mirror of https://github.com/pjreddie/darknet.git
460 lines
15 KiB
C
460 lines
15 KiB
C
#include "darknet.h"
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#include <sys/time.h>
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#include <assert.h>
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void extend_data_truth(data *d, int n, float val)
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{
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int i, j;
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for(i = 0; i < d->y.rows; ++i){
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d->y.vals[i] = realloc(d->y.vals[i], (d->y.cols+n)*sizeof(float));
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for(j = 0; j < n; ++j){
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d->y.vals[i][d->y.cols + j] = val;
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}
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}
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d->y.cols += n;
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}
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matrix network_loss_data(network *net, data test)
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{
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int i,b;
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int k = 1;
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matrix pred = make_matrix(test.X.rows, k);
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float *X = calloc(net->batch*test.X.cols, sizeof(float));
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float *y = calloc(net->batch*test.y.cols, sizeof(float));
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for(i = 0; i < test.X.rows; i += net->batch){
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for(b = 0; b < net->batch; ++b){
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if(i+b == test.X.rows) break;
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memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
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memcpy(y+b*test.y.cols, test.y.vals[i+b], test.y.cols*sizeof(float));
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}
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network orig = *net;
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net->input = X;
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net->truth = y;
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net->train = 0;
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net->delta = 0;
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forward_network(net);
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*net = orig;
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float *delta = net->layers[net->n-1].output;
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for(b = 0; b < net->batch; ++b){
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if(i+b == test.X.rows) break;
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int t = max_index(y + b*test.y.cols, 1000);
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float err = sum_array(delta + b*net->outputs, net->outputs);
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pred.vals[i+b][0] = -err;
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//pred.vals[i+b][0] = 1-delta[b*net->outputs + t];
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}
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}
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free(X);
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free(y);
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return pred;
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}
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void train_attention(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear)
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{
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int i, j;
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float avg_cls_loss = -1;
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float avg_att_loss = -1;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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printf("%d\n", ngpus);
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network **nets = calloc(ngpus, sizeof(network*));
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srand(time(0));
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int seed = rand();
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for(i = 0; i < ngpus; ++i){
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srand(seed);
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#ifdef GPU
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cuda_set_device(gpus[i]);
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#endif
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nets[i] = load_network(cfgfile, weightfile, clear);
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nets[i]->learning_rate *= ngpus;
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}
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srand(time(0));
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network *net = nets[0];
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int imgs = net->batch * net->subdivisions * ngpus;
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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list *options = read_data_cfg(datacfg);
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char *backup_directory = option_find_str(options, "backup", "/backup/");
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char *label_list = option_find_str(options, "labels", "data/labels.list");
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char *train_list = option_find_str(options, "train", "data/train.list");
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int classes = option_find_int(options, "classes", 2);
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char **labels = get_labels(label_list);
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list *plist = get_paths(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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double time;
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int divs=3;
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int size=2;
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load_args args = {0};
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args.w = divs*net->w/size;
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args.h = divs*net->h/size;
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args.size = divs*net->w/size;
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args.threads = 32;
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args.hierarchy = net->hierarchy;
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args.min = net->min_ratio*args.w;
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args.max = net->max_ratio*args.w;
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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.paths = paths;
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args.classes = classes;
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args.n = imgs;
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args.m = N;
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args.labels = labels;
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args.type = CLASSIFICATION_DATA;
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data train;
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data buffer;
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pthread_t load_thread;
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args.d = &buffer;
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load_thread = load_data(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 = what_time_is_it_now();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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data resized = resize_data(train, net->w, net->h);
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extend_data_truth(&resized, divs*divs, 0);
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data *tiles = tile_data(train, divs, size);
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printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
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time = what_time_is_it_now();
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float aloss = 0;
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float closs = 0;
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int z;
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for (i = 0; i < divs*divs/ngpus; ++i) {
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#pragma omp parallel for
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for(j = 0; j < ngpus; ++j){
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int index = i*ngpus + j;
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extend_data_truth(tiles+index, divs*divs, SECRET_NUM);
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matrix deltas = network_loss_data(nets[j], tiles[index]);
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for(z = 0; z < resized.y.rows; ++z){
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resized.y.vals[z][train.y.cols + index] = deltas.vals[z][0];
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}
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free_matrix(deltas);
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}
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}
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int *inds = calloc(resized.y.rows, sizeof(int));
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for(z = 0; z < resized.y.rows; ++z){
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int index = max_index(resized.y.vals[z] + train.y.cols, divs*divs);
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inds[z] = index;
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for(i = 0; i < divs*divs; ++i){
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resized.y.vals[z][train.y.cols + i] = (i == index)? 1 : 0;
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}
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}
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data best = select_data(tiles, inds);
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free(inds);
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#ifdef GPU
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if (ngpus == 1) {
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closs = train_network(net, best);
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} else {
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closs = train_networks(nets, ngpus, best, 4);
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}
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#endif
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for (i = 0; i < divs*divs; ++i) {
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printf("%.2f ", resized.y.vals[0][train.y.cols + i]);
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if((i+1)%divs == 0) printf("\n");
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free_data(tiles[i]);
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}
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free_data(best);
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printf("\n");
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image im = float_to_image(64,64,3,resized.X.vals[0]);
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//show_image(im, "orig");
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//cvWaitKey(100);
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/*
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image im1 = float_to_image(64,64,3,tiles[i].X.vals[0]);
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image im2 = float_to_image(64,64,3,resized.X.vals[0]);
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show_image(im1, "tile");
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show_image(im2, "res");
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*/
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#ifdef GPU
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if (ngpus == 1) {
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aloss = train_network(net, resized);
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} else {
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aloss = train_networks(nets, ngpus, resized, 4);
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}
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#endif
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for(i = 0; i < divs*divs; ++i){
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printf("%f ", nets[0]->output[1000 + i]);
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if ((i+1) % divs == 0) printf("\n");
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}
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printf("\n");
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free_data(resized);
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free_data(train);
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if(avg_cls_loss == -1) avg_cls_loss = closs;
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if(avg_att_loss == -1) avg_att_loss = aloss;
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avg_cls_loss = avg_cls_loss*.9 + closs*.1;
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avg_att_loss = avg_att_loss*.9 + aloss*.1;
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printf("%ld, %.3f: Att: %f, %f avg, Class: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, aloss, avg_att_loss, closs, avg_cls_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
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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)%1000 == 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_network(net);
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free_ptrs((void**)labels, classes);
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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 validate_attention_single(char *datacfg, char *filename, char *weightfile)
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{
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int i, j;
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network *net = load_network(filename, weightfile, 0);
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set_batch_network(net, 1);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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char *label_list = option_find_str(options, "labels", "data/labels.list");
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char *leaf_list = option_find_str(options, "leaves", 0);
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if(leaf_list) change_leaves(net->hierarchy, leaf_list);
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char *valid_list = option_find_str(options, "valid", "data/train.list");
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int classes = option_find_int(options, "classes", 2);
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int topk = option_find_int(options, "top", 1);
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char **labels = get_labels(label_list);
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list *plist = get_paths(valid_list);
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char **paths = (char **)list_to_array(plist);
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int m = plist->size;
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free_list(plist);
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float avg_acc = 0;
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float avg_topk = 0;
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int *indexes = calloc(topk, sizeof(int));
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int divs = 4;
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int size = 2;
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int extra = 0;
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float *avgs = calloc(classes, sizeof(float));
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int *inds = calloc(divs*divs, sizeof(int));
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for(i = 0; i < m; ++i){
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int class = -1;
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char *path = paths[i];
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for(j = 0; j < classes; ++j){
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if(strstr(path, labels[j])){
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class = j;
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break;
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}
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}
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image im = load_image_color(paths[i], 0, 0);
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image resized = resize_min(im, net->w*divs/size);
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image crop = crop_image(resized, (resized.w - net->w*divs/size)/2, (resized.h - net->h*divs/size)/2, net->w*divs/size, net->h*divs/size);
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image rcrop = resize_image(crop, net->w, net->h);
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//show_image(im, "orig");
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//show_image(crop, "cropped");
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//cvWaitKey(0);
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float *pred = network_predict(net, rcrop.data);
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//pred[classes + 56] = 0;
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for(j = 0; j < divs*divs; ++j){
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printf("%.2f ", pred[classes + j]);
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if((j+1)%divs == 0) printf("\n");
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}
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printf("\n");
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copy_cpu(classes, pred, 1, avgs, 1);
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top_k(pred + classes, divs*divs, divs*divs, inds);
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show_image(crop, "crop");
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for(j = 0; j < extra; ++j){
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int index = inds[j];
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int row = index / divs;
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int col = index % divs;
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int y = row * crop.h / divs - (net->h - crop.h/divs)/2;
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int x = col * crop.w / divs - (net->w - crop.w/divs)/2;
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printf("%d %d %d %d\n", row, col, y, x);
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image tile = crop_image(crop, x, y, net->w, net->h);
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float *pred = network_predict(net, tile.data);
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axpy_cpu(classes, 1., pred, 1, avgs, 1);
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show_image(tile, "tile");
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//cvWaitKey(10);
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}
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if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
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if(rcrop.data != resized.data) free_image(rcrop);
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if(resized.data != im.data) free_image(resized);
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free_image(im);
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free_image(crop);
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top_k(pred, classes, topk, indexes);
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if(indexes[0] == class) avg_acc += 1;
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for(j = 0; j < topk; ++j){
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if(indexes[j] == class) avg_topk += 1;
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}
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printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
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}
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}
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void validate_attention_multi(char *datacfg, char *filename, char *weightfile)
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{
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int i, j;
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network *net = load_network(filename, weightfile, 0);
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set_batch_network(net, 1);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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char *label_list = option_find_str(options, "labels", "data/labels.list");
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char *valid_list = option_find_str(options, "valid", "data/train.list");
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int classes = option_find_int(options, "classes", 2);
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int topk = option_find_int(options, "top", 1);
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char **labels = get_labels(label_list);
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list *plist = get_paths(valid_list);
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int scales[] = {224, 288, 320, 352, 384};
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int nscales = sizeof(scales)/sizeof(scales[0]);
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char **paths = (char **)list_to_array(plist);
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int m = plist->size;
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free_list(plist);
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float avg_acc = 0;
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float avg_topk = 0;
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int *indexes = calloc(topk, sizeof(int));
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for(i = 0; i < m; ++i){
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int class = -1;
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char *path = paths[i];
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for(j = 0; j < classes; ++j){
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if(strstr(path, labels[j])){
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class = j;
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break;
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}
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}
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float *pred = calloc(classes, sizeof(float));
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image im = load_image_color(paths[i], 0, 0);
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for(j = 0; j < nscales; ++j){
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image r = resize_min(im, scales[j]);
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resize_network(net, r.w, r.h);
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float *p = network_predict(net, r.data);
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if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1 , 1);
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axpy_cpu(classes, 1, p, 1, pred, 1);
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flip_image(r);
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p = network_predict(net, r.data);
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axpy_cpu(classes, 1, p, 1, pred, 1);
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if(r.data != im.data) free_image(r);
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}
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free_image(im);
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top_k(pred, classes, topk, indexes);
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free(pred);
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if(indexes[0] == class) avg_acc += 1;
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for(j = 0; j < topk; ++j){
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if(indexes[j] == class) avg_topk += 1;
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}
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printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
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}
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}
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void predict_attention(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
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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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list *options = read_data_cfg(datacfg);
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char *name_list = option_find_str(options, "names", 0);
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if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
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if(top == 0) top = option_find_int(options, "top", 1);
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int i = 0;
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char **names = get_labels(name_list);
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clock_t time;
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int *indexes = calloc(top, sizeof(int));
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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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image r = letterbox_image(im, net->w, net->h);
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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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if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
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top_k(predictions, net->outputs, top, indexes);
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fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time));
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for(i = 0; i < top; ++i){
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int index = indexes[i];
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//if(net->hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net->hierarchy->parent[index] >= 0) ? names[net->hierarchy->parent[index]] : "Root");
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//else printf("%s: %f\n",names[index], predictions[index]);
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printf("%5.2f%%: %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_attention(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 *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
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int ngpus;
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int *gpus = read_intlist(gpu_list, &ngpus, gpu_index);
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int top = find_int_arg(argc, argv, "-t", 0);
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int clear = find_arg(argc, argv, "-clear");
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char *data = argv[3];
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char *cfg = argv[4];
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char *weights = (argc > 5) ? argv[5] : 0;
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char *filename = (argc > 6) ? argv[6]: 0;
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char *layer_s = (argc > 7) ? argv[7]: 0;
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if(0==strcmp(argv[2], "predict")) predict_attention(data, cfg, weights, filename, top);
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else if(0==strcmp(argv[2], "train")) train_attention(data, cfg, weights, gpus, ngpus, clear);
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else if(0==strcmp(argv[2], "valid")) validate_attention_single(data, cfg, weights);
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else if(0==strcmp(argv[2], "validmulti")) validate_attention_multi(data, cfg, weights);
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}
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