#include "network.h"
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#include "utils.h"
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#include "parser.h"
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char *class_names[] = {"bg", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
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#define AMNT 3
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void draw_detection(image im, float *box, int side)
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{
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int classes = 21;
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int elems = 4+classes;
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int j;
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int r, c;
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for(r = 0; r < side; ++r){
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for(c = 0; c < side; ++c){
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j = (r*side + c) * elems;
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//printf("%d\n", j);
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//printf("Prob: %f\n", box[j]);
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int class = max_index(box+j, classes);
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if(box[j+class] > .02 || 1){
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//int z;
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//for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+z], class_names[z]);
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printf("%f %s\n", box[j+class], class_names[class]);
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float red = get_color(0,class,classes);
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float green = get_color(1,class,classes);
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float blue = get_color(2,class,classes);
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j += classes;
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int d = im.w/side;
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int y = r*d+box[j]*d;
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int x = c*d+box[j+1]*d;
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int h = box[j+2]*im.h;
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int w = box[j+3]*im.w;
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draw_box(im, x-w/2, y-h/2, x+w/2, y+h/2,red,green,blue);
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}
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}
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}
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//printf("Done\n");
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show_image(im, "box");
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cvWaitKey(0);
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}
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void train_detection(char *cfgfile, char *weightfile)
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{
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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float avg_loss = -1;
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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//net.seen = 0;
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 128;
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srand(time(0));
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//srand(23410);
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int i = net.seen/imgs;
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list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
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char **paths = (char **)list_to_array(plist);
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printf("%d\n", plist->size);
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data train, buffer;
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int im_dim = 512;
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int jitter = 64;
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int classes = 20;
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int background = 1;
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pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
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clock_t time;
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while(1){
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
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/*
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image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[114]);
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draw_detection(im, train.y.vals[114], 7);
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show_image(im, "truth");
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cvWaitKey(0);
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*/
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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float loss = train_network(net, train);
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net.seen += imgs;
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if (avg_loss < 0) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
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if(i%100==0){
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
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save_weights(net, buff);
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}
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free_data(train);
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}
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}
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void validate_detection(char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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list *plist = get_paths("/home/pjreddie/data/voc/val.txt");
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//list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
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char **paths = (char **)list_to_array(plist);
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int im_size = 448;
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int classes = 20;
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int background = 0;
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int nuisance = 1;
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int num_output = 7*7*(4+classes+background+nuisance);
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int m = plist->size;
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int i = 0;
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int splits = 100;
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int num = (i+1)*m/splits - i*m/splits;
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fprintf(stderr, "%d\n", m);
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data val, buffer;
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pthread_t load_thread = load_data_thread(paths, num, 0, 0, num_output, im_size, im_size, &buffer);
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clock_t time;
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for(i = 1; i <= splits; ++i){
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time=clock();
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pthread_join(load_thread, 0);
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val = buffer;
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num = (i+1)*m/splits - i*m/splits;
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char **part = paths+(i*m/splits);
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if(i != splits) load_thread = load_data_thread(part, num, 0, 0, num_output, im_size, im_size, &buffer);
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fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
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matrix pred = network_predict_data(net, val);
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int j, k, class;
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for(j = 0; j < pred.rows; ++j){
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for(k = 0; k < pred.cols; k += classes+4+background+nuisance){
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float scale = 1.;
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if(nuisance) scale = 1.-pred.vals[j][k];
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for(class = 0; class < classes; ++class){
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int index = (k)/(classes+4+background+nuisance);
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int r = index/7;
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int c = index%7;
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int ci = k+classes+background+nuisance;
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float y = (r + pred.vals[j][ci + 0])/7.;
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float x = (c + pred.vals[j][ci + 1])/7.;
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float h = pred.vals[j][ci + 2];
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float w = pred.vals[j][ci + 3];
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printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, scale*pred.vals[j][k+class+background+nuisance], y, x, h, w);
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}
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}
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}
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time=clock();
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free_data(val);
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}
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}
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void test_detection(char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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int im_size = 448;
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set_batch_network(&net, 1);
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srand(2222222);
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clock_t time;
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char filename[256];
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while(1){
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fgets(filename, 256, stdin);
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strtok(filename, "\n");
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image im = load_image_color(filename, im_size, im_size);
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translate_image(im, -128);
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scale_image(im, 1/128.);
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printf("%d %d %d\n", im.h, im.w, im.c);
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float *X = im.data;
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time=clock();
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float *predictions = network_predict(net, X);
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printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
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draw_detection(im, predictions, 7);
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free_image(im);
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}
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}
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void run_detection(int argc, char **argv)
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{
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if(argc < 4){
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fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
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return;
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}
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char *cfg = argv[3];
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char *weights = (argc > 4) ? argv[4] : 0;
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if(0==strcmp(argv[2], "test")) test_detection(cfg, weights);
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else if(0==strcmp(argv[2], "train")) train_detection(cfg, weights);
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else if(0==strcmp(argv[2], "valid")) validate_detection(cfg, weights);
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}
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