#include "data.h" #include "utils.h" #include "image.h" #include "cuda.h" #include #include #include 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 < .01 || h < .01) 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 < .01 || h < .01)) 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) 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_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); 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_detection(random_paths[i], boxes, d.y.vals[i], classes, flip, dx, dy, 1./sx, 1./sy); free_image(orig); free_image(cropped); } 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 == 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 == 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_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]; } } //translate_data_rows(d, -128); 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); //translate_data_rows(d, -128); 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); } } 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; }