ptr;
    if (args.threads == 0) args.threads = 1;
    data *out = args.d;
    int total = args.n;
    free(ptr);
    data *buffers = calloc(args.threads, sizeof(data));
    pthread_t *threads = calloc(args.threads, sizeof(pthread_t));
    for(i = 0; i < args.threads; ++i){
        args.d = buffers + i;
        args.n = (i+1) * total/args.threads - i * total/args.threads;
        threads[i] = load_data_in_thread(args);
    }
    for(i = 0; i < args.threads; ++i){
        pthread_join(threads[i], 0);
    }
    *out = concat_datas(buffers, args.threads);
    out->shallow = 0;
    for(i = 0; i < args.threads; ++i){
        buffers[i].shallow = 1;
        free_data(buffers[i]);
    }
    free(buffers);
    free(threads);
    return 0;
}

pthread_t load_data(load_args args)
{
    pthread_t thread;
    struct load_args *ptr = calloc(1, sizeof(struct load_args));
    *ptr = args;
    if(pthread_create(&thread, 0, load_threads, ptr)) error("Thread creation failed");
    return thread;
}

data load_data_writing(char **paths, int n, int m, int w, int h, int out_w, int out_h)
{
    if(m) paths = get_random_paths(paths, n, m);
    char **replace_paths = find_replace_paths(paths, n, ".png", "-label.png");
    data d = {0};
    d.shallow = 0;
    d.X = load_image_paths(paths, n, w, h);
    d.y = load_image_paths_gray(replace_paths, n, out_w, out_h);
    if(m) free(paths);
    int i;
    for(i = 0; i < n; ++i) free(replace_paths[i]);
    free(replace_paths);
    return d;
}

data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h)
{
    if(m) paths = get_random_paths(paths, n, m);
    data d = {0};
    d.shallow = 0;
    d.X = load_image_paths(paths, n, w, h);
    d.y = load_labels_paths(paths, n, labels, k, 0);
    if(m) free(paths);
    return d;
}

/*
   data load_data_study(char **paths, int n, int m, char **labels, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
   {
   data d = {0};
   d.indexes = calloc(n, sizeof(int));
   if(m) paths = get_random_paths_indexes(paths, n, m, d.indexes);
   d.shallow = 0;
   d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
   d.y = load_labels_paths(paths, n, labels, k);
   if(m) free(paths);
   return d;
   }
 */

data load_data_super(char **paths, int n, int m, int w, int h, int scale)
{
    if(m) paths = get_random_paths(paths, n, m);
    data d = {0};
    d.shallow = 0;

    int i;
    d.X.rows = n;
    d.X.vals = calloc(n, sizeof(float*));
    d.X.cols = w*h*3;

    d.y.rows = n;
    d.y.vals = calloc(n, sizeof(float*));
    d.y.cols = w*scale * h*scale * 3;

    for(i = 0; i < n; ++i){
        image im = load_image_color(paths[i], 0, 0);
        image crop = random_crop_image(im, w*scale, h*scale);
        int flip = rand()%2;
        if (flip) flip_image(crop);
        image resize = resize_image(crop, w, h);
        d.X.vals[i] = resize.data;
        d.y.vals[i] = crop.data;
        free_image(im);
    }

    if(m) free(paths);
    return d;
}

data load_data_regression(char **paths, int n, int m, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
{
    if(m) paths = get_random_paths(paths, n, m);
    data d = {0};
    d.shallow = 0;
    d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
    d.y = load_regression_labels_paths(paths, n);
    if(m) free(paths);
    return d;
}

data load_data_augment(char **paths, int n, int m, char **labels, int k, tree *hierarchy, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
{
    if(m) paths = get_random_paths(paths, n, m);
    data d = {0};
    d.shallow = 0;
    d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
    d.y = load_labels_paths(paths, n, labels, k, hierarchy);
    if(m) free(paths);
    return d;
}

data load_data_tag(char **paths, int n, int m, int k, int min, int max, int size, float angle, float aspect, float hue, float saturation, float exposure)
{
    if(m) paths = get_random_paths(paths, n, m);
    data d = {0};
    d.w = size;
    d.h = size;
    d.shallow = 0;
    d.X = load_image_augment_paths(paths, n, min, max, size, angle, aspect, hue, saturation, exposure);
    d.y = load_tags_paths(paths, n, k);
    if(m) free(paths);
    return d;
}

matrix concat_matrix(matrix m1, matrix m2)
{
    int i, count = 0;
    matrix m;
    m.cols = m1.cols;
    m.rows = m1.rows+m2.rows;
    m.vals = calloc(m1.rows + m2.rows, sizeof(float*));
    for(i = 0; i < m1.rows; ++i){
        m.vals[count++] = m1.vals[i];
    }
    for(i = 0; i < m2.rows; ++i){
        m.vals[count++] = m2.vals[i];
    }
    return m;
}

data concat_data(data d1, data d2)
{
    data d = {0};
    d.shallow = 1;
    d.X = concat_matrix(d1.X, d2.X);
    d.y = concat_matrix(d1.y, d2.y);
    return d;
}

data concat_datas(data *d, int n)
{
    int i;
    data out = {0};
    for(i = 0; i < n; ++i){
        data new = concat_data(d[i], out);
        free_data(out);
        out = new;
    }
    return out;
}

data load_categorical_data_csv(char *filename, int target, int k)
{
    data d = {0};
    d.shallow = 0;
    matrix X = csv_to_matrix(filename);
    float *truth_1d = pop_column(&X, target);
    float **truth = one_hot_encode(truth_1d, X.rows, k);
    matrix y;
    y.rows = X.rows;
    y.cols = k;
    y.vals = truth;
    d.X = X;
    d.y = y;
    free(truth_1d);
    return d;
}

data load_cifar10_data(char *filename)
{
    data d = {0};
    d.shallow = 0;
    long i,j;
    matrix X = make_matrix(10000, 3072);
    matrix y = make_matrix(10000, 10);
    d.X = X;
    d.y = y;

    FILE *fp = fopen(filename, "rb");
    if(!fp) file_error(filename);
    for(i = 0; i < 10000; ++i){
        unsigned char bytes[3073];
        fread(bytes, 1, 3073, fp);
        int class = bytes[0];
        y.vals[i][class] = 1;
        for(j = 0; j < X.cols; ++j){
            X.vals[i][j] = (double)bytes[j+1];
        }
    }
    scale_data_rows(d, 1./255);
    //normalize_data_rows(d);
    fclose(fp);
    return d;
}

void get_random_batch(data d, int n, float *X, float *y)
{
    int j;
    for(j = 0; j < n; ++j){
        int index = rand()%d.X.rows;
        memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
        memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
    }
}

void get_next_batch(data d, int n, int offset, float *X, float *y)
{
    int j;
    for(j = 0; j < n; ++j){
        int index = offset + j;
        memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
        memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
    }
}

void smooth_data(data d)
{
    int i, j;
    float scale = 1. / d.y.cols;
    float eps = .1;
    for(i = 0; i < d.y.rows; ++i){
        for(j = 0; j < d.y.cols; ++j){
            d.y.vals[i][j] = eps * scale + (1-eps) * d.y.vals[i][j];
        }
    }
}

data load_all_cifar10()
{
    data d = {0};
    d.shallow = 0;
    int i,j,b;
    matrix X = make_matrix(50000, 3072);
    matrix y = make_matrix(50000, 10);
    d.X = X;
    d.y = y;


    for(b = 0; b < 5; ++b){
        char buff[256];
        sprintf(buff, "data/cifar/cifar-10-batches-bin/data_batch_%d.bin", b+1);
        FILE *fp = fopen(buff, "rb");
        if(!fp) file_error(buff);
        for(i = 0; i < 10000; ++i){
            unsigned char bytes[3073];
            fread(bytes, 1, 3073, fp);
            int class = bytes[0];
            y.vals[i+b*10000][class] = 1;
            for(j = 0; j < X.cols; ++j){
                X.vals[i+b*10000][j] = (double)bytes[j+1];
            }
        }
        fclose(fp);
    }
    //normalize_data_rows(d);
    scale_data_rows(d, 1./255);
    smooth_data(d);
    return d;
}

data load_go(char *filename)
{
    FILE *fp = fopen(filename, "rb");
    matrix X = make_matrix(3363059, 361);
    matrix y = make_matrix(3363059, 361);
    int row, col;

    if(!fp) file_error(filename);
    char *label;
    int count = 0;
    while((label = fgetl(fp))){
        int i;
        if(count == X.rows){
            X = resize_matrix(X, count*2);
            y = resize_matrix(y, count*2);
        }
        sscanf(label, "%d %d", &row, &col);
        char *board = fgetl(fp);

        int index = row*19 + col;
        y.vals[count][index] = 1;

        for(i = 0; i < 19*19; ++i){
            float val = 0;
            if(board[i] == '1') val = 1;
            else if(board[i] == '2') val = -1;
            X.vals[count][i] = val;
        }
        ++count;
        free(label);
        free(board);
    }
    X = resize_matrix(X, count);
    y = resize_matrix(y, count);

    data d = {0};
    d.shallow = 0;
    d.X = X;
    d.y = y;


    fclose(fp);

    return d;
}


void randomize_data(data d)
{
    int i;
    for(i = d.X.rows-1; i > 0; --i){
        int index = rand()%i;
        float *swap = d.X.vals[index];
        d.X.vals[index] = d.X.vals[i];
        d.X.vals[i] = swap;

        swap = d.y.vals[index];
        d.y.vals[index] = d.y.vals[i];
        d.y.vals[i] = swap;
    }
}

void scale_data_rows(data d, float s)
{
    int i;
    for(i = 0; i < d.X.rows; ++i){
        scale_array(d.X.vals[i], d.X.cols, s);
    }
}

void translate_data_rows(data d, float s)
{
    int i;
    for(i = 0; i < d.X.rows; ++i){
        translate_array(d.X.vals[i], d.X.cols, s);
    }
}

data copy_data(data d)
{
    data c = {0};
    c.w = d.w;
    c.h = d.h;
    c.shallow = 0;
    c.num_boxes = d.num_boxes;
    c.boxes = d.boxes;
    c.X = copy_matrix(d.X);
    c.y = copy_matrix(d.y);
    return c;
}

void normalize_data_rows(data d)
{
    int i;
    for(i = 0; i < d.X.rows; ++i){
        normalize_array(d.X.vals[i], d.X.cols);
    }
}

data get_data_part(data d, int part, int total)
{
    data p = {0};
    p.shallow = 1;
    p.X.rows = d.X.rows * (part + 1) / total - d.X.rows * part / total;
    p.y.rows = d.y.rows * (part + 1) / total - d.y.rows * part / total;
    p.X.cols = d.X.cols;
    p.y.cols = d.y.cols;
    p.X.vals = d.X.vals + d.X.rows * part / total;
    p.y.vals = d.y.vals + d.y.rows * part / total;
    return p;
}

data get_random_data(data d, int num)
{
    data r = {0};
    r.shallow = 1;

    r.X.rows = num;
    r.y.rows = num;

    r.X.cols = d.X.cols;
    r.y.cols = d.y.cols;

    r.X.vals = calloc(num, sizeof(float *));
    r.y.vals = calloc(num, sizeof(float *));

    int i;
    for(i = 0; i < num; ++i){
        int index = rand()%d.X.rows;
        r.X.vals[i] = d.X.vals[index];
        r.y.vals[i] = d.y.vals[index];
    }
    return r;
}

data *split_data(data d, int part, int total)
{
    data *split = calloc(2, sizeof(data));
    int i;
    int start = part*d.X.rows/total;
    int end = (part+1)*d.X.rows/total;
    data train;
    data test;
    train.shallow = test.shallow = 1;

    test.X.rows = test.y.rows = end-start;
    train.X.rows = train.y.rows = d.X.rows - (end-start);
    train.X.cols = test.X.cols = d.X.cols;
    train.y.cols = test.y.cols = d.y.cols;

    train.X.vals = calloc(train.X.rows, sizeof(float*));
    test.X.vals = calloc(test.X.rows, sizeof(float*));
    train.y.vals = calloc(train.y.rows, sizeof(float*));
    test.y.vals = calloc(test.y.rows, sizeof(float*));

    for(i = 0; i < start; ++i){
        train.X.vals[i] = d.X.vals[i];
        train.y.vals[i] = d.y.vals[i];
    }
    for(i = start; i < end; ++i){
        test.X.vals[i-start] = d.X.vals[i];
        test.y.vals[i-start] = d.y.vals[i];
    }
    for(i = end; i < d.X.rows; ++i){
        train.X.vals[i-(end-start)] = d.X.vals[i];
        train.y.vals[i-(end-start)] = d.y.vals[i];
    }
    split[0] = train;
    split[1] = test;
    return split;
}