#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_tag(char *cfgfile, char *weightfile, int clear)
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{
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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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char *backup_directory = "/home/pjreddie/backup/";
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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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if(clear) *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 = 1024;
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list *plist = get_paths("/home/pjreddie/tag/train.list");
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char **paths = (char **)list_to_array(plist);
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printf("%d\n", plist->size);
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int N = plist->size;
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clock_t time;
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pthread_t load_thread;
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data train;
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data 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.min = net.w;
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args.max = net.max_crop;
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args.size = net.w;
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args.paths = paths;
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args.classes = net.outputs;
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args.n = imgs;
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args.m = N;
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args.d = &buffer;
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args.type = TAG_DATA;
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args.angle = net.angle;
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args.exposure = net.exposure;
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args.saturation = net.saturation;
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args.hue = net.hue;
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fprintf(stderr, "%d classes\n", net.outputs);
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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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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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if(get_current_batch(net)%100 == 0){
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char buff[256];
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sprintf(buff, "%s/%s.backup",backup_directory,base);
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save_weights(net, buff);
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}
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}
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char buff[256];
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sprintf(buff, "%s/%s.weights", backup_directory, base);
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save_weights(net, buff);
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pthread_join(load_thread, 0);
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free_data(buffer);
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free_network(net);
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free_ptrs((void**)paths, plist->size);
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free_list(plist);
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free(base);
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}
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void test_tag(char *cfgfile, char *weightfile, char *filename)
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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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int i = 0;
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char **names = get_labels("data/tags.txt");
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clock_t time;
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int indexes[10];
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char buff[256];
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char *input = buff;
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int size = net.w;
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while(1){
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if(filename){
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strncpy(input, filename, 256);
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}else{
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printf("Enter Image Path: ");
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fflush(stdout);
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input = fgets(input, 256, stdin);
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if(!input) return;
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strtok(input, "\n");
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}
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image im = load_image_color(input, 0, 0);
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image r = resize_min(im, size);
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resize_network(&net, r.w, r.h);
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printf("%d %d\n", r.w, r.h);
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float *X = r.data;
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time=clock();
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float *predictions = network_predict(net, X);
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top_predictions(net, 10, indexes);
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printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
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for(i = 0; i < 10; ++i){
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int index = indexes[i];
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printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
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}
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if(r.data != im.data) free_image(r);
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free_image(im);
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if (filename) break;
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}
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}
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void run_tag(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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int clear = find_arg(argc, argv, "-clear");
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char *cfg = argv[3];
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char *weights = (argc > 4) ? argv[4] : 0;
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char *filename = (argc > 5) ? argv[5] : 0;
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if(0==strcmp(argv[2], "train")) train_tag(cfg, weights, clear);
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else if(0==strcmp(argv[2], "test")) test_tag(cfg, weights, filename);
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}
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