#include "network.h"
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#include "utils.h"
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#include "parser.h"
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#include "option_list.h"
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#include "blas.h"
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#include "assert.h"
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#include "classifier.h"
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#include "dark_cuda.h"
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#ifdef WIN32
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#include <time.h>
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#include "gettimeofday.h"
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#else
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#include <sys/time.h>
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#endif
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float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom);
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float *get_regression_values(char **labels, int n)
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{
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float* v = (float*)xcalloc(n, sizeof(float));
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int i;
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for(i = 0; i < n; ++i){
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char *p = strchr(labels[i], ' ');
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*p = 0;
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v[i] = atof(p+1);
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}
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return v;
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}
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void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dontuse_opencv, int dont_show, int mjpeg_port, int calc_topk, int show_imgs, char* chart_path)
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{
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int i;
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float avg_loss = -1;
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float avg_contrastive_acc = 0;
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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 = (network*)xcalloc(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] = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&nets[i], weightfile);
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}
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if (clear) {
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*nets[i].seen = 0;
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*nets[i].cur_iteration = 0;
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}
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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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int topk_data = option_find_int(options, "top", 5);
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char topk_buff[10];
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sprintf(topk_buff, "top%d", topk_data);
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layer l = net.layers[net.n - 1];
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if (classes != l.outputs && (l.type == SOFTMAX || l.type == COST)) {
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printf("\n Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n",
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l.outputs, classes);
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getchar();
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}
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char **labels = get_labels(label_list);
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if (net.unsupervised) {
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free(labels);
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labels = NULL;
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}
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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 train_images_num = plist->size;
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clock_t time;
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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.c = net.c;
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args.threads = 32;
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if (net.contrastive && args.threads > net.batch/2) args.threads = net.batch / 2;
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args.hierarchy = net.hierarchy;
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args.contrastive = net.contrastive;
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args.dontuse_opencv = dontuse_opencv;
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args.min = net.min_crop;
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args.max = net.max_crop;
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args.flip = net.flip;
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args.blur = net.blur;
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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 > net.h ? net.w : net.h;
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args.label_smooth_eps = net.label_smooth_eps;
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args.mixup = net.mixup;
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if (dont_show && show_imgs) show_imgs = 2;
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args.show_imgs = show_imgs;
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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 = train_images_num;
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args.labels = labels;
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args.type = CLASSIFICATION_DATA;
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#ifdef OPENCV
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//args.threads = 3;
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mat_cv* img = NULL;
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float max_img_loss = net.max_chart_loss;
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int number_of_lines = 100;
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int img_size = 1000;
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char windows_name[100];
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sprintf(windows_name, "chart_%s.png", base);
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if (!dontuse_opencv) img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path);
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#endif //OPENCV
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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 iter_save = get_current_batch(net);
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int iter_save_last = get_current_batch(net);
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int iter_topk = get_current_batch(net);
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float topk = 0;
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int count = 0;
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double start, time_remaining, avg_time = -1, alpha_time = 0.01;
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start = what_time_is_it_now();
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while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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float loss = 0;
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#ifdef GPU
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if(ngpus == 1){
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loss = train_network(net, train);
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} else {
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loss = train_networks(nets, ngpus, train, 4);
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}
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#else
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loss = train_network(net, train);
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#endif
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if(avg_loss == -1 || isnan(avg_loss) || isinf(avg_loss)) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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i = get_current_batch(net);
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int calc_topk_for_each = iter_topk + 2 * train_images_num / (net.batch * net.subdivisions); // calculate TOPk for each 2 Epochs
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calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in);
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calc_topk_for_each = fmax(calc_topk_for_each, 100);
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if (i % 10 == 0) {
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if (calc_topk) {
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fprintf(stderr, "\n (next TOP%d calculation at %d iterations) ", topk_data, calc_topk_for_each);
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if (topk > 0) fprintf(stderr, " Last accuracy TOP%d = %2.2f %% \n", topk_data, topk * 100);
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}
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if (net.cudnn_half) {
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if (i < net.burn_in * 3) fprintf(stderr, " Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
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else fprintf(stderr, " Tensor Cores are used.\n");
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}
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}
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int draw_precision = 0;
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if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) {
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iter_topk = i;
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if (net.contrastive && l.type != SOFTMAX && l.type != COST) {
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int k;
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for (k = 0; k < net.n; ++k) if (net.layers[k].type == CONTRASTIVE) break;
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topk = *(net.layers[k].loss) / 100;
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sprintf(topk_buff, "Contr");
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}
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else {
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topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP-n
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printf("\n accuracy %s = %f \n", topk_buff, topk);
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}
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draw_precision = 1;
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}
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time_remaining = ((net.max_batches - i) / ngpus) * (what_time_is_it_now() - start) / 60 / 60;
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// set initial value, even if resume training from 10000 iteration
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if (avg_time < 0) avg_time = time_remaining;
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else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time;
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start = what_time_is_it_now();
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printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images, %f hours left\n", get_current_batch(net), (float)(*net.seen)/ train_images_num, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen, avg_time);
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#ifdef OPENCV
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if (net.contrastive) {
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float cur_con_acc = -1;
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int k;
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for (k = 0; k < net.n; ++k)
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if (net.layers[k].type == CONTRASTIVE) cur_con_acc = *net.layers[k].loss;
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if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01;
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printf(" avg_contrastive_acc = %f \n", avg_contrastive_acc);
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}
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if (!dontuse_opencv) draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, topk_buff, avg_contrastive_acc / 100, dont_show, mjpeg_port, avg_time);
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#endif // OPENCV
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if (i >= (iter_save + 1000)) {
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iter_save = i;
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#ifdef GPU
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if (ngpus != 1) sync_nets(nets, ngpus, 0);
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#endif
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
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save_weights(net, buff);
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}
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if (i >= (iter_save_last + 100)) {
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iter_save_last = i;
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#ifdef GPU
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if (ngpus != 1) sync_nets(nets, ngpus, 0);
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#endif
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char buff[256];
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sprintf(buff, "%s/%s_last.weights", backup_directory, base);
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save_weights(net, buff);
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}
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free_data(train);
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}
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#ifdef GPU
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if (ngpus != 1) sync_nets(nets, ngpus, 0);
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#endif
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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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#ifdef OPENCV
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release_mat(&img);
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destroy_all_windows_cv();
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#endif
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pthread_join(load_thread, 0);
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free_data(buffer);
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//free_network(net);
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for (i = 0; i < ngpus; ++i) free_network(nets[i]);
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free(nets);
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//free_ptrs((void**)labels, classes);
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if(labels) free(labels);
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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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free_list_contents_kvp(options);
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free_list(options);
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}
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/*
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void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
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{
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srand(time(0));
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float avg_loss = -1;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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if(clear) *net.seen = 0;
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int imgs = net.batch * net.subdivisions;
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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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clock_t time;
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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.threads = 8;
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args.min = net.min_crop;
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args.max = net.max_crop;
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args.flip = net.flip;
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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.hierarchy = net.hierarchy;
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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=clock();
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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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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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#ifdef OPENCV
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if(0){
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int u;
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for(u = 0; u < imgs; ++u){
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image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
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show_image(im, "loaded");
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cvWaitKey(0);
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}
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}
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#endif
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float loss = train_network(net, train);
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free_data(train);
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
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if(*net.seen/N > epoch){
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epoch = *net.seen/N;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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save_weights(net, buff);
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}
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if(get_current_batch(net)%100 == 0){
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char buff[256];
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sprintf(buff, "%s/%s.backup",backup_directory,base);
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save_weights(net, buff);
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}
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}
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char buff[256];
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sprintf(buff, "%s/%s.weights", backup_directory, base);
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save_weights(net, buff);
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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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*/
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void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
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{
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int i = 0;
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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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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if (topk > classes) topk = classes;
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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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clock_t time;
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float avg_acc = 0;
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float avg_topk = 0;
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int splits = m/1000;
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int num = (i+1)*m/splits - i*m/splits;
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data val, buffer;
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.paths = paths;
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args.classes = classes;
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args.n = num;
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args.m = 0;
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args.labels = labels;
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args.d = &buffer;
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args.type = OLD_CLASSIFICATION_DATA;
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pthread_t load_thread = load_data_in_thread(args);
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for(i = 1; i <= splits; ++i){
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time=clock();
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pthread_join(load_thread, 0);
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val = buffer;
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num = (i+1)*m/splits - i*m/splits;
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char **part = paths+(i*m/splits);
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if(i != splits){
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args.paths = part;
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load_thread = load_data_in_thread(args);
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}
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printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
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time=clock();
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float *acc = network_accuracies(net, val, topk);
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avg_acc += acc[0];
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avg_topk += acc[1];
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printf("%d: top 1: %f, top %d: %f, %lf seconds, %d images\n", i, avg_acc/i, topk, avg_topk/i, sec(clock()-time), val.X.rows);
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free_data(val);
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}
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}
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void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
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{
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int i, j;
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network net = parse_network_cfg(filename);
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set_batch_network(&net, 1);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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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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if (topk > classes) topk = classes;
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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 = (int*)xcalloc(topk, sizeof(int));
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for(i = 0; i < m; ++i){
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int class_id = -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_id = j;
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break;
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}
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}
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int w = net.w;
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int h = net.h;
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int shift = 32;
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image im = load_image_color(paths[i], w+shift, h+shift);
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image images[10];
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images[0] = crop_image(im, -shift, -shift, w, h);
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images[1] = crop_image(im, shift, -shift, w, h);
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images[2] = crop_image(im, 0, 0, w, h);
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images[3] = crop_image(im, -shift, shift, w, h);
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images[4] = crop_image(im, shift, shift, w, h);
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flip_image(im);
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images[5] = crop_image(im, -shift, -shift, w, h);
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images[6] = crop_image(im, shift, -shift, w, h);
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images[7] = crop_image(im, 0, 0, w, h);
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images[8] = crop_image(im, -shift, shift, w, h);
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images[9] = crop_image(im, shift, shift, w, h);
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float* pred = (float*)xcalloc(classes, sizeof(float));
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for(j = 0; j < 10; ++j){
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float *p = network_predict(net, images[j].data);
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if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
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axpy_cpu(classes, 1, p, 1, pred, 1);
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free_image(images[j]);
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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_id) avg_acc += 1;
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for(j = 0; j < topk; ++j){
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if(indexes[j] == class_id) 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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}
|
free(indexes);
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}
|
|
void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
|
{
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int i, j;
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network net = parse_network_cfg(filename);
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set_batch_network(&net, 1);
|
if(weightfile){
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load_weights(&net, weightfile);
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}
|
srand(time(0));
|
|
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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if (topk > classes) topk = classes;
|
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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;
|
free_list(plist);
|
|
float avg_acc = 0;
|
float avg_topk = 0;
|
int* indexes = (int*)xcalloc(topk, sizeof(int));
|
|
int size = net.w;
|
for(i = 0; i < m; ++i){
|
int class_id = -1;
|
char *path = paths[i];
|
for(j = 0; j < classes; ++j){
|
if(strstr(path, labels[j])){
|
class_id = j;
|
break;
|
}
|
}
|
image im = load_image_color(paths[i], 0, 0);
|
image resized = resize_min(im, size);
|
resize_network(&net, resized.w, resized.h);
|
//show_image(im, "orig");
|
//show_image(crop, "cropped");
|
//cvWaitKey(0);
|
float *pred = network_predict(net, resized.data);
|
if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
|
|
free_image(im);
|
free_image(resized);
|
top_k(pred, classes, topk, indexes);
|
|
if(indexes[0] == class_id) avg_acc += 1;
|
for(j = 0; j < topk; ++j){
|
if(indexes[j] == class_id) avg_topk += 1;
|
}
|
|
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
|
}
|
free(indexes);
|
}
|
|
|
float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom)
|
{
|
int i, j;
|
network net;
|
int old_batch = -1;
|
if (existing_net) {
|
net = *existing_net; // for validation during training
|
old_batch = net.batch;
|
set_batch_network(&net, 1);
|
}
|
else {
|
net = parse_network_cfg_custom(filename, 1, 0);
|
if (weightfile) {
|
load_weights(&net, weightfile);
|
}
|
//set_batch_network(&net, 1);
|
fuse_conv_batchnorm(net);
|
calculate_binary_weights(net);
|
}
|
srand(time(0));
|
|
list *options = read_data_cfg(datacfg);
|
|
char *label_list = option_find_str(options, "labels", "data/labels.list");
|
char *leaf_list = option_find_str(options, "leaves", 0);
|
if(leaf_list) change_leaves(net.hierarchy, leaf_list);
|
char *valid_list = option_find_str(options, "valid", "data/train.list");
|
int classes = option_find_int(options, "classes", 2);
|
int topk = option_find_int(options, "top", 1);
|
if (topk_custom > 0) topk = topk_custom; // for validation during training
|
if (topk > classes) topk = classes;
|
printf(" TOP calculation...\n");
|
|
char **labels = get_labels(label_list);
|
list *plist = get_paths(valid_list);
|
|
char **paths = (char **)list_to_array(plist);
|
int m = plist->size;
|
free_list(plist);
|
|
float avg_acc = 0;
|
float avg_topk = 0;
|
int* indexes = (int*)xcalloc(topk, sizeof(int));
|
|
for(i = 0; i < m; ++i){
|
int class_id = -1;
|
char *path = paths[i];
|
for(j = 0; j < classes; ++j){
|
if(strstr(path, labels[j])){
|
class_id = j;
|
break;
|
}
|
}
|
image im = load_image_color(paths[i], 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);
|
//show_image(im, "orig");
|
//show_image(crop, "cropped");
|
//cvWaitKey(0);
|
float *pred = network_predict(net, crop.data);
|
if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
|
|
if(resized.data != im.data) free_image(resized);
|
free_image(im);
|
free_image(crop);
|
top_k(pred, classes, topk, indexes);
|
|
if(indexes[0] == class_id) avg_acc += 1;
|
for(j = 0; j < topk; ++j){
|
if(indexes[j] == class_id) avg_topk += 1;
|
}
|
|
if (existing_net) printf("\r");
|
else printf("\n");
|
printf("%d: top 1: %f, top %d: %f", i, avg_acc/(i+1), topk, avg_topk/(i+1));
|
}
|
free(indexes);
|
if (existing_net) {
|
set_batch_network(&net, old_batch);
|
}
|
float topk_result = avg_topk / i;
|
return topk_result;
|
}
|
|
void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
|
{
|
int i, j;
|
network net = parse_network_cfg(filename);
|
set_batch_network(&net, 1);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
srand(time(0));
|
|
list *options = read_data_cfg(datacfg);
|
|
char *label_list = option_find_str(options, "labels", "data/labels.list");
|
char *valid_list = option_find_str(options, "valid", "data/train.list");
|
int classes = option_find_int(options, "classes", 2);
|
int topk = option_find_int(options, "top", 1);
|
if (topk > classes) topk = classes;
|
|
char **labels = get_labels(label_list);
|
list *plist = get_paths(valid_list);
|
int scales[] = {224, 288, 320, 352, 384};
|
int nscales = sizeof(scales)/sizeof(scales[0]);
|
|
char **paths = (char **)list_to_array(plist);
|
int m = plist->size;
|
free_list(plist);
|
|
float avg_acc = 0;
|
float avg_topk = 0;
|
int* indexes = (int*)xcalloc(topk, sizeof(int));
|
|
for(i = 0; i < m; ++i){
|
int class_id = -1;
|
char *path = paths[i];
|
for(j = 0; j < classes; ++j){
|
if(strstr(path, labels[j])){
|
class_id = j;
|
break;
|
}
|
}
|
float* pred = (float*)xcalloc(classes, sizeof(float));
|
image im = load_image_color(paths[i], 0, 0);
|
for(j = 0; j < nscales; ++j){
|
image r = resize_min(im, scales[j]);
|
resize_network(&net, r.w, r.h);
|
float *p = network_predict(net, r.data);
|
if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
|
axpy_cpu(classes, 1, p, 1, pred, 1);
|
flip_image(r);
|
p = network_predict(net, r.data);
|
axpy_cpu(classes, 1, p, 1, pred, 1);
|
if(r.data != im.data) free_image(r);
|
}
|
free_image(im);
|
top_k(pred, classes, topk, indexes);
|
free(pred);
|
if(indexes[0] == class_id) avg_acc += 1;
|
for(j = 0; j < topk; ++j){
|
if(indexes[j] == class_id) avg_topk += 1;
|
}
|
|
printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
|
}
|
free(indexes);
|
}
|
|
void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
|
{
|
network net = parse_network_cfg_custom(cfgfile, 1, 0);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
set_batch_network(&net, 1);
|
srand(2222222);
|
|
list *options = read_data_cfg(datacfg);
|
|
char *name_list = option_find_str(options, "names", 0);
|
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
|
int classes = option_find_int(options, "classes", 2);
|
int top = option_find_int(options, "top", 1);
|
if (top > classes) top = classes;
|
|
char **names = get_labels(name_list);
|
clock_t time;
|
int* indexes = (int*)xcalloc(top, sizeof(int));
|
char buff[256];
|
char *input = buff;
|
while(1){
|
if(filename){
|
strncpy(input, filename, 256);
|
}else{
|
printf("Enter Image Path: ");
|
fflush(stdout);
|
input = fgets(input, 256, stdin);
|
if(!input) break;
|
strtok(input, "\n");
|
}
|
image orig = load_image_color(input, 0, 0);
|
image r = resize_min(orig, 256);
|
image im = crop_image(r, (r.w - 224 - 1)/2 + 1, (r.h - 224 - 1)/2 + 1, 224, 224);
|
float mean[] = {0.48263312050943, 0.45230225481413, 0.40099074308742};
|
float std[] = {0.22590347483426, 0.22120921437787, 0.22103996251583};
|
float var[3];
|
var[0] = std[0]*std[0];
|
var[1] = std[1]*std[1];
|
var[2] = std[2]*std[2];
|
|
normalize_cpu(im.data, mean, var, 1, 3, im.w*im.h);
|
|
float *X = im.data;
|
time=clock();
|
float *predictions = network_predict(net, X);
|
|
layer l = net.layers[layer_num];
|
int i;
|
for(i = 0; i < l.c; ++i){
|
if(l.rolling_mean) printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
|
}
|
#ifdef GPU
|
cuda_pull_array(l.output_gpu, l.output, l.outputs);
|
#endif
|
for(i = 0; i < l.outputs; ++i){
|
printf("%f\n", l.output[i]);
|
}
|
/*
|
|
printf("\n\nWeights\n");
|
for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
|
printf("%f\n", l.filters[i]);
|
}
|
|
printf("\n\nBiases\n");
|
for(i = 0; i < l.n; ++i){
|
printf("%f\n", l.biases[i]);
|
}
|
*/
|
|
top_predictions(net, top, indexes);
|
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
for(i = 0; i < top; ++i){
|
int index = indexes[i];
|
printf("%s: %f\n", names[index], predictions[index]);
|
}
|
free_image(im);
|
if (filename) break;
|
}
|
free(indexes);
|
}
|
|
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
|
{
|
network net = parse_network_cfg_custom(cfgfile, 1, 0);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
set_batch_network(&net, 1);
|
srand(2222222);
|
|
fuse_conv_batchnorm(net);
|
calculate_binary_weights(net);
|
|
list *options = read_data_cfg(datacfg);
|
|
char *name_list = option_find_str(options, "names", 0);
|
if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
|
int classes = option_find_int(options, "classes", 2);
|
printf(" classes = %d, output in cfg = %d \n", classes, net.layers[net.n - 1].c);
|
layer l = net.layers[net.n - 1];
|
if (classes != l.outputs && (l.type == SOFTMAX || l.type == COST)) {
|
printf("\n Error: num of filters = %d in the last conv-layer in cfg-file doesn't match to classes = %d in data-file \n",
|
l.outputs, classes);
|
getchar();
|
}
|
if (top == 0) top = option_find_int(options, "top", 1);
|
if (top > classes) top = classes;
|
|
int i = 0;
|
char **names = get_labels(name_list);
|
clock_t time;
|
int* indexes = (int*)xcalloc(top, sizeof(int));
|
char buff[256];
|
char *input = buff;
|
//int size = net.w;
|
while(1){
|
if(filename){
|
strncpy(input, filename, 256);
|
}else{
|
printf("Enter Image Path: ");
|
fflush(stdout);
|
input = fgets(input, 256, stdin);
|
if(!input) break;
|
strtok(input, "\n");
|
}
|
image im = load_image_color(input, 0, 0);
|
image resized = resize_min(im, net.w);
|
image cropped = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
|
printf("%d %d\n", cropped.w, cropped.h);
|
|
float *X = cropped.data;
|
|
double time = get_time_point();
|
float *predictions = network_predict(net, X);
|
printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);
|
|
if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0);
|
top_k(predictions, net.outputs, top, indexes);
|
|
for(i = 0; i < top; ++i){
|
int index = indexes[i];
|
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");
|
else printf("%s: %f\n",names[index], predictions[index]);
|
}
|
|
free_image(cropped);
|
if (resized.data != im.data) {
|
free_image(resized);
|
}
|
free_image(im);
|
|
if (filename) break;
|
}
|
free(indexes);
|
free_network(net);
|
free_list_contents_kvp(options);
|
free_list(options);
|
}
|
|
|
void label_classifier(char *datacfg, char *filename, char *weightfile)
|
{
|
int i;
|
network net = parse_network_cfg(filename);
|
set_batch_network(&net, 1);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
srand(time(0));
|
|
list *options = read_data_cfg(datacfg);
|
|
char *label_list = option_find_str(options, "names", "data/labels.list");
|
char *test_list = option_find_str(options, "test", "data/train.list");
|
int classes = option_find_int(options, "classes", 2);
|
|
char **labels = get_labels(label_list);
|
list *plist = get_paths(test_list);
|
|
char **paths = (char **)list_to_array(plist);
|
int m = plist->size;
|
free_list(plist);
|
|
for(i = 0; i < m; ++i){
|
image im = load_image_color(paths[i], 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);
|
float *pred = network_predict(net, crop.data);
|
|
if(resized.data != im.data) free_image(resized);
|
free_image(im);
|
free_image(crop);
|
int ind = max_index(pred, classes);
|
|
printf("%s\n", labels[ind]);
|
}
|
}
|
|
|
void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
|
{
|
int curr = 0;
|
network net = parse_network_cfg(cfgfile);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
srand(time(0));
|
fuse_conv_batchnorm(net);
|
calculate_binary_weights(net);
|
|
list *options = read_data_cfg(datacfg);
|
|
char *test_list = option_find_str(options, "test", "data/test.list");
|
int classes = option_find_int(options, "classes", 2);
|
|
list *plist = get_paths(test_list);
|
|
char **paths = (char **)list_to_array(plist);
|
int m = plist->size;
|
free_list(plist);
|
|
clock_t time;
|
|
data val, buffer;
|
|
load_args args = {0};
|
args.w = net.w;
|
args.h = net.h;
|
args.paths = paths;
|
args.classes = classes;
|
args.n = net.batch;
|
args.m = 0;
|
args.labels = 0;
|
args.d = &buffer;
|
args.type = OLD_CLASSIFICATION_DATA;
|
|
pthread_t load_thread = load_data_in_thread(args);
|
for(curr = net.batch; curr < m; curr += net.batch){
|
time=clock();
|
|
pthread_join(load_thread, 0);
|
val = buffer;
|
|
if(curr < m){
|
args.paths = paths + curr;
|
if (curr + net.batch > m) args.n = m - curr;
|
load_thread = load_data_in_thread(args);
|
}
|
fprintf(stderr, "Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
|
|
time=clock();
|
matrix pred = network_predict_data(net, val);
|
|
int i, j;
|
if (target_layer >= 0){
|
//layer l = net.layers[target_layer];
|
}
|
|
for(i = 0; i < pred.rows; ++i){
|
printf("%s", paths[curr-net.batch+i]);
|
for(j = 0; j < pred.cols; ++j){
|
printf("\t%g", pred.vals[i][j]);
|
}
|
printf("\n");
|
}
|
|
free_matrix(pred);
|
|
fprintf(stderr, "%lf seconds, %d images, %d total\n", sec(clock()-time), val.X.rows, curr);
|
free_data(val);
|
}
|
}
|
|
|
void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
|
{
|
#ifdef OPENCV
|
float threat = 0;
|
float roll = .2;
|
|
printf("Classifier Demo\n");
|
network net = parse_network_cfg(cfgfile);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
set_batch_network(&net, 1);
|
list *options = read_data_cfg(datacfg);
|
|
srand(2222222);
|
cap_cv * cap;
|
|
if (filename) {
|
//cap = cvCaptureFromFile(filename);
|
cap = get_capture_video_stream(filename);
|
}
|
else {
|
//cap = cvCaptureFromCAM(cam_index);
|
cap = get_capture_webcam(cam_index);
|
}
|
|
int classes = option_find_int(options, "classes", 2);
|
int top = option_find_int(options, "top", 1);
|
if (top > classes) top = classes;
|
|
char *name_list = option_find_str(options, "names", 0);
|
char **names = get_labels(name_list);
|
|
int* indexes = (int*)xcalloc(top, sizeof(int));
|
|
if(!cap) error("Couldn't connect to webcam.\n");
|
create_window_cv("Threat", 0, 512, 512);
|
float fps = 0;
|
int i;
|
|
int count = 0;
|
|
while(1){
|
++count;
|
struct timeval tval_before, tval_after, tval_result;
|
gettimeofday(&tval_before, NULL);
|
|
//image in = get_image_from_stream(cap);
|
image in = get_image_from_stream_cpp(cap);
|
if(!in.data) break;
|
image in_s = resize_image(in, net.w, net.h);
|
|
image out = in;
|
int x1 = out.w / 20;
|
int y1 = out.h / 20;
|
int x2 = 2*x1;
|
int y2 = out.h - out.h/20;
|
|
int border = .01*out.h;
|
int h = y2 - y1 - 2*border;
|
int w = x2 - x1 - 2*border;
|
|
float *predictions = network_predict(net, in_s.data);
|
float curr_threat = 0;
|
if(1){
|
curr_threat = predictions[0] * 0 +
|
predictions[1] * .6 +
|
predictions[2];
|
} else {
|
curr_threat = predictions[218] +
|
predictions[539] +
|
predictions[540] +
|
predictions[368] +
|
predictions[369] +
|
predictions[370];
|
}
|
threat = roll * curr_threat + (1-roll) * threat;
|
|
draw_box_width(out, x2 + border, y1 + .02*h, x2 + .5 * w, y1 + .02*h + border, border, 0,0,0);
|
if(threat > .97) {
|
draw_box_width(out, x2 + .5 * w + border,
|
y1 + .02*h - 2*border,
|
x2 + .5 * w + 6*border,
|
y1 + .02*h + 3*border, 3*border, 1,0,0);
|
}
|
draw_box_width(out, x2 + .5 * w + border,
|
y1 + .02*h - 2*border,
|
x2 + .5 * w + 6*border,
|
y1 + .02*h + 3*border, .5*border, 0,0,0);
|
draw_box_width(out, x2 + border, y1 + .42*h, x2 + .5 * w, y1 + .42*h + border, border, 0,0,0);
|
if(threat > .57) {
|
draw_box_width(out, x2 + .5 * w + border,
|
y1 + .42*h - 2*border,
|
x2 + .5 * w + 6*border,
|
y1 + .42*h + 3*border, 3*border, 1,1,0);
|
}
|
draw_box_width(out, x2 + .5 * w + border,
|
y1 + .42*h - 2*border,
|
x2 + .5 * w + 6*border,
|
y1 + .42*h + 3*border, .5*border, 0,0,0);
|
|
draw_box_width(out, x1, y1, x2, y2, border, 0,0,0);
|
for(i = 0; i < threat * h ; ++i){
|
float ratio = (float) i / h;
|
float r = (ratio < .5) ? (2*(ratio)) : 1;
|
float g = (ratio < .5) ? 1 : 1 - 2*(ratio - .5);
|
draw_box_width(out, x1 + border, y2 - border - i, x2 - border, y2 - border - i, 1, r, g, 0);
|
}
|
top_predictions(net, top, indexes);
|
char buff[256];
|
sprintf(buff, "tmp/threat_%06d", count);
|
//save_image(out, buff);
|
|
#ifndef _WIN32
|
printf("\033[2J");
|
printf("\033[1;1H");
|
#endif
|
printf("\nFPS:%.0f\n",fps);
|
|
for(i = 0; i < top; ++i){
|
int index = indexes[i];
|
printf("%.1f%%: %s\n", predictions[index]*100, names[index]);
|
}
|
|
if(1){
|
show_image(out, "Threat");
|
wait_key_cv(10);
|
}
|
free_image(in_s);
|
free_image(in);
|
|
gettimeofday(&tval_after, NULL);
|
timersub(&tval_after, &tval_before, &tval_result);
|
float curr = 1000000.f/((long int)tval_result.tv_usec);
|
fps = .9*fps + .1*curr;
|
}
|
#endif
|
}
|
|
|
void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename)
|
{
|
#ifdef OPENCV_DISABLE
|
int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697};
|
|
printf("Classifier Demo\n");
|
network net = parse_network_cfg(cfgfile);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
set_batch_network(&net, 1);
|
list *options = read_data_cfg(datacfg);
|
|
srand(2222222);
|
CvCapture * cap;
|
|
if (filename) {
|
//cap = cvCaptureFromFile(filename);
|
cap = get_capture_video_stream(filename);
|
}
|
else {
|
//cap = cvCaptureFromCAM(cam_index);
|
cap = get_capture_webcam(cam_index);
|
}
|
|
int classes = option_find_int(options, "classes", 2);
|
int top = option_find_int(options, "top", 1);
|
if (top > classes) top = classes;
|
|
char *name_list = option_find_str(options, "names", 0);
|
char **names = get_labels(name_list);
|
|
int* indexes = (int*)xcalloc(top, sizeof(int));
|
|
if(!cap) error("Couldn't connect to webcam.\n");
|
cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL);
|
cvResizeWindow("Threat Detection", 512, 512);
|
float fps = 0;
|
int i;
|
|
while(1){
|
struct timeval tval_before, tval_after, tval_result;
|
gettimeofday(&tval_before, NULL);
|
|
//image in = get_image_from_stream(cap);
|
image in = get_image_from_stream_cpp(cap);
|
image in_s = resize_image(in, net.w, net.h);
|
show_image(in, "Threat Detection");
|
|
float *predictions = network_predict(net, in_s.data);
|
top_predictions(net, top, indexes);
|
|
printf("\033[2J");
|
printf("\033[1;1H");
|
|
int threat = 0;
|
for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
|
int index = bad_cats[i];
|
if(predictions[index] > .01){
|
printf("Threat Detected!\n");
|
threat = 1;
|
break;
|
}
|
}
|
if(!threat) printf("Scanning...\n");
|
for(i = 0; i < sizeof(bad_cats)/sizeof(bad_cats[0]); ++i){
|
int index = bad_cats[i];
|
if(predictions[index] > .01){
|
printf("%s\n", names[index]);
|
}
|
}
|
|
free_image(in_s);
|
free_image(in);
|
|
cvWaitKey(10);
|
|
gettimeofday(&tval_after, NULL);
|
timersub(&tval_after, &tval_before, &tval_result);
|
float curr = 1000000.f/((long int)tval_result.tv_usec);
|
fps = .9*fps + .1*curr;
|
}
|
#endif
|
}
|
|
void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_index, const char *filename, int benchmark, int benchmark_layers)
|
{
|
#ifdef OPENCV
|
printf("Classifier Demo\n");
|
network net = parse_network_cfg_custom(cfgfile, 1, 0);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
net.benchmark_layers = benchmark_layers;
|
set_batch_network(&net, 1);
|
list *options = read_data_cfg(datacfg);
|
|
fuse_conv_batchnorm(net);
|
calculate_binary_weights(net);
|
|
srand(2222222);
|
cap_cv * cap;
|
|
if(filename){
|
cap = get_capture_video_stream(filename);
|
}else{
|
cap = get_capture_webcam(cam_index);
|
}
|
|
int classes = option_find_int(options, "classes", 2);
|
int top = option_find_int(options, "top", 1);
|
if (top > classes) top = classes;
|
|
char *name_list = option_find_str(options, "names", 0);
|
char **names = get_labels(name_list);
|
|
int* indexes = (int*)xcalloc(top, sizeof(int));
|
|
if(!cap) error("Couldn't connect to webcam.\n");
|
if (!benchmark) create_window_cv("Classifier", 0, 512, 512);
|
float fps = 0;
|
int i;
|
|
double start_time = get_time_point();
|
float avg_fps = 0;
|
int frame_counter = 0;
|
|
while(1){
|
struct timeval tval_before, tval_after, tval_result;
|
gettimeofday(&tval_before, NULL);
|
|
//image in = get_image_from_stream(cap);
|
image in_s, in;
|
if (!benchmark) {
|
in = get_image_from_stream_cpp(cap);
|
in_s = resize_image(in, net.w, net.h);
|
show_image(in, "Classifier");
|
}
|
else {
|
static image tmp;
|
if (!tmp.data) tmp = make_image(net.w, net.h, 3);
|
in_s = tmp;
|
}
|
|
double time = get_time_point();
|
float *predictions = network_predict(net, in_s.data);
|
double frame_time_ms = (get_time_point() - time)/1000;
|
frame_counter++;
|
|
if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1);
|
top_predictions(net, top, indexes);
|
|
#ifndef _WIN32
|
printf("\033[2J");
|
printf("\033[1;1H");
|
#endif
|
|
|
if (!benchmark) {
|
printf("\rFPS: %.2f (use -benchmark command line flag for correct measurement)\n", fps);
|
for (i = 0; i < top; ++i) {
|
int index = indexes[i];
|
printf("%.1f%%: %s\n", predictions[index] * 100, names[index]);
|
}
|
printf("\n");
|
|
free_image(in_s);
|
free_image(in);
|
|
int c = wait_key_cv(10);// cvWaitKey(10);
|
if (c == 27 || c == 1048603) break;
|
}
|
else {
|
printf("\rFPS: %.2f \t AVG_FPS = %.2f ", fps, avg_fps);
|
}
|
|
//gettimeofday(&tval_after, NULL);
|
//timersub(&tval_after, &tval_before, &tval_result);
|
//float curr = 1000000.f/((long int)tval_result.tv_usec);
|
float curr = 1000.f / frame_time_ms;
|
if (fps == 0) fps = curr;
|
else fps = .9*fps + .1*curr;
|
|
float spent_time = (get_time_point() - start_time) / 1000000;
|
if (spent_time >= 3.0f) {
|
//printf(" spent_time = %f \n", spent_time);
|
avg_fps = frame_counter / spent_time;
|
frame_counter = 0;
|
start_time = get_time_point();
|
}
|
}
|
#endif
|
}
|
|
|
void run_classifier(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 mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
|
char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
|
int *gpus = 0;
|
int gpu = 0;
|
int ngpus = 0;
|
if(gpu_list){
|
printf("%s\n", gpu_list);
|
int len = strlen(gpu_list);
|
ngpus = 1;
|
int i;
|
for(i = 0; i < len; ++i){
|
if (gpu_list[i] == ',') ++ngpus;
|
}
|
gpus = (int*)xcalloc(ngpus, sizeof(int));
|
for(i = 0; i < ngpus; ++i){
|
gpus[i] = atoi(gpu_list);
|
gpu_list = strchr(gpu_list, ',')+1;
|
}
|
} else {
|
gpu = gpu_index;
|
gpus = &gpu;
|
ngpus = 1;
|
}
|
|
int dont_show = find_arg(argc, argv, "-dont_show");
|
int benchmark = find_arg(argc, argv, "-benchmark");
|
int benchmark_layers = find_arg(argc, argv, "-benchmark_layers");
|
if (benchmark_layers) benchmark = 1;
|
int dontuse_opencv = find_arg(argc, argv, "-dontuse_opencv");
|
int show_imgs = find_arg(argc, argv, "-show_imgs");
|
int calc_topk = find_arg(argc, argv, "-topk");
|
int cam_index = find_int_arg(argc, argv, "-c", 0);
|
int top = find_int_arg(argc, argv, "-t", 0);
|
int clear = find_arg(argc, argv, "-clear");
|
char *data = argv[3];
|
char *cfg = argv[4];
|
char *weights = (argc > 5) ? argv[5] : 0;
|
char *filename = (argc > 6) ? argv[6]: 0;
|
char *layer_s = (argc > 7) ? argv[7]: 0;
|
int layer = layer_s ? atoi(layer_s) : -1;
|
char* chart_path = find_char_arg(argc, argv, "-chart", 0);
|
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
|
else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
|
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear, dontuse_opencv, dont_show, mjpeg_port, calc_topk, show_imgs, chart_path);
|
else if(0==strcmp(argv[2], "demo")) demo_classifier(data, cfg, weights, cam_index, filename, benchmark, benchmark_layers);
|
else if(0==strcmp(argv[2], "gun")) gun_classifier(data, cfg, weights, cam_index, filename);
|
else if(0==strcmp(argv[2], "threat")) threat_classifier(data, cfg, weights, cam_index, filename);
|
else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
|
else if(0==strcmp(argv[2], "label")) label_classifier(data, cfg, weights);
|
else if(0==strcmp(argv[2], "valid")) validate_classifier_single(data, cfg, weights, NULL, -1);
|
else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);
|
else if(0==strcmp(argv[2], "valid10")) validate_classifier_10(data, cfg, weights);
|
else if(0==strcmp(argv[2], "validcrop")) validate_classifier_crop(data, cfg, weights);
|
else if(0==strcmp(argv[2], "validfull")) validate_classifier_full(data, cfg, weights);
|
|
if (gpus && gpu_list && ngpus > 1) free(gpus);
|
}
|
