#include "data.h"
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#include "utils.h"
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#include "image.h"
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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struct load_args{
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char **paths;
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int n;
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int m;
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char **labels;
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int k;
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int h;
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int w;
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int nh;
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int nw;
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int jitter;
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int classes;
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int background;
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data *d;
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};
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list *get_paths(char *filename)
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{
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char *path;
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FILE *file = fopen(filename, "r");
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if(!file) file_error(filename);
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list *lines = make_list();
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while((path=fgetl(file))){
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list_insert(lines, path);
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}
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fclose(file);
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return lines;
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}
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char **get_random_paths(char **paths, int n, int m)
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{
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char **random_paths = calloc(n, sizeof(char*));
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int i;
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for(i = 0; i < n; ++i){
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int index = rand()%m;
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random_paths[i] = paths[index];
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if(i == 0) printf("%s\n", paths[index]);
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}
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return random_paths;
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}
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matrix load_image_paths(char **paths, int n, int h, int w)
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{
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int i;
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matrix X;
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X.rows = n;
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X.vals = calloc(X.rows, sizeof(float*));
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X.cols = 0;
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for(i = 0; i < n; ++i){
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image im = load_image_color(paths[i], h, w);
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X.vals[i] = im.data;
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X.cols = im.h*im.w*im.c;
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}
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return X;
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}
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typedef struct box{
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int id;
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float x,y,w,h;
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} box;
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box *read_boxes(char *filename, int *n)
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{
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box *boxes = calloc(1, sizeof(box));
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FILE *file = fopen(filename, "r");
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if(!file) file_error(filename);
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float x, y, h, w;
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int id;
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int count = 0;
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while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
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boxes = realloc(boxes, (count+1)*sizeof(box));
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boxes[count].id = id;
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boxes[count].x = x;
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boxes[count].y = y;
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boxes[count].h = h;
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boxes[count].w = w;
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++count;
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}
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fclose(file);
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*n = count;
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return boxes;
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}
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void randomize_boxes(box *b, int n)
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{
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int i;
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for(i = 0; i < n; ++i){
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box swap = b[i];
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int index = rand()%n;
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b[i] = b[index];
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b[index] = swap;
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}
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}
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void fill_truth_detection(char *path, float *truth, int classes, int height, int width, int num_height, int num_width, int dy, int dx, int jitter, int flip, int background)
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{
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int box_height = height/num_height;
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int box_width = width/num_width;
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char *labelpath = find_replace(path, "VOC2012/JPEGImages", "labels");
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labelpath = find_replace(labelpath, ".jpg", ".txt");
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int count = 0;
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box *boxes = read_boxes(labelpath, &count);
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randomize_boxes(boxes, count);
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float x, y, h, w;
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int id;
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int i;
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if(background){
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for(i = 0; i < num_height*num_width*(4+classes+background); i += 4+classes+background){
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truth[i] = 1;
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}
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}
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for(i = 0; i < count; ++i){
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x = boxes[i].x;
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y = boxes[i].y;
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w = boxes[i].w;
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h = boxes[i].h;
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id = boxes[i].id;
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if(flip) x = 1-x;
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x *= width + jitter;
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y *= height + jitter;
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x -= dx;
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y -= dy;
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int i = x/box_width;
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int j = y/box_height;
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if(i < 0) i = 0;
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if(i >= num_width) i = num_width-1;
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if(j < 0) j = 0;
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if(j >= num_height) j = num_height-1;
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float dw = (x - i*box_width)/box_width;
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float dh = (y - j*box_height)/box_height;
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int index = (i+j*num_width)*(4+classes+background);
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if(truth[index+classes+background]) continue;
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if(background) truth[index++] = 0;
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truth[index+id] = 1;
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index += classes;
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truth[index++] = dh;
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truth[index++] = dw;
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truth[index++] = h*(height+jitter)/height;
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truth[index++] = w*(width+jitter)/width;
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}
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free(boxes);
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}
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#define NUMCHARS 37
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void print_letters(float *pred, int n)
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{
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int i;
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for(i = 0; i < n; ++i){
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int index = max_index(pred+i*NUMCHARS, NUMCHARS);
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printf("%c", int_to_alphanum(index));
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}
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printf("\n");
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}
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void fill_truth_captcha(char *path, int n, float *truth)
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{
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char *begin = strrchr(path, '/');
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++begin;
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int i;
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for(i = 0; i < strlen(begin) && i < n && begin[i] != '.'; ++i){
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int index = alphanum_to_int(begin[i]);
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if(index > 35) printf("Bad %c\n", begin[i]);
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truth[i*NUMCHARS+index] = 1;
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}
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for(;i < n; ++i){
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truth[i*NUMCHARS + NUMCHARS-1] = 1;
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}
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}
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data load_data_captcha(char **paths, int n, int m, int k, int h, int w)
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{
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if(m) paths = get_random_paths(paths, n, m);
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data d;
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d.shallow = 0;
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d.X = load_image_paths(paths, n, h, w);
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d.y = make_matrix(n, k*NUMCHARS);
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int i;
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for(i = 0; i < n; ++i){
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fill_truth_captcha(paths[i], k, d.y.vals[i]);
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}
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if(m) free(paths);
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return d;
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}
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data load_data_captcha_encode(char **paths, int n, int m, int h, int w)
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{
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if(m) paths = get_random_paths(paths, n, m);
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data d;
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d.shallow = 0;
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d.X = load_image_paths(paths, n, h, w);
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d.X.cols = 17100;
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d.y = d.X;
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if(m) free(paths);
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return d;
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}
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void fill_truth(char *path, char **labels, int k, float *truth)
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{
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int i;
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memset(truth, 0, k*sizeof(float));
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int count = 0;
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for(i = 0; i < k; ++i){
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if(strstr(path, labels[i])){
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truth[i] = 1;
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++count;
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}
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}
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if(count != 1) printf("%d, %s\n", count, path);
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}
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matrix load_labels_paths(char **paths, int n, char **labels, int k)
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{
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matrix y = make_matrix(n, k);
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int i;
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for(i = 0; i < n && labels; ++i){
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fill_truth(paths[i], labels, k, y.vals[i]);
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}
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return y;
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}
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data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w)
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{
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list *plist = get_paths(filename);
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char **paths = (char **)list_to_array(plist);
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int n = plist->size;
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data d;
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d.shallow = 0;
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d.X = load_image_paths(paths, n, h, w);
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d.y = load_labels_paths(paths, n, labels, k);
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free_list_contents(plist);
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free_list(plist);
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free(paths);
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return d;
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}
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char **get_labels(char *filename)
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{
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list *plist = get_paths(filename);
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char **labels = (char **)list_to_array(plist);
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free_list(plist);
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return labels;
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}
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void free_data(data d)
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{
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if(!d.shallow){
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free_matrix(d.X);
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free_matrix(d.y);
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}else{
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free(d.X.vals);
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free(d.y.vals);
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}
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}
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data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, int background)
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{
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char **random_paths = get_random_paths(paths, n, m);
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int i;
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data d;
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d.shallow = 0;
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d.X = load_image_paths(random_paths, n, h, w);
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int k = nh*nw*(4+classes+background);
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d.y = make_matrix(n, k);
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for(i = 0; i < n; ++i){
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int dx = rand()%jitter;
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int dy = rand()%jitter;
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int flip = rand()%2;
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fill_truth_detection(random_paths[i], d.y.vals[i], classes, h-jitter, w-jitter, nh, nw, dy, dx, jitter, flip, background);
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image a = float_to_image(h, w, 3, d.X.vals[i]);
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if(flip) flip_image(a);
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jitter_image(a,h-jitter,w-jitter,dy,dx);
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}
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d.X.cols = (h-jitter)*(w-jitter)*3;
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free(random_paths);
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return d;
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}
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void *load_detection_thread(void *ptr)
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{
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printf("Loading data: %d\n", rand());
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struct load_args a = *(struct load_args*)ptr;
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*a.d = load_data_detection_jitter_random(a.n, a.paths, a.m, a.classes, a.h, a.w, a.nh, a.nw, a.jitter, a.background);
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translate_data_rows(*a.d, -128);
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scale_data_rows(*a.d, 1./128);
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free(ptr);
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return 0;
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}
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pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, int background, data *d)
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{
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pthread_t thread;
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struct load_args *args = calloc(1, sizeof(struct load_args));
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args->n = n;
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args->paths = paths;
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args->m = m;
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args->h = h;
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args->w = w;
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args->nh = nh;
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args->nw = nw;
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args->classes = classes;
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args->jitter = jitter;
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args->background = background;
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args->d = d;
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if(pthread_create(&thread, 0, load_detection_thread, args)) {
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error("Thread creation failed");
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}
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return thread;
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}
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data load_data(char **paths, int n, int m, char **labels, int k, int h, int w)
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{
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if(m) paths = get_random_paths(paths, n, m);
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data d;
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d.shallow = 0;
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d.X = load_image_paths(paths, n, h, w);
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d.y = load_labels_paths(paths, n, labels, k);
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if(m) free(paths);
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return d;
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}
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void *load_in_thread(void *ptr)
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{
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struct load_args a = *(struct load_args*)ptr;
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*a.d = load_data(a.paths, a.n, a.m, a.labels, a.k, a.h, a.w);
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translate_data_rows(*a.d, -128);
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scale_data_rows(*a.d, 1./128);
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free(ptr);
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return 0;
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}
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pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d)
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{
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pthread_t thread;
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struct load_args *args = calloc(1, sizeof(struct load_args));
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args->n = n;
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args->paths = paths;
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args->m = m;
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args->labels = labels;
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args->k = k;
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args->h = h;
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args->w = w;
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args->d = d;
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if(pthread_create(&thread, 0, load_in_thread, args)) {
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error("Thread creation failed");
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}
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return thread;
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}
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data load_categorical_data_csv(char *filename, int target, int k)
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{
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data d;
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d.shallow = 0;
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matrix X = csv_to_matrix(filename);
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float *truth_1d = pop_column(&X, target);
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float **truth = one_hot_encode(truth_1d, X.rows, k);
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matrix y;
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y.rows = X.rows;
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y.cols = k;
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y.vals = truth;
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d.X = X;
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d.y = y;
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free(truth_1d);
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return d;
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}
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data load_cifar10_data(char *filename)
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{
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data d;
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d.shallow = 0;
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long i,j;
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matrix X = make_matrix(10000, 3072);
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matrix y = make_matrix(10000, 10);
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d.X = X;
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d.y = y;
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FILE *fp = fopen(filename, "rb");
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if(!fp) file_error(filename);
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for(i = 0; i < 10000; ++i){
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unsigned char bytes[3073];
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fread(bytes, 1, 3073, fp);
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int class = bytes[0];
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y.vals[i][class] = 1;
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for(j = 0; j < X.cols; ++j){
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X.vals[i][j] = (double)bytes[j+1];
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}
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}
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translate_data_rows(d, -144);
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scale_data_rows(d, 1./128);
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//normalize_data_rows(d);
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fclose(fp);
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return d;
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}
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void get_random_batch(data d, int n, float *X, float *y)
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{
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int j;
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for(j = 0; j < n; ++j){
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int index = rand()%d.X.rows;
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memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
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memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
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}
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}
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void get_next_batch(data d, int n, int offset, float *X, float *y)
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{
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int j;
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for(j = 0; j < n; ++j){
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int index = offset + j;
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memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
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memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
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}
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}
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data load_all_cifar10()
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{
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data d;
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d.shallow = 0;
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int i,j,b;
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matrix X = make_matrix(50000, 3072);
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matrix y = make_matrix(50000, 10);
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d.X = X;
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d.y = y;
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for(b = 0; b < 5; ++b){
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char buff[256];
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sprintf(buff, "data/cifar10/data_batch_%d.bin", b+1);
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FILE *fp = fopen(buff, "rb");
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if(!fp) file_error(buff);
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for(i = 0; i < 10000; ++i){
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unsigned char bytes[3073];
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fread(bytes, 1, 3073, fp);
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int class = bytes[0];
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y.vals[i+b*10000][class] = 1;
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for(j = 0; j < X.cols; ++j){
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X.vals[i+b*10000][j] = (double)bytes[j+1];
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}
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}
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fclose(fp);
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}
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//normalize_data_rows(d);
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translate_data_rows(d, -144);
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scale_data_rows(d, 1./128);
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return d;
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}
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void randomize_data(data d)
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{
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int i;
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for(i = d.X.rows-1; i > 0; --i){
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int index = rand()%i;
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float *swap = d.X.vals[index];
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d.X.vals[index] = d.X.vals[i];
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d.X.vals[i] = swap;
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swap = d.y.vals[index];
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d.y.vals[index] = d.y.vals[i];
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d.y.vals[i] = swap;
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}
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}
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void scale_data_rows(data d, float s)
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{
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int i;
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for(i = 0; i < d.X.rows; ++i){
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scale_array(d.X.vals[i], d.X.cols, s);
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}
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}
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void translate_data_rows(data d, float s)
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{
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int i;
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for(i = 0; i < d.X.rows; ++i){
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translate_array(d.X.vals[i], d.X.cols, s);
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}
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}
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void normalize_data_rows(data d)
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{
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int i;
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for(i = 0; i < d.X.rows; ++i){
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normalize_array(d.X.vals[i], d.X.cols);
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}
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}
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data *split_data(data d, int part, int total)
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{
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data *split = calloc(2, sizeof(data));
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int i;
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int start = part*d.X.rows/total;
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int end = (part+1)*d.X.rows/total;
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data train;
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data test;
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train.shallow = test.shallow = 1;
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test.X.rows = test.y.rows = end-start;
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train.X.rows = train.y.rows = d.X.rows - (end-start);
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train.X.cols = test.X.cols = d.X.cols;
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train.y.cols = test.y.cols = d.y.cols;
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train.X.vals = calloc(train.X.rows, sizeof(float*));
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test.X.vals = calloc(test.X.rows, sizeof(float*));
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train.y.vals = calloc(train.y.rows, sizeof(float*));
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test.y.vals = calloc(test.y.rows, sizeof(float*));
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for(i = 0; i < start; ++i){
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train.X.vals[i] = d.X.vals[i];
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train.y.vals[i] = d.y.vals[i];
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}
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for(i = start; i < end; ++i){
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test.X.vals[i-start] = d.X.vals[i];
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test.y.vals[i-start] = d.y.vals[i];
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}
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for(i = end; i < d.X.rows; ++i){
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train.X.vals[i-(end-start)] = d.X.vals[i];
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train.y.vals[i-(end-start)] = d.y.vals[i];
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}
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split[0] = train;
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split[1] = test;
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return split;
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}
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