mirror of https://github.com/pjreddie/darknet.git
1379 lines
44 KiB
C
1379 lines
44 KiB
C
#include <math.h>
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#include "darknet.h"
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/*
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void train_lsd3(char *fcfg, char *fweight, char *gcfg, char *gweight, char *acfg, char *aweight, int clear)
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{
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#ifdef GPU
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//char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
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char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list";
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//char *style_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
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char *style_images = "/home/pjreddie/zelda.txt";
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char *backup_directory = "/home/pjreddie/backup/";
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srand(time(0));
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network fnet = load_network(fcfg, fweight, clear);
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network gnet = load_network(gcfg, gweight, clear);
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network anet = load_network(acfg, aweight, clear);
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char *gbase = basecfg(gcfg);
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char *abase = basecfg(acfg);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay);
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int imgs = gnet->batch*gnet->subdivisions;
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int i = *gnet->seen/imgs;
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data train, tbuffer;
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data style, sbuffer;
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list *slist = get_paths(style_images);
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char **spaths = (char **)list_to_array(slist);
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list *tlist = get_paths(train_images);
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char **tpaths = (char **)list_to_array(tlist);
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load_args targs= get_base_args(gnet);
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targs.paths = tpaths;
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targs.n = imgs;
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targs.m = tlist->size;
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targs.d = &tbuffer;
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targs.type = CLASSIFICATION_DATA;
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targs.classes = 1;
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char *ls[1] = {"zelda"};
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targs.labels = ls;
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load_args sargs = get_base_args(gnet);
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sargs.paths = spaths;
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sargs.n = imgs;
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sargs.m = slist->size;
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sargs.d = &sbuffer;
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sargs.type = CLASSIFICATION_DATA;
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sargs.classes = 1;
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sargs.labels = ls;
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pthread_t tload_thread = load_data_in_thread(targs);
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pthread_t sload_thread = load_data_in_thread(sargs);
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clock_t time;
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float aloss_avg = -1;
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float floss_avg = -1;
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fnet->train=1;
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int x_size = fnet->inputs*fnet->batch;
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int y_size = fnet->truths*fnet->batch;
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float *X = calloc(x_size, sizeof(float));
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float *y = calloc(y_size, sizeof(float));
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int ax_size = anet->inputs*anet->batch;
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int ay_size = anet->truths*anet->batch;
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fill_gpu(ay_size, .9, anet->truth_gpu, 1);
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anet->delta_gpu = cuda_make_array(0, ax_size);
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anet->train = 1;
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int gx_size = gnet->inputs*gnet->batch;
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int gy_size = gnet->truths*gnet->batch;
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gstate.input = cuda_make_array(0, gx_size);
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gstate.truth = 0;
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gstate.delta = 0;
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gstate.train = 1;
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while (get_current_batch(gnet) < gnet->max_batches) {
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i += 1;
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time=clock();
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pthread_join(tload_thread, 0);
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pthread_join(sload_thread, 0);
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train = tbuffer;
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style = sbuffer;
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tload_thread = load_data_in_thread(targs);
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sload_thread = load_data_in_thread(sargs);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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data generated = copy_data(train);
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time=clock();
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int j, k;
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float floss = 0;
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for(j = 0; j < fnet->subdivisions; ++j){
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layer imlayer = gnet->layers[gnet->n - 1];
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get_next_batch(train, fnet->batch, j*fnet->batch, X, y);
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cuda_push_array(fstate.input, X, x_size);
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cuda_push_array(gstate.input, X, gx_size);
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*gnet->seen += gnet->batch;
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forward_network_gpu(fnet, fstate);
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float *feats = fnet->layers[fnet->n - 2].output_gpu;
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copy_gpu(y_size, feats, 1, fstate.truth, 1);
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forward_network_gpu(gnet, gstate);
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float *gen = gnet->layers[gnet->n-1].output_gpu;
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copy_gpu(x_size, gen, 1, fstate.input, 1);
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fill_gpu(x_size, 0, fstate.delta, 1);
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forward_network_gpu(fnet, fstate);
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backward_network_gpu(fnet, fstate);
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//HERE
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astate.input = gen;
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fill_gpu(ax_size, 0, astate.delta, 1);
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forward_network_gpu(anet, astate);
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backward_network_gpu(anet, astate);
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float *delta = imlayer.delta_gpu;
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fill_gpu(x_size, 0, delta, 1);
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scal_gpu(x_size, 100, astate.delta, 1);
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scal_gpu(x_size, .001, fstate.delta, 1);
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axpy_gpu(x_size, 1, fstate.delta, 1, delta, 1);
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axpy_gpu(x_size, 1, astate.delta, 1, delta, 1);
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//fill_gpu(x_size, 0, delta, 1);
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//cuda_push_array(delta, X, x_size);
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//axpy_gpu(x_size, -1, imlayer.output_gpu, 1, delta, 1);
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//printf("pix error: %f\n", cuda_mag_array(delta, x_size));
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printf("fea error: %f\n", cuda_mag_array(fstate.delta, x_size));
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printf("adv error: %f\n", cuda_mag_array(astate.delta, x_size));
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//axpy_gpu(x_size, 1, astate.delta, 1, delta, 1);
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backward_network_gpu(gnet, gstate);
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floss += get_network_cost(fnet) /(fnet->subdivisions*fnet->batch);
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cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
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for(k = 0; k < gnet->batch; ++k){
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int index = j*gnet->batch + k;
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copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
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generated.y.vals[index][0] = .1;
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style.y.vals[index][0] = .9;
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}
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}
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*/
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/*
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image sim = float_to_image(anet->w, anet->h, anet->c, style.X.vals[j]);
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show_image(sim, "style");
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cvWaitKey(0);
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*/
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/*
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harmless_update_network_gpu(anet);
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data merge = concat_data(style, generated);
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randomize_data(merge);
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float aloss = train_network(anet, merge);
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update_network_gpu(gnet);
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free_data(merge);
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free_data(train);
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free_data(generated);
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free_data(style);
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if (aloss_avg < 0) aloss_avg = aloss;
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if (floss_avg < 0) floss_avg = floss;
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aloss_avg = aloss_avg*.9 + aloss*.1;
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floss_avg = floss_avg*.9 + floss*.1;
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printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, floss, aloss, floss_avg, aloss_avg, get_current_rate(gnet), 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, gbase, i);
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save_weights(gnet, buff);
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sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
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save_weights(anet, 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, gbase);
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save_weights(gnet, buff);
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sprintf(buff, "%s/%s.backup", backup_directory, abase);
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save_weights(anet, buff);
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}
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}
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#endif
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}
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*/
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/*
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void train_pix2pix(char *cfg, char *weight, char *acfg, char *aweight, int clear)
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{
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#ifdef GPU
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//char *train_images = "/home/pjreddie/data/coco/train1.txt";
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//char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
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char *train_images = "/home/pjreddie/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(cfg);
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char *abase = basecfg(acfg);
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printf("%s\n", base);
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network net = load_network(cfg, weight, clear);
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network anet = load_network(acfg, aweight, clear);
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int i, j, k;
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layer imlayer = {0};
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for (i = 0; i < net->n; ++i) {
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if (net->layers[i].out_c == 3) {
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imlayer = net->layers[i];
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break;
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}
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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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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.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.min = net->min_crop;
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args.max = net->max_crop;
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args.angle = net->angle;
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args.aspect = net->aspect;
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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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args.size = net->w;
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args.type = CLASSIFICATION_DATA;
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args.classes = 1;
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char *ls[1] = {"coco"};
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args.labels = ls;
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pthread_t load_thread = load_data_in_thread(args);
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clock_t time;
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network_state gstate = {0};
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gstate.index = 0;
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gstate.net = net;
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int x_size = get_network_input_size(net)*net->batch;
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int y_size = x_size;
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gstate.input = cuda_make_array(0, x_size);
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gstate.truth = cuda_make_array(0, y_size);
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gstate.delta = 0;
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gstate.train = 1;
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float *pixs = calloc(x_size, sizeof(float));
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float *graypixs = calloc(x_size, sizeof(float));
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float *y = calloc(y_size, sizeof(float));
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network_state astate = {0};
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astate.index = 0;
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astate.net = anet;
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int ay_size = get_network_output_size(anet)*anet->batch;
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astate.input = 0;
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astate.truth = 0;
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astate.delta = 0;
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astate.train = 1;
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float *imerror = cuda_make_array(0, imlayer.outputs);
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float *ones_gpu = cuda_make_array(0, ay_size);
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fill_gpu(ay_size, .9, ones_gpu, 1);
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float aloss_avg = -1;
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float gloss_avg = -1;
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//data generated = copy_data(train);
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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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data gray = copy_data(train);
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for(j = 0; j < imgs; ++j){
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image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]);
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grayscale_image_3c(gim);
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train.y.vals[j][0] = .9;
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image yim = float_to_image(net->w, net->h, net->c, train.X.vals[j]);
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//rgb_to_yuv(yim);
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}
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time=clock();
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float gloss = 0;
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for(j = 0; j < net->subdivisions; ++j){
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get_next_batch(train, net->batch, j*net->batch, pixs, y);
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get_next_batch(gray, net->batch, j*net->batch, graypixs, y);
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cuda_push_array(gstate.input, graypixs, x_size);
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cuda_push_array(gstate.truth, pixs, y_size);
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*/
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/*
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image origi = float_to_image(net->w, net->h, 3, pixs);
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image grayi = float_to_image(net->w, net->h, 3, graypixs);
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show_image(grayi, "gray");
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show_image(origi, "orig");
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cvWaitKey(0);
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*/
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/*
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*net->seen += net->batch;
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forward_network_gpu(net, gstate);
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fill_gpu(imlayer.outputs, 0, imerror, 1);
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astate.input = imlayer.output_gpu;
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astate.delta = imerror;
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astate.truth = ones_gpu;
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forward_network_gpu(anet, astate);
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backward_network_gpu(anet, astate);
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scal_gpu(imlayer.outputs, .1, net->layers[net->n-1].delta_gpu, 1);
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backward_network_gpu(net, gstate);
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scal_gpu(imlayer.outputs, 1000, imerror, 1);
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printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
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printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs));
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axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
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gloss += get_network_cost(net) /(net->subdivisions*net->batch);
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cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
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for(k = 0; k < net->batch; ++k){
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int index = j*net->batch + k;
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copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1);
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gray.y.vals[index][0] = .1;
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}
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}
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harmless_update_network_gpu(anet);
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data merge = concat_data(train, gray);
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randomize_data(merge);
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float aloss = train_network(anet, merge);
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update_network_gpu(net);
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update_network_gpu(anet);
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free_data(merge);
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free_data(train);
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free_data(gray);
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if (aloss_avg < 0) aloss_avg = aloss;
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aloss_avg = aloss_avg*.9 + aloss*.1;
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gloss_avg = gloss_avg*.9 + gloss*.1;
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printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, 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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sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
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save_weights(anet, 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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sprintf(buff, "%s/%s.backup", backup_directory, abase);
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save_weights(anet, buff);
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}
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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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#endif
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}
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*/
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void slerp(float *start, float *end, float s, int n, float *out)
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{
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float omega = acos(dot_cpu(n, start, 1, end, 1));
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float so = sin(omega);
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fill_cpu(n, 0, out, 1);
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axpy_cpu(n, sin((1-s)*omega)/so, start, 1, out, 1);
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axpy_cpu(n, sin(s*omega)/so, end, 1, out, 1);
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float mag = mag_array(out, n);
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scale_array(out, n, 1./mag);
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}
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image random_unit_vector_image(int w, int h, int c)
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{
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image im = make_image(w, h, c);
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int i;
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for(i = 0; i < im.w*im.h*im.c; ++i){
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im.data[i] = rand_normal();
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}
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float mag = mag_array(im.data, im.w*im.h*im.c);
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scale_array(im.data, im.w*im.h*im.c, 1./mag);
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return im;
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}
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void inter_dcgan(char *cfgfile, char *weightfile)
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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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int i, imlayer = 0;
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for (i = 0; i < net->n; ++i) {
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if (net->layers[i].out_c == 3) {
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imlayer = i;
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printf("%d\n", i);
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break;
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}
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}
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image start = random_unit_vector_image(net->w, net->h, net->c);
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image end = random_unit_vector_image(net->w, net->h, net->c);
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image im = make_image(net->w, net->h, net->c);
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image orig = copy_image(start);
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int c = 0;
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int count = 0;
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int max_count = 15;
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while(1){
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++c;
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if(count == max_count){
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count = 0;
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free_image(start);
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start = end;
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end = random_unit_vector_image(net->w, net->h, net->c);
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if(c > 300){
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end = orig;
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}
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if(c>300 + max_count) return;
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}
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++count;
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slerp(start.data, end.data, (float)count / max_count, im.w*im.h*im.c, im.data);
|
|
|
|
float *X = im.data;
|
|
time=clock();
|
|
network_predict(net, X);
|
|
image out = get_network_image_layer(net, imlayer);
|
|
//yuv_to_rgb(out);
|
|
normalize_image(out);
|
|
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
|
//char buff[256];
|
|
sprintf(buff, "out%05d", c);
|
|
save_image(out, "out");
|
|
save_image(out, buff);
|
|
show_image(out, "out", 0);
|
|
}
|
|
}
|
|
|
|
void test_dcgan(char *cfgfile, char *weightfile)
|
|
{
|
|
network *net = load_network(cfgfile, weightfile, 0);
|
|
set_batch_network(net, 1);
|
|
srand(2222222);
|
|
|
|
clock_t time;
|
|
char buff[256];
|
|
char *input = buff;
|
|
int imlayer = 0;
|
|
|
|
imlayer = net->n-1;
|
|
|
|
while(1){
|
|
image im = make_image(net->w, net->h, net->c);
|
|
int i;
|
|
for(i = 0; i < im.w*im.h*im.c; ++i){
|
|
im.data[i] = rand_normal();
|
|
}
|
|
//float mag = mag_array(im.data, im.w*im.h*im.c);
|
|
//scale_array(im.data, im.w*im.h*im.c, 1./mag);
|
|
|
|
float *X = im.data;
|
|
time=clock();
|
|
network_predict(net, X);
|
|
image out = get_network_image_layer(net, imlayer);
|
|
//yuv_to_rgb(out);
|
|
normalize_image(out);
|
|
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
|
save_image(out, "out");
|
|
show_image(out, "out", 0);
|
|
|
|
free_image(im);
|
|
}
|
|
}
|
|
|
|
void set_network_alpha_beta(network *net, float alpha, float beta)
|
|
{
|
|
int i;
|
|
for(i = 0; i < net->n; ++i){
|
|
if(net->layers[i].type == SHORTCUT){
|
|
net->layers[i].alpha = alpha;
|
|
net->layers[i].beta = beta;
|
|
}
|
|
}
|
|
}
|
|
|
|
void train_prog(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images, int maxbatch)
|
|
{
|
|
#ifdef GPU
|
|
char *backup_directory = "/home/pjreddie/backup/";
|
|
srand(time(0));
|
|
char *base = basecfg(cfg);
|
|
char *abase = basecfg(acfg);
|
|
printf("%s\n", base);
|
|
network *gnet = load_network(cfg, weight, clear);
|
|
network *anet = load_network(acfg, aweight, clear);
|
|
|
|
int i, j, k;
|
|
layer imlayer = gnet->layers[gnet->n-1];
|
|
|
|
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay);
|
|
int imgs = gnet->batch*gnet->subdivisions;
|
|
i = *gnet->seen/imgs;
|
|
data train, buffer;
|
|
|
|
|
|
list *plist = get_paths(train_images);
|
|
char **paths = (char **)list_to_array(plist);
|
|
|
|
load_args args= get_base_args(anet);
|
|
args.paths = paths;
|
|
args.n = imgs;
|
|
args.m = plist->size;
|
|
args.d = &buffer;
|
|
args.type = CLASSIFICATION_DATA;
|
|
args.threads=16;
|
|
args.classes = 1;
|
|
char *ls[2] = {"imagenet", "zzzzzzzz"};
|
|
args.labels = ls;
|
|
|
|
pthread_t load_thread = load_data_in_thread(args);
|
|
clock_t time;
|
|
|
|
gnet->train = 1;
|
|
anet->train = 1;
|
|
|
|
int x_size = gnet->inputs*gnet->batch;
|
|
int y_size = gnet->truths*gnet->batch;
|
|
float *imerror = cuda_make_array(0, y_size);
|
|
|
|
float aloss_avg = -1;
|
|
|
|
if (maxbatch == 0) maxbatch = gnet->max_batches;
|
|
while (get_current_batch(gnet) < maxbatch) {
|
|
{
|
|
int cb = get_current_batch(gnet);
|
|
float alpha = (float) cb / (maxbatch/2);
|
|
if(alpha > 1) alpha = 1;
|
|
float beta = 1 - alpha;
|
|
printf("%f %f\n", alpha, beta);
|
|
set_network_alpha_beta(gnet, alpha, beta);
|
|
set_network_alpha_beta(anet, beta, alpha);
|
|
}
|
|
|
|
i += 1;
|
|
time=clock();
|
|
pthread_join(load_thread, 0);
|
|
train = buffer;
|
|
|
|
load_thread = load_data_in_thread(args);
|
|
|
|
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
|
|
|
data gen = copy_data(train);
|
|
for (j = 0; j < imgs; ++j) {
|
|
train.y.vals[j][0] = 1;
|
|
gen.y.vals[j][0] = 0;
|
|
}
|
|
time=clock();
|
|
|
|
for (j = 0; j < gnet->subdivisions; ++j) {
|
|
get_next_batch(train, gnet->batch, j*gnet->batch, gnet->truth, 0);
|
|
int z;
|
|
for(z = 0; z < x_size; ++z){
|
|
gnet->input[z] = rand_normal();
|
|
}
|
|
/*
|
|
for(z = 0; z < gnet->batch; ++z){
|
|
float mag = mag_array(gnet->input + z*gnet->inputs, gnet->inputs);
|
|
scale_array(gnet->input + z*gnet->inputs, gnet->inputs, 1./mag);
|
|
}
|
|
*/
|
|
*gnet->seen += gnet->batch;
|
|
forward_network(gnet);
|
|
|
|
fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
|
|
fill_cpu(anet->truths*anet->batch, 1, anet->truth, 1);
|
|
copy_cpu(anet->inputs*anet->batch, imlayer.output, 1, anet->input, 1);
|
|
anet->delta_gpu = imerror;
|
|
forward_network(anet);
|
|
backward_network(anet);
|
|
|
|
//float genaloss = *anet->cost / anet->batch;
|
|
|
|
scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
|
|
scal_gpu(imlayer.outputs*imlayer.batch, 0, gnet->layers[gnet->n-1].delta_gpu, 1);
|
|
|
|
axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1);
|
|
|
|
backward_network(gnet);
|
|
|
|
for(k = 0; k < gnet->batch; ++k){
|
|
int index = j*gnet->batch + k;
|
|
copy_cpu(gnet->outputs, gnet->output + k*gnet->outputs, 1, gen.X.vals[index], 1);
|
|
}
|
|
}
|
|
harmless_update_network_gpu(anet);
|
|
|
|
data merge = concat_data(train, gen);
|
|
float aloss = train_network(anet, merge);
|
|
|
|
#ifdef OPENCV
|
|
if(display){
|
|
image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
|
|
image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
|
|
show_image(im, "gen", 1);
|
|
show_image(im2, "train", 1);
|
|
save_image(im, "gen");
|
|
save_image(im2, "train");
|
|
}
|
|
#endif
|
|
|
|
update_network_gpu(gnet);
|
|
|
|
free_data(merge);
|
|
free_data(train);
|
|
free_data(gen);
|
|
if (aloss_avg < 0) aloss_avg = aloss;
|
|
aloss_avg = aloss_avg*.9 + aloss*.1;
|
|
|
|
printf("%d: adv: %f | adv_avg: %f, %f rate, %lf seconds, %d images\n", i, aloss, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs);
|
|
if(i%10000==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
|
save_weights(gnet, buff);
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
|
|
save_weights(anet, buff);
|
|
}
|
|
if(i%1000==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
|
save_weights(gnet, buff);
|
|
sprintf(buff, "%s/%s.backup", backup_directory, abase);
|
|
save_weights(anet, buff);
|
|
}
|
|
}
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
|
save_weights(gnet, buff);
|
|
#endif
|
|
}
|
|
|
|
void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images, int maxbatch)
|
|
{
|
|
#ifdef GPU
|
|
char *backup_directory = "/home/pjreddie/backup/";
|
|
srand(time(0));
|
|
char *base = basecfg(cfg);
|
|
char *abase = basecfg(acfg);
|
|
printf("%s\n", base);
|
|
network *gnet = load_network(cfg, weight, clear);
|
|
network *anet = load_network(acfg, aweight, clear);
|
|
//float orig_rate = anet->learning_rate;
|
|
|
|
int i, j, k;
|
|
layer imlayer = {0};
|
|
for (i = 0; i < gnet->n; ++i) {
|
|
if (gnet->layers[i].out_c == 3) {
|
|
imlayer = gnet->layers[i];
|
|
break;
|
|
}
|
|
}
|
|
|
|
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", gnet->learning_rate, gnet->momentum, gnet->decay);
|
|
int imgs = gnet->batch*gnet->subdivisions;
|
|
i = *gnet->seen/imgs;
|
|
data train, buffer;
|
|
|
|
|
|
list *plist = get_paths(train_images);
|
|
//int N = plist->size;
|
|
char **paths = (char **)list_to_array(plist);
|
|
|
|
load_args args= get_base_args(anet);
|
|
args.paths = paths;
|
|
args.n = imgs;
|
|
args.m = plist->size;
|
|
args.d = &buffer;
|
|
args.type = CLASSIFICATION_DATA;
|
|
args.threads=16;
|
|
args.classes = 1;
|
|
char *ls[2] = {"imagenet", "zzzzzzzz"};
|
|
args.labels = ls;
|
|
|
|
pthread_t load_thread = load_data_in_thread(args);
|
|
clock_t time;
|
|
|
|
gnet->train = 1;
|
|
anet->train = 1;
|
|
|
|
int x_size = gnet->inputs*gnet->batch;
|
|
int y_size = gnet->truths*gnet->batch;
|
|
float *imerror = cuda_make_array(0, y_size);
|
|
|
|
//int ay_size = anet->truths*anet->batch;
|
|
|
|
float aloss_avg = -1;
|
|
|
|
//data generated = copy_data(train);
|
|
|
|
if (maxbatch == 0) maxbatch = gnet->max_batches;
|
|
while (get_current_batch(gnet) < maxbatch) {
|
|
i += 1;
|
|
time=clock();
|
|
pthread_join(load_thread, 0);
|
|
train = buffer;
|
|
|
|
//translate_data_rows(train, -.5);
|
|
//scale_data_rows(train, 2);
|
|
|
|
load_thread = load_data_in_thread(args);
|
|
|
|
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
|
|
|
data gen = copy_data(train);
|
|
for (j = 0; j < imgs; ++j) {
|
|
train.y.vals[j][0] = 1;
|
|
gen.y.vals[j][0] = 0;
|
|
}
|
|
time=clock();
|
|
|
|
for(j = 0; j < gnet->subdivisions; ++j){
|
|
get_next_batch(train, gnet->batch, j*gnet->batch, gnet->truth, 0);
|
|
int z;
|
|
for(z = 0; z < x_size; ++z){
|
|
gnet->input[z] = rand_normal();
|
|
}
|
|
for(z = 0; z < gnet->batch; ++z){
|
|
float mag = mag_array(gnet->input + z*gnet->inputs, gnet->inputs);
|
|
scale_array(gnet->input + z*gnet->inputs, gnet->inputs, 1./mag);
|
|
}
|
|
/*
|
|
for(z = 0; z < 100; ++z){
|
|
printf("%f, ", gnet->input[z]);
|
|
}
|
|
printf("\n");
|
|
printf("input: %f %f\n", mean_array(gnet->input, x_size), variance_array(gnet->input, x_size));
|
|
*/
|
|
|
|
//cuda_push_array(gnet->input_gpu, gnet->input, x_size);
|
|
//cuda_push_array(gnet->truth_gpu, gnet->truth, y_size);
|
|
*gnet->seen += gnet->batch;
|
|
forward_network(gnet);
|
|
|
|
fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
|
|
fill_cpu(anet->truths*anet->batch, 1, anet->truth, 1);
|
|
copy_cpu(anet->inputs*anet->batch, imlayer.output, 1, anet->input, 1);
|
|
anet->delta_gpu = imerror;
|
|
forward_network(anet);
|
|
backward_network(anet);
|
|
|
|
//float genaloss = *anet->cost / anet->batch;
|
|
//printf("%f\n", genaloss);
|
|
|
|
scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
|
|
scal_gpu(imlayer.outputs*imlayer.batch, 0, gnet->layers[gnet->n-1].delta_gpu, 1);
|
|
|
|
//printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
|
|
//printf("features %f\n", cuda_mag_array(gnet->layers[gnet->n-1].delta_gpu, imlayer.outputs*imlayer.batch));
|
|
|
|
axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1);
|
|
|
|
backward_network(gnet);
|
|
|
|
/*
|
|
for(k = 0; k < gnet->n; ++k){
|
|
layer l = gnet->layers[k];
|
|
cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
|
|
printf("%d: %f %f\n", k, mean_array(l.output, l.outputs*l.batch), variance_array(l.output, l.outputs*l.batch));
|
|
}
|
|
*/
|
|
|
|
for(k = 0; k < gnet->batch; ++k){
|
|
int index = j*gnet->batch + k;
|
|
copy_cpu(gnet->outputs, gnet->output + k*gnet->outputs, 1, gen.X.vals[index], 1);
|
|
}
|
|
}
|
|
harmless_update_network_gpu(anet);
|
|
|
|
data merge = concat_data(train, gen);
|
|
//randomize_data(merge);
|
|
float aloss = train_network(anet, merge);
|
|
|
|
//translate_image(im, 1);
|
|
//scale_image(im, .5);
|
|
//translate_image(im2, 1);
|
|
//scale_image(im2, .5);
|
|
#ifdef OPENCV
|
|
if(display){
|
|
image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
|
|
image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
|
|
show_image(im, "gen", 1);
|
|
show_image(im2, "train", 1);
|
|
save_image(im, "gen");
|
|
save_image(im2, "train");
|
|
}
|
|
#endif
|
|
|
|
/*
|
|
if(aloss < .1){
|
|
anet->learning_rate = 0;
|
|
} else if (aloss > .3){
|
|
anet->learning_rate = orig_rate;
|
|
}
|
|
*/
|
|
|
|
update_network_gpu(gnet);
|
|
|
|
free_data(merge);
|
|
free_data(train);
|
|
free_data(gen);
|
|
if (aloss_avg < 0) aloss_avg = aloss;
|
|
aloss_avg = aloss_avg*.9 + aloss*.1;
|
|
|
|
printf("%d: adv: %f | adv_avg: %f, %f rate, %lf seconds, %d images\n", i, aloss, aloss_avg, get_current_rate(gnet), sec(clock()-time), i*imgs);
|
|
if(i%10000==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
|
save_weights(gnet, buff);
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
|
|
save_weights(anet, buff);
|
|
}
|
|
if(i%1000==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
|
save_weights(gnet, buff);
|
|
sprintf(buff, "%s/%s.backup", backup_directory, abase);
|
|
save_weights(anet, buff);
|
|
}
|
|
}
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
|
save_weights(gnet, buff);
|
|
#endif
|
|
}
|
|
|
|
void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display)
|
|
{
|
|
#ifdef GPU
|
|
//char *train_images = "/home/pjreddie/data/coco/train1.txt";
|
|
//char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
|
|
char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list";
|
|
char *backup_directory = "/home/pjreddie/backup/";
|
|
srand(time(0));
|
|
char *base = basecfg(cfg);
|
|
char *abase = basecfg(acfg);
|
|
printf("%s\n", base);
|
|
network *net = load_network(cfg, weight, clear);
|
|
network *anet = load_network(acfg, aweight, clear);
|
|
|
|
int i, j, k;
|
|
layer imlayer = {0};
|
|
for (i = 0; i < net->n; ++i) {
|
|
if (net->layers[i].out_c == 3) {
|
|
imlayer = net->layers[i];
|
|
break;
|
|
}
|
|
}
|
|
|
|
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
|
|
int imgs = net->batch*net->subdivisions;
|
|
i = *net->seen/imgs;
|
|
data train, buffer;
|
|
|
|
|
|
list *plist = get_paths(train_images);
|
|
//int N = plist->size;
|
|
char **paths = (char **)list_to_array(plist);
|
|
|
|
load_args args= get_base_args(net);
|
|
args.paths = paths;
|
|
args.n = imgs;
|
|
args.m = plist->size;
|
|
args.d = &buffer;
|
|
|
|
args.type = CLASSIFICATION_DATA;
|
|
args.classes = 1;
|
|
char *ls[2] = {"imagenet"};
|
|
args.labels = ls;
|
|
|
|
pthread_t load_thread = load_data_in_thread(args);
|
|
clock_t time;
|
|
|
|
int x_size = net->inputs*net->batch;
|
|
//int y_size = x_size;
|
|
net->delta = 0;
|
|
net->train = 1;
|
|
float *pixs = calloc(x_size, sizeof(float));
|
|
float *graypixs = calloc(x_size, sizeof(float));
|
|
//float *y = calloc(y_size, sizeof(float));
|
|
|
|
//int ay_size = anet->outputs*anet->batch;
|
|
anet->delta = 0;
|
|
anet->train = 1;
|
|
|
|
float *imerror = cuda_make_array(0, imlayer.outputs*imlayer.batch);
|
|
|
|
float aloss_avg = -1;
|
|
float gloss_avg = -1;
|
|
|
|
//data generated = copy_data(train);
|
|
|
|
while (get_current_batch(net) < net->max_batches) {
|
|
i += 1;
|
|
time=clock();
|
|
pthread_join(load_thread, 0);
|
|
train = buffer;
|
|
load_thread = load_data_in_thread(args);
|
|
|
|
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
|
|
|
data gray = copy_data(train);
|
|
for(j = 0; j < imgs; ++j){
|
|
image gim = float_to_image(net->w, net->h, net->c, gray.X.vals[j]);
|
|
grayscale_image_3c(gim);
|
|
train.y.vals[j][0] = .95;
|
|
gray.y.vals[j][0] = .05;
|
|
}
|
|
time=clock();
|
|
float gloss = 0;
|
|
|
|
for(j = 0; j < net->subdivisions; ++j){
|
|
get_next_batch(train, net->batch, j*net->batch, pixs, 0);
|
|
get_next_batch(gray, net->batch, j*net->batch, graypixs, 0);
|
|
cuda_push_array(net->input_gpu, graypixs, net->inputs*net->batch);
|
|
cuda_push_array(net->truth_gpu, pixs, net->truths*net->batch);
|
|
/*
|
|
image origi = float_to_image(net->w, net->h, 3, pixs);
|
|
image grayi = float_to_image(net->w, net->h, 3, graypixs);
|
|
show_image(grayi, "gray");
|
|
show_image(origi, "orig");
|
|
cvWaitKey(0);
|
|
*/
|
|
*net->seen += net->batch;
|
|
forward_network_gpu(net);
|
|
|
|
fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
|
|
copy_gpu(anet->inputs*anet->batch, imlayer.output_gpu, 1, anet->input_gpu, 1);
|
|
fill_gpu(anet->inputs*anet->batch, .95, anet->truth_gpu, 1);
|
|
anet->delta_gpu = imerror;
|
|
forward_network_gpu(anet);
|
|
backward_network_gpu(anet);
|
|
|
|
scal_gpu(imlayer.outputs*imlayer.batch, 1./100., net->layers[net->n-1].delta_gpu, 1);
|
|
|
|
scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
|
|
|
|
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
|
|
printf("features %f\n", cuda_mag_array(net->layers[net->n-1].delta_gpu, imlayer.outputs*imlayer.batch));
|
|
|
|
axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, net->layers[net->n-1].delta_gpu, 1);
|
|
|
|
backward_network_gpu(net);
|
|
|
|
|
|
gloss += *net->cost /(net->subdivisions*net->batch);
|
|
|
|
for(k = 0; k < net->batch; ++k){
|
|
int index = j*net->batch + k;
|
|
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, gray.X.vals[index], 1);
|
|
}
|
|
}
|
|
harmless_update_network_gpu(anet);
|
|
|
|
data merge = concat_data(train, gray);
|
|
//randomize_data(merge);
|
|
float aloss = train_network(anet, merge);
|
|
|
|
update_network_gpu(net);
|
|
|
|
#ifdef OPENCV
|
|
if(display){
|
|
image im = float_to_image(anet->w, anet->h, anet->c, gray.X.vals[0]);
|
|
image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
|
|
show_image(im, "gen", 1);
|
|
show_image(im2, "train", 1);
|
|
}
|
|
#endif
|
|
free_data(merge);
|
|
free_data(train);
|
|
free_data(gray);
|
|
if (aloss_avg < 0) aloss_avg = aloss;
|
|
aloss_avg = aloss_avg*.9 + aloss*.1;
|
|
gloss_avg = gloss_avg*.9 + gloss*.1;
|
|
|
|
printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs);
|
|
if(i%1000==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
|
save_weights(net, buff);
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
|
|
save_weights(anet, buff);
|
|
}
|
|
if(i%100==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
|
save_weights(net, buff);
|
|
sprintf(buff, "%s/%s.backup", backup_directory, abase);
|
|
save_weights(anet, buff);
|
|
}
|
|
}
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
|
save_weights(net, buff);
|
|
#endif
|
|
}
|
|
|
|
/*
|
|
void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfile, int clear)
|
|
{
|
|
#ifdef GPU
|
|
char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
|
|
char *backup_directory = "/home/pjreddie/backup/";
|
|
srand(time(0));
|
|
char *base = basecfg(cfgfile);
|
|
printf("%s\n", base);
|
|
network net = parse_network_cfg(cfgfile);
|
|
if(weightfile){
|
|
load_weights(&net, weightfile);
|
|
}
|
|
if(clear) *net->seen = 0;
|
|
|
|
char *abase = basecfg(acfgfile);
|
|
network anet = parse_network_cfg(acfgfile);
|
|
if(aweightfile){
|
|
load_weights(&anet, aweightfile);
|
|
}
|
|
if(clear) *anet->seen = 0;
|
|
|
|
int i, j, k;
|
|
layer imlayer = {0};
|
|
for (i = 0; i < net->n; ++i) {
|
|
if (net->layers[i].out_c == 3) {
|
|
imlayer = net->layers[i];
|
|
break;
|
|
}
|
|
}
|
|
|
|
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
|
|
int imgs = net->batch*net->subdivisions;
|
|
i = *net->seen/imgs;
|
|
data train, buffer;
|
|
|
|
|
|
list *plist = get_paths(train_images);
|
|
//int N = plist->size;
|
|
char **paths = (char **)list_to_array(plist);
|
|
|
|
load_args args = {0};
|
|
args.w = net->w;
|
|
args.h = net->h;
|
|
args.paths = paths;
|
|
args.n = imgs;
|
|
args.m = plist->size;
|
|
args.d = &buffer;
|
|
|
|
args.min = net->min_crop;
|
|
args.max = net->max_crop;
|
|
args.angle = net->angle;
|
|
args.aspect = net->aspect;
|
|
args.exposure = net->exposure;
|
|
args.saturation = net->saturation;
|
|
args.hue = net->hue;
|
|
args.size = net->w;
|
|
args.type = CLASSIFICATION_DATA;
|
|
args.classes = 1;
|
|
char *ls[1] = {"coco"};
|
|
args.labels = ls;
|
|
|
|
pthread_t load_thread = load_data_in_thread(args);
|
|
clock_t time;
|
|
|
|
network_state gstate = {0};
|
|
gstate.index = 0;
|
|
gstate.net = net;
|
|
int x_size = get_network_input_size(net)*net->batch;
|
|
int y_size = 1*net->batch;
|
|
gstate.input = cuda_make_array(0, x_size);
|
|
gstate.truth = 0;
|
|
gstate.delta = 0;
|
|
gstate.train = 1;
|
|
float *X = calloc(x_size, sizeof(float));
|
|
float *y = calloc(y_size, sizeof(float));
|
|
|
|
network_state astate = {0};
|
|
astate.index = 0;
|
|
astate.net = anet;
|
|
int ay_size = get_network_output_size(anet)*anet->batch;
|
|
astate.input = 0;
|
|
astate.truth = 0;
|
|
astate.delta = 0;
|
|
astate.train = 1;
|
|
|
|
float *imerror = cuda_make_array(0, imlayer.outputs);
|
|
float *ones_gpu = cuda_make_array(0, ay_size);
|
|
fill_gpu(ay_size, 1, ones_gpu, 1);
|
|
|
|
float aloss_avg = -1;
|
|
float gloss_avg = -1;
|
|
|
|
//data generated = copy_data(train);
|
|
|
|
while (get_current_batch(net) < net->max_batches) {
|
|
i += 1;
|
|
time=clock();
|
|
pthread_join(load_thread, 0);
|
|
train = buffer;
|
|
load_thread = load_data_in_thread(args);
|
|
|
|
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
|
|
|
data generated = copy_data(train);
|
|
time=clock();
|
|
float gloss = 0;
|
|
|
|
for(j = 0; j < net->subdivisions; ++j){
|
|
get_next_batch(train, net->batch, j*net->batch, X, y);
|
|
cuda_push_array(gstate.input, X, x_size);
|
|
*net->seen += net->batch;
|
|
forward_network_gpu(net, gstate);
|
|
|
|
fill_gpu(imlayer.outputs, 0, imerror, 1);
|
|
astate.input = imlayer.output_gpu;
|
|
astate.delta = imerror;
|
|
astate.truth = ones_gpu;
|
|
forward_network_gpu(anet, astate);
|
|
backward_network_gpu(anet, astate);
|
|
|
|
scal_gpu(imlayer.outputs, 1, imerror, 1);
|
|
axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
|
|
|
|
backward_network_gpu(net, gstate);
|
|
|
|
printf("features %f\n", cuda_mag_array(imlayer.delta_gpu, imlayer.outputs));
|
|
printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
|
|
|
|
gloss += get_network_cost(net) /(net->subdivisions*net->batch);
|
|
|
|
cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
|
|
for(k = 0; k < net->batch; ++k){
|
|
int index = j*net->batch + k;
|
|
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
|
|
generated.y.vals[index][0] = 0;
|
|
}
|
|
}
|
|
harmless_update_network_gpu(anet);
|
|
|
|
data merge = concat_data(train, generated);
|
|
randomize_data(merge);
|
|
float aloss = train_network(anet, merge);
|
|
|
|
update_network_gpu(net);
|
|
update_network_gpu(anet);
|
|
free_data(merge);
|
|
free_data(train);
|
|
free_data(generated);
|
|
if (aloss_avg < 0) aloss_avg = aloss;
|
|
aloss_avg = aloss_avg*.9 + aloss*.1;
|
|
gloss_avg = gloss_avg*.9 + gloss*.1;
|
|
|
|
printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs);
|
|
if(i%1000==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
|
save_weights(net, buff);
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
|
|
save_weights(anet, buff);
|
|
}
|
|
if(i%100==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
|
save_weights(net, buff);
|
|
sprintf(buff, "%s/%s.backup", backup_directory, abase);
|
|
save_weights(anet, buff);
|
|
}
|
|
}
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
|
save_weights(net, buff);
|
|
#endif
|
|
}
|
|
*/
|
|
|
|
/*
|
|
void train_lsd(char *cfgfile, char *weightfile, int clear)
|
|
{
|
|
char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
|
|
char *backup_directory = "/home/pjreddie/backup/";
|
|
srand(time(0));
|
|
char *base = basecfg(cfgfile);
|
|
printf("%s\n", base);
|
|
float avg_loss = -1;
|
|
network net = parse_network_cfg(cfgfile);
|
|
if(weightfile){
|
|
load_weights(&net, weightfile);
|
|
}
|
|
if(clear) *net->seen = 0;
|
|
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
|
|
int imgs = net->batch*net->subdivisions;
|
|
int i = *net->seen/imgs;
|
|
data train, buffer;
|
|
|
|
|
|
list *plist = get_paths(train_images);
|
|
//int N = plist->size;
|
|
char **paths = (char **)list_to_array(plist);
|
|
|
|
load_args args = {0};
|
|
args.w = net->w;
|
|
args.h = net->h;
|
|
args.paths = paths;
|
|
args.n = imgs;
|
|
args.m = plist->size;
|
|
args.d = &buffer;
|
|
|
|
args.min = net->min_crop;
|
|
args.max = net->max_crop;
|
|
args.angle = net->angle;
|
|
args.aspect = net->aspect;
|
|
args.exposure = net->exposure;
|
|
args.saturation = net->saturation;
|
|
args.hue = net->hue;
|
|
args.size = net->w;
|
|
args.type = CLASSIFICATION_DATA;
|
|
args.classes = 1;
|
|
char *ls[1] = {"coco"};
|
|
args.labels = ls;
|
|
|
|
pthread_t load_thread = load_data_in_thread(args);
|
|
clock_t time;
|
|
//while(i*imgs < N*120){
|
|
while(get_current_batch(net) < net->max_batches){
|
|
i += 1;
|
|
time=clock();
|
|
pthread_join(load_thread, 0);
|
|
train = buffer;
|
|
load_thread = load_data_in_thread(args);
|
|
|
|
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
|
|
|
time=clock();
|
|
float loss = train_network(net, train);
|
|
if (avg_loss < 0) avg_loss = loss;
|
|
avg_loss = avg_loss*.9 + loss*.1;
|
|
|
|
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);
|
|
if(i%1000==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
|
save_weights(net, buff);
|
|
}
|
|
if(i%100==0){
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
|
save_weights(net, buff);
|
|
}
|
|
free_data(train);
|
|
}
|
|
char buff[256];
|
|
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
|
save_weights(net, buff);
|
|
}
|
|
*/
|
|
|
|
void test_lsd(char *cfg, char *weights, char *filename, int gray)
|
|
{
|
|
network *net = load_network(cfg, weights, 0);
|
|
set_batch_network(net, 1);
|
|
srand(2222222);
|
|
|
|
clock_t time;
|
|
char buff[256];
|
|
char *input = buff;
|
|
int i, imlayer = 0;
|
|
|
|
for (i = 0; i < net->n; ++i) {
|
|
if (net->layers[i].out_c == 3) {
|
|
imlayer = i;
|
|
printf("%d\n", i);
|
|
break;
|
|
}
|
|
}
|
|
|
|
while(1){
|
|
if(filename){
|
|
strncpy(input, filename, 256);
|
|
}else{
|
|
printf("Enter Image Path: ");
|
|
fflush(stdout);
|
|
input = fgets(input, 256, stdin);
|
|
if(!input) return;
|
|
strtok(input, "\n");
|
|
}
|
|
image im = load_image_color(input, 0, 0);
|
|
image resized = resize_min(im, net->w);
|
|
image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
|
|
if(gray) grayscale_image_3c(crop);
|
|
|
|
float *X = crop.data;
|
|
time=clock();
|
|
network_predict(net, X);
|
|
image out = get_network_image_layer(net, imlayer);
|
|
//yuv_to_rgb(out);
|
|
constrain_image(out);
|
|
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
|
save_image(out, "out");
|
|
show_image(out, "out", 1);
|
|
show_image(crop, "crop", 0);
|
|
|
|
free_image(im);
|
|
free_image(resized);
|
|
free_image(crop);
|
|
if (filename) break;
|
|
}
|
|
}
|
|
|
|
|
|
void run_lsd(int argc, char **argv)
|
|
{
|
|
if(argc < 4){
|
|
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
|
|
return;
|
|
}
|
|
|
|
int clear = find_arg(argc, argv, "-clear");
|
|
int display = find_arg(argc, argv, "-display");
|
|
int batches = find_int_arg(argc, argv, "-b", 0);
|
|
char *file = find_char_arg(argc, argv, "-file", "/home/pjreddie/data/imagenet/imagenet1k.train.list");
|
|
|
|
char *cfg = argv[3];
|
|
char *weights = (argc > 4) ? argv[4] : 0;
|
|
char *filename = (argc > 5) ? argv[5] : 0;
|
|
char *acfg = argv[5];
|
|
char *aweights = (argc > 6) ? argv[6] : 0;
|
|
//if(0==strcmp(argv[2], "train")) train_lsd(cfg, weights, clear);
|
|
//else if(0==strcmp(argv[2], "train2")) train_lsd2(cfg, weights, acfg, aweights, clear);
|
|
//else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear);
|
|
//else if(0==strcmp(argv[2], "train3")) train_lsd3(argv[3], argv[4], argv[5], argv[6], argv[7], argv[8], clear);
|
|
if(0==strcmp(argv[2], "traingan")) train_dcgan(cfg, weights, acfg, aweights, clear, display, file, batches);
|
|
else if(0==strcmp(argv[2], "trainprog")) train_prog(cfg, weights, acfg, aweights, clear, display, file, batches);
|
|
else if(0==strcmp(argv[2], "traincolor")) train_colorizer(cfg, weights, acfg, aweights, clear, display);
|
|
else if(0==strcmp(argv[2], "gan")) test_dcgan(cfg, weights);
|
|
else if(0==strcmp(argv[2], "inter")) inter_dcgan(cfg, weights);
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else if(0==strcmp(argv[2], "test")) test_lsd(cfg, weights, filename, 0);
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else if(0==strcmp(argv[2], "color")) test_lsd(cfg, weights, filename, 1);
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/*
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else if(0==strcmp(argv[2], "valid")) validate_lsd(cfg, weights);
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*/
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}
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