#include "network.h"
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#include "utils.h"
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#include "parser.h"
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#ifdef OPENCV
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#include "opencv2/highgui/highgui_c.h"
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#endif
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void train_writing(char *cfgfile, char *weightfile)
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{
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char *backup_directory = "/home/pjreddie/backup/";
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data_seed = time(0);
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srand(time(0));
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float avg_loss = -1;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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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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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = net.batch*net.subdivisions;
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list *plist = get_paths("figures.list");
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char **paths = (char **)list_to_array(plist);
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clock_t time;
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int N = plist->size;
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printf("N: %d\n", N);
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data train, buffer;
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.paths = paths;
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args.n = imgs;
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args.m = N;
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args.downsample = 1;
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args.d = &buffer;
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args.type = WRITING_DATA;
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pthread_t load_thread = load_data_in_thread(args);
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int epoch = (*net.seen)/N;
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while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
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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_in_thread(args);
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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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/*
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image pred = float_to_image(64, 64, 1, out);
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print_image(pred);
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*/
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/*
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image im = float_to_image(256, 256, 3, train.X.vals[0]);
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image lab = float_to_image(64, 64, 1, train.y.vals[0]);
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image pred = float_to_image(64, 64, 1, out);
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show_image(im, "image");
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show_image(lab, "label");
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print_image(lab);
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show_image(pred, "pred");
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cvWaitKey(0);
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*/
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
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free_data(train);
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if(*net.seen/N > epoch){
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epoch = *net.seen/N;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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save_weights(net, buff);
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}
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}
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}
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void test_writing(char *cfgfile, char *weightfile, char *outfile)
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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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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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fgets(filename, 256, stdin);
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strtok(filename, "\n");
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image im = load_image_color(filename, 0, 0);
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//image im = load_image_color("/home/pjreddie/darknet/data/figs/C02-1001-Figure-1.png", 0, 0);
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image sized = resize_image(im, net.w, net.h);
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printf("%d %d %d\n", im.h, im.w, im.c);
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float *X = sized.data;
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time=clock();
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network_predict(net, X);
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printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
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image pred = get_network_image(net);
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if (outfile) {
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printf("Save image as %s.png (shape: %d %d)\n", outfile, pred.w, pred.h);
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save_image(pred, outfile);
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} else {
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show_image(pred, "prediction");
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#ifdef OPENCV
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cvWaitKey(0);
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cvDestroyAllWindows();
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#endif
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}
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free_image(im);
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free_image(sized);
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}
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void run_writing(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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char *outfile = (argc > 5) ? argv[5] : 0;
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if(0==strcmp(argv[2], "train")) train_writing(cfg, weights);
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else if(0==strcmp(argv[2], "test")) test_writing(cfg, weights, outfile);
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}
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