mirror of https://github.com/pjreddie/darknet.git
121 lines
3.5 KiB
C
121 lines
3.5 KiB
C
#include "darknet.h"
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void train_super(char *cfgfile, char *weightfile, int clear)
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{
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char *train_images = "/data/imagenet/imagenet1k.train.list";
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char *backup_directory = "/home/pjreddie/backup/";
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srand(time(0));
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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 = load_network(cfgfile, weightfile, clear);
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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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int i = *net->seen/imgs;
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data train, buffer;
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list *plist = get_paths(train_images);
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//int N = plist->size;
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char **paths = (char **)list_to_array(plist);
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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.scale = 4;
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args.paths = paths;
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args.n = imgs;
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args.m = plist->size;
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args.d = &buffer;
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args.type = SUPER_DATA;
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pthread_t load_thread = load_data_in_thread(args);
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clock_t time;
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//while(i*imgs < N*120){
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while(get_current_batch(net) < net->max_batches){
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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_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 < 0) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
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if(i%1000==0){
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
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save_weights(net, buff);
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}
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if(i%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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free_data(train);
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}
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char buff[256];
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sprintf(buff, "%s/%s_final.weights", backup_directory, base);
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save_weights(net, buff);
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}
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void test_super(char *cfgfile, char *weightfile, char *filename)
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{
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network *net = load_network(cfgfile, weightfile, 0);
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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 buff[256];
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char *input = buff;
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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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resize_network(net, im.w, im.h);
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printf("%d %d\n", im.w, im.h);
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float *X = im.data;
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time=clock();
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network_predict(net, X);
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image out = get_network_image(net);
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printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
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save_image(out, "out");
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show_image(out, "out", 0);
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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_super(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 *filename = (argc > 5) ? argv[5] : 0;
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int clear = find_arg(argc, argv, "-clear");
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if(0==strcmp(argv[2], "train")) train_super(cfg, weights, clear);
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else if(0==strcmp(argv[2], "test")) test_super(cfg, weights, filename);
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/*
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else if(0==strcmp(argv[2], "valid")) validate_super(cfg, weights);
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*/
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}
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