From 0126e953b3f293b111179e4777103c64f778870c Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 17 六月 2021 09:57:28 +0800
Subject: [PATCH] bug

---
 lib/detecter_tools/darknet/classifier.c | 2808 ++++++++++++++++++++++++++++++-----------------------------
 1 files changed, 1,417 insertions(+), 1,391 deletions(-)

diff --git a/lib/detecter_tools/darknet/classifier.c b/lib/detecter_tools/darknet/classifier.c
index 588c832..f84e1dc 100644
--- a/lib/detecter_tools/darknet/classifier.c
+++ b/lib/detecter_tools/darknet/classifier.c
@@ -1,1391 +1,1417 @@
-#include "network.h"
-#include "utils.h"
-#include "parser.h"
-#include "option_list.h"
-#include "blas.h"
-#include "assert.h"
-#include "classifier.h"
-#include "dark_cuda.h"
-#ifdef WIN32
-#include <time.h>
-#include "gettimeofday.h"
-#else
-#include <sys/time.h>
-#endif
-
-float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom);
-
-float *get_regression_values(char **labels, int n)
-{
-    float* v = (float*)xcalloc(n, sizeof(float));
-    int i;
-    for(i = 0; i < n; ++i){
-        char *p = strchr(labels[i], ' ');
-        *p = 0;
-        v[i] = atof(p+1);
-    }
-    return v;
-}
-
-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)
-{
-    int i;
-
-    float avg_loss = -1;
-    char *base = basecfg(cfgfile);
-    printf("%s\n", base);
-    printf("%d\n", ngpus);
-    network* nets = (network*)xcalloc(ngpus, sizeof(network));
-
-    srand(time(0));
-    int seed = rand();
-    for(i = 0; i < ngpus; ++i){
-        srand(seed);
-#ifdef GPU
-        cuda_set_device(gpus[i]);
-#endif
-        nets[i] = parse_network_cfg(cfgfile);
-        if(weightfile){
-            load_weights(&nets[i], weightfile);
-        }
-        if (clear) {
-            *nets[i].seen = 0;
-            *nets[i].cur_iteration = 0;
-        }
-        nets[i].learning_rate *= ngpus;
-    }
-    srand(time(0));
-    network net = nets[0];
-
-    int imgs = net.batch * net.subdivisions * ngpus;
-
-    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
-    list *options = read_data_cfg(datacfg);
-
-    char *backup_directory = option_find_str(options, "backup", "/backup/");
-    char *label_list = option_find_str(options, "labels", "data/labels.list");
-    char *train_list = option_find_str(options, "train", "data/train.list");
-    int classes = option_find_int(options, "classes", 2);
-    int topk_data = option_find_int(options, "top", 5);
-    char topk_buff[10];
-    sprintf(topk_buff, "top%d", topk_data);
-    if (classes != net.layers[net.n - 1].inputs) {
-        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",
-            net.layers[net.n - 1].inputs, classes);
-        getchar();
-    }
-
-    char **labels = get_labels(label_list);
-    list *plist = get_paths(train_list);
-    char **paths = (char **)list_to_array(plist);
-    printf("%d\n", plist->size);
-    int train_images_num = plist->size;
-    clock_t time;
-
-    load_args args = {0};
-    args.w = net.w;
-    args.h = net.h;
-    args.c = net.c;
-    args.threads = 32;
-    args.hierarchy = net.hierarchy;
-
-    args.dontuse_opencv = dontuse_opencv;
-    args.min = net.min_crop;
-    args.max = net.max_crop;
-    args.flip = net.flip;
-    args.blur = net.blur;
-    args.angle = net.angle;
-    args.aspect = net.aspect;
-    args.exposure = net.exposure;
-    args.saturation = net.saturation;
-    args.hue = net.hue;
-    args.size = net.w > net.h ? net.w : net.h;
-
-    args.label_smooth_eps = net.label_smooth_eps;
-    args.mixup = net.mixup;
-    if (dont_show && show_imgs) show_imgs = 2;
-    args.show_imgs = show_imgs;
-
-    args.paths = paths;
-    args.classes = classes;
-    args.n = imgs;
-    args.m = train_images_num;
-    args.labels = labels;
-    args.type = CLASSIFICATION_DATA;
-
-#ifdef OPENCV
-    //args.threads = 3;
-    mat_cv* img = NULL;
-    float max_img_loss = 10;
-    int number_of_lines = 100;
-    int img_size = 1000;
-    char windows_name[100];
-    sprintf(windows_name, "chart_%s.png", base);
-    if (!dontuse_opencv) img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path);
-#endif  //OPENCV
-
-    data train;
-    data buffer;
-    pthread_t load_thread;
-    args.d = &buffer;
-    load_thread = load_data(args);
-
-    int iter_save = get_current_batch(net);
-    int iter_save_last = get_current_batch(net);
-    int iter_topk = get_current_batch(net);
-    float topk = 0;
-
-    int count = 0;
-    double start, time_remaining, avg_time = -1, alpha_time = 0.01;
-    start = what_time_is_it_now();
-
-    while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
-        time=clock();
-
-        pthread_join(load_thread, 0);
-        train = buffer;
-        load_thread = load_data(args);
-
-        printf("Loaded: %lf seconds\n", sec(clock()-time));
-        time=clock();
-
-        float loss = 0;
-#ifdef GPU
-        if(ngpus == 1){
-            loss = train_network(net, train);
-        } else {
-            loss = train_networks(nets, ngpus, train, 4);
-        }
-#else
-        loss = train_network(net, train);
-#endif
-        if(avg_loss == -1 || isnan(avg_loss) || isinf(avg_loss)) avg_loss = loss;
-        avg_loss = avg_loss*.9 + loss*.1;
-
-        i = get_current_batch(net);
-
-        int calc_topk_for_each = iter_topk + 2 * train_images_num / (net.batch * net.subdivisions);  // calculate TOPk for each 2 Epochs
-        calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in);
-        calc_topk_for_each = fmax(calc_topk_for_each, 100);
-        if (i % 10 == 0) {
-            if (calc_topk) {
-                fprintf(stderr, "\n (next TOP%d calculation at %d iterations) ", topk_data, calc_topk_for_each);
-                if (topk > 0) fprintf(stderr, " Last accuracy TOP%d = %2.2f %% \n", topk_data, topk * 100);
-            }
-
-            if (net.cudnn_half) {
-                if (i < net.burn_in * 3) fprintf(stderr, " Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
-                else fprintf(stderr, " Tensor Cores are used.\n");
-            }
-        }
-
-        int draw_precision = 0;
-        if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) {
-            iter_topk = i;
-            topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP-n
-            printf("\n accuracy %s = %f \n", topk_buff, topk);
-            draw_precision = 1;
-        }
-
-        time_remaining = ((net.max_batches - i) / ngpus) * (what_time_is_it_now() - start) / 60 / 60;
-        // set initial value, even if resume training from 10000 iteration
-        if (avg_time < 0) avg_time = time_remaining;
-        else avg_time = alpha_time * time_remaining + (1 -  alpha_time) * avg_time;
-        start = what_time_is_it_now();
-        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);
-#ifdef OPENCV
-        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, dont_show, mjpeg_port, avg_time);
-#endif  // OPENCV
-
-        if (i >= (iter_save + 1000)) {
-            iter_save = i;
-#ifdef GPU
-            if (ngpus != 1) sync_nets(nets, ngpus, 0);
-#endif
-            char buff[256];
-            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
-            save_weights(net, buff);
-        }
-
-        if (i >= (iter_save_last + 100)) {
-            iter_save_last = i;
-#ifdef GPU
-            if (ngpus != 1) sync_nets(nets, ngpus, 0);
-#endif
-            char buff[256];
-            sprintf(buff, "%s/%s_last.weights", backup_directory, base);
-            save_weights(net, buff);
-        }
-        free_data(train);
-    }
-#ifdef GPU
-    if (ngpus != 1) sync_nets(nets, ngpus, 0);
-#endif
-    char buff[256];
-    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
-    save_weights(net, buff);
-
-#ifdef OPENCV
-    release_mat(&img);
-    destroy_all_windows_cv();
-#endif
-
-    pthread_join(load_thread, 0);
-    free_data(buffer);
-
-    //free_network(net);
-    for (i = 0; i < ngpus; ++i) free_network(nets[i]);
-    free(nets);
-
-    //free_ptrs((void**)labels, classes);
-    free(labels);
-    free_ptrs((void**)paths, plist->size);
-    free_list(plist);
-    free(nets);
-    free(base);
-
-    free_list_contents_kvp(options);
-    free_list(options);
-
-}
-
-
-/*
-   void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
-   {
-   srand(time(0));
-   float avg_loss = -1;
-   char *base = basecfg(cfgfile);
-   printf("%s\n", base);
-   network net = parse_network_cfg(cfgfile);
-   if(weightfile){
-   load_weights(&net, weightfile);
-   }
-   if(clear) *net.seen = 0;
-
-   int imgs = net.batch * net.subdivisions;
-
-   printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
-   list *options = read_data_cfg(datacfg);
-
-   char *backup_directory = option_find_str(options, "backup", "/backup/");
-   char *label_list = option_find_str(options, "labels", "data/labels.list");
-   char *train_list = option_find_str(options, "train", "data/train.list");
-   int classes = option_find_int(options, "classes", 2);
-
-   char **labels = get_labels(label_list);
-   list *plist = get_paths(train_list);
-   char **paths = (char **)list_to_array(plist);
-   printf("%d\n", plist->size);
-   int N = plist->size;
-   clock_t time;
-
-   load_args args = {0};
-   args.w = net.w;
-   args.h = net.h;
-   args.threads = 8;
-
-   args.min = net.min_crop;
-   args.max = net.max_crop;
-   args.flip = net.flip;
-   args.angle = net.angle;
-   args.aspect = net.aspect;
-   args.exposure = net.exposure;
-   args.saturation = net.saturation;
-   args.hue = net.hue;
-   args.size = net.w;
-   args.hierarchy = net.hierarchy;
-
-   args.paths = paths;
-   args.classes = classes;
-   args.n = imgs;
-   args.m = N;
-   args.labels = labels;
-   args.type = CLASSIFICATION_DATA;
-
-   data train;
-   data buffer;
-   pthread_t load_thread;
-   args.d = &buffer;
-   load_thread = load_data(args);
-
-   int epoch = (*net.seen)/N;
-   while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
-   time=clock();
-
-   pthread_join(load_thread, 0);
-   train = buffer;
-   load_thread = load_data(args);
-
-   printf("Loaded: %lf seconds\n", sec(clock()-time));
-   time=clock();
-
-#ifdef OPENCV
-if(0){
-int u;
-for(u = 0; u < imgs; ++u){
-    image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
-    show_image(im, "loaded");
-    cvWaitKey(0);
-}
-}
-#endif
-
-float loss = train_network(net, train);
-free_data(train);
-
-if(avg_loss == -1) avg_loss = loss;
-avg_loss = avg_loss*.9 + loss*.1;
-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);
-if(*net.seen/N > epoch){
-    epoch = *net.seen/N;
-    char buff[256];
-    sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
-    save_weights(net, buff);
-}
-if(get_current_batch(net)%100 == 0){
-    char buff[256];
-    sprintf(buff, "%s/%s.backup",backup_directory,base);
-    save_weights(net, buff);
-}
-}
-char buff[256];
-sprintf(buff, "%s/%s.weights", backup_directory, base);
-save_weights(net, buff);
-
-free_network(net);
-free_ptrs((void**)labels, classes);
-free_ptrs((void**)paths, plist->size);
-free_list(plist);
-free(base);
-}
-*/
-
-void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
-{
-    int i = 0;
-    network net = parse_network_cfg(filename);
-    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);
-
-    char **paths = (char **)list_to_array(plist);
-    int m = plist->size;
-    free_list(plist);
-
-    clock_t time;
-    float avg_acc = 0;
-    float avg_topk = 0;
-    int splits = m/1000;
-    int num = (i+1)*m/splits - i*m/splits;
-
-    data val, buffer;
-
-    load_args args = {0};
-    args.w = net.w;
-    args.h = net.h;
-
-    args.paths = paths;
-    args.classes = classes;
-    args.n = num;
-    args.m = 0;
-    args.labels = labels;
-    args.d = &buffer;
-    args.type = OLD_CLASSIFICATION_DATA;
-
-    pthread_t load_thread = load_data_in_thread(args);
-    for(i = 1; i <= splits; ++i){
-        time=clock();
-
-        pthread_join(load_thread, 0);
-        val = buffer;
-
-        num = (i+1)*m/splits - i*m/splits;
-        char **part = paths+(i*m/splits);
-        if(i != splits){
-            args.paths = part;
-            load_thread = load_data_in_thread(args);
-        }
-        printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
-
-        time=clock();
-        float *acc = network_accuracies(net, val, topk);
-        avg_acc += acc[0];
-        avg_topk += acc[1];
-        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);
-        free_data(val);
-    }
-}
-
-void validate_classifier_10(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);
-
-    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;
-            }
-        }
-        int w = net.w;
-        int h = net.h;
-        int shift = 32;
-        image im = load_image_color(paths[i], w+shift, h+shift);
-        image images[10];
-        images[0] = crop_image(im, -shift, -shift, w, h);
-        images[1] = crop_image(im, shift, -shift, w, h);
-        images[2] = crop_image(im, 0, 0, w, h);
-        images[3] = crop_image(im, -shift, shift, w, h);
-        images[4] = crop_image(im, shift, shift, w, h);
-        flip_image(im);
-        images[5] = crop_image(im, -shift, -shift, w, h);
-        images[6] = crop_image(im, shift, -shift, w, h);
-        images[7] = crop_image(im, 0, 0, w, h);
-        images[8] = crop_image(im, -shift, shift, w, h);
-        images[9] = crop_image(im, shift, shift, w, h);
-        float* pred = (float*)xcalloc(classes, sizeof(float));
-        for(j = 0; j < 10; ++j){
-            float *p = network_predict(net, images[j].data);
-            if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
-            axpy_cpu(classes, 1, p, 1, pred, 1);
-            free_image(images[j]);
-        }
-        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 validate_classifier_full(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);
-
-    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));
-
-    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);
-    if (classes != net.layers[net.n - 1].inputs) {
-        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",
-            net.layers[net.n - 1].inputs, 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 r = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
-        //image r = resize_min(im, size);
-        //resize_network(&net, r.w, r.h);
-        printf("%d %d\n", r.w, r.h);
-
-        float *X = r.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]);
-        }
-        if(r.data != im.data) free_image(r);
-        free_image(im);
-        free_image(resized);
-        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);
-}
+#include "network.h"
+#include "utils.h"
+#include "parser.h"
+#include "option_list.h"
+#include "blas.h"
+#include "assert.h"
+#include "classifier.h"
+#include "dark_cuda.h"
+#ifdef WIN32
+#include <time.h>
+#include "gettimeofday.h"
+#else
+#include <sys/time.h>
+#endif
+
+float validate_classifier_single(char *datacfg, char *filename, char *weightfile, network *existing_net, int topk_custom);
+
+float *get_regression_values(char **labels, int n)
+{
+    float* v = (float*)xcalloc(n, sizeof(float));
+    int i;
+    for(i = 0; i < n; ++i){
+        char *p = strchr(labels[i], ' ');
+        *p = 0;
+        v[i] = atof(p+1);
+    }
+    return v;
+}
+
+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)
+{
+    int i;
+
+    float avg_loss = -1;
+    float avg_contrastive_acc = 0;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network* nets = (network*)xcalloc(ngpus, sizeof(network));
+
+    srand(time(0));
+    int seed = rand();
+    for(i = 0; i < ngpus; ++i){
+        srand(seed);
+#ifdef GPU
+        cuda_set_device(gpus[i]);
+#endif
+        nets[i] = parse_network_cfg(cfgfile);
+        if(weightfile){
+            load_weights(&nets[i], weightfile);
+        }
+        if (clear) {
+            *nets[i].seen = 0;
+            *nets[i].cur_iteration = 0;
+        }
+        nets[i].learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network net = nets[0];
+
+    int imgs = net.batch * net.subdivisions * ngpus;
+
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+    list *options = read_data_cfg(datacfg);
+
+    char *backup_directory = option_find_str(options, "backup", "/backup/");
+    char *label_list = option_find_str(options, "labels", "data/labels.list");
+    char *train_list = option_find_str(options, "train", "data/train.list");
+    int classes = option_find_int(options, "classes", 2);
+    int topk_data = option_find_int(options, "top", 5);
+    char topk_buff[10];
+    sprintf(topk_buff, "top%d", topk_data);
+    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();
+    }
+
+    char **labels = get_labels(label_list);
+    if (net.unsupervised) {
+        free(labels);
+        labels = NULL;
+    }
+    list *plist = get_paths(train_list);
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int train_images_num = plist->size;
+    clock_t time;
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+    args.c = net.c;
+    args.threads = 32;
+    if (net.contrastive && args.threads > net.batch/2) args.threads = net.batch / 2;
+    args.hierarchy = net.hierarchy;
+
+    args.contrastive = net.contrastive;
+    args.dontuse_opencv = dontuse_opencv;
+    args.min = net.min_crop;
+    args.max = net.max_crop;
+    args.flip = net.flip;
+    args.blur = net.blur;
+    args.angle = net.angle;
+    args.aspect = net.aspect;
+    args.exposure = net.exposure;
+    args.saturation = net.saturation;
+    args.hue = net.hue;
+    args.size = net.w > net.h ? net.w : net.h;
+
+    args.label_smooth_eps = net.label_smooth_eps;
+    args.mixup = net.mixup;
+    if (dont_show && show_imgs) show_imgs = 2;
+    args.show_imgs = show_imgs;
+
+    args.paths = paths;
+    args.classes = classes;
+    args.n = imgs;
+    args.m = train_images_num;
+    args.labels = labels;
+    args.type = CLASSIFICATION_DATA;
+
+#ifdef OPENCV
+    //args.threads = 3;
+    mat_cv* img = NULL;
+    float max_img_loss = net.max_chart_loss;
+    int number_of_lines = 100;
+    int img_size = 1000;
+    char windows_name[100];
+    sprintf(windows_name, "chart_%s.png", base);
+    if (!dontuse_opencv) img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path);
+#endif  //OPENCV
+
+    data train;
+    data buffer;
+    pthread_t load_thread;
+    args.d = &buffer;
+    load_thread = load_data(args);
+
+    int iter_save = get_current_batch(net);
+    int iter_save_last = get_current_batch(net);
+    int iter_topk = get_current_batch(net);
+    float topk = 0;
+
+    int count = 0;
+    double start, time_remaining, avg_time = -1, alpha_time = 0.01;
+    start = what_time_is_it_now();
+
+    while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+        time=clock();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+
+        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        time=clock();
+
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(nets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        if(avg_loss == -1 || isnan(avg_loss) || isinf(avg_loss)) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+
+        i = get_current_batch(net);
+
+        int calc_topk_for_each = iter_topk + 2 * train_images_num / (net.batch * net.subdivisions);  // calculate TOPk for each 2 Epochs
+        calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in);
+        calc_topk_for_each = fmax(calc_topk_for_each, 100);
+        if (i % 10 == 0) {
+            if (calc_topk) {
+                fprintf(stderr, "\n (next TOP%d calculation at %d iterations) ", topk_data, calc_topk_for_each);
+                if (topk > 0) fprintf(stderr, " Last accuracy TOP%d = %2.2f %% \n", topk_data, topk * 100);
+            }
+
+            if (net.cudnn_half) {
+                if (i < net.burn_in * 3) fprintf(stderr, " Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
+                else fprintf(stderr, " Tensor Cores are used.\n");
+            }
+        }
+
+        int draw_precision = 0;
+        if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) {
+            iter_topk = i;
+            if (net.contrastive && l.type != SOFTMAX && l.type != COST) {
+                int k;
+                for (k = 0; k < net.n; ++k) if (net.layers[k].type == CONTRASTIVE) break;
+                topk = *(net.layers[k].loss) / 100;
+                sprintf(topk_buff, "Contr");
+            }
+            else {
+                topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP-n
+                printf("\n accuracy %s = %f \n", topk_buff, topk);
+            }
+            draw_precision = 1;
+        }
+
+        time_remaining = ((net.max_batches - i) / ngpus) * (what_time_is_it_now() - start) / 60 / 60;
+        // set initial value, even if resume training from 10000 iteration
+        if (avg_time < 0) avg_time = time_remaining;
+        else avg_time = alpha_time * time_remaining + (1 -  alpha_time) * avg_time;
+        start = what_time_is_it_now();
+        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);
+#ifdef OPENCV
+        if (net.contrastive) {
+            float cur_con_acc = -1;
+            int k;
+            for (k = 0; k < net.n; ++k)
+                if (net.layers[k].type == CONTRASTIVE) cur_con_acc = *net.layers[k].loss;
+            if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01;
+            printf("  avg_contrastive_acc = %f \n", avg_contrastive_acc);
+        }
+        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);
+#endif  // OPENCV
+
+        if (i >= (iter_save + 1000)) {
+            iter_save = i;
+#ifdef GPU
+            if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+            char buff[256];
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            save_weights(net, buff);
+        }
+
+        if (i >= (iter_save_last + 100)) {
+            iter_save_last = i;
+#ifdef GPU
+            if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+            char buff[256];
+            sprintf(buff, "%s/%s_last.weights", backup_directory, base);
+            save_weights(net, buff);
+        }
+        free_data(train);
+    }
+#ifdef GPU
+    if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+    char buff[256];
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+    save_weights(net, buff);
+
+#ifdef OPENCV
+    release_mat(&img);
+    destroy_all_windows_cv();
+#endif
+
+    pthread_join(load_thread, 0);
+    free_data(buffer);
+
+    //free_network(net);
+    for (i = 0; i < ngpus; ++i) free_network(nets[i]);
+    free(nets);
+
+    //free_ptrs((void**)labels, classes);
+    if(labels) free(labels);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+
+    free_list_contents_kvp(options);
+    free_list(options);
+
+}
+
+
+/*
+   void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
+   {
+   srand(time(0));
+   float avg_loss = -1;
+   char *base = basecfg(cfgfile);
+   printf("%s\n", base);
+   network net = parse_network_cfg(cfgfile);
+   if(weightfile){
+   load_weights(&net, weightfile);
+   }
+   if(clear) *net.seen = 0;
+
+   int imgs = net.batch * net.subdivisions;
+
+   printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+   list *options = read_data_cfg(datacfg);
+
+   char *backup_directory = option_find_str(options, "backup", "/backup/");
+   char *label_list = option_find_str(options, "labels", "data/labels.list");
+   char *train_list = option_find_str(options, "train", "data/train.list");
+   int classes = option_find_int(options, "classes", 2);
+
+   char **labels = get_labels(label_list);
+   list *plist = get_paths(train_list);
+   char **paths = (char **)list_to_array(plist);
+   printf("%d\n", plist->size);
+   int N = plist->size;
+   clock_t time;
+
+   load_args args = {0};
+   args.w = net.w;
+   args.h = net.h;
+   args.threads = 8;
+
+   args.min = net.min_crop;
+   args.max = net.max_crop;
+   args.flip = net.flip;
+   args.angle = net.angle;
+   args.aspect = net.aspect;
+   args.exposure = net.exposure;
+   args.saturation = net.saturation;
+   args.hue = net.hue;
+   args.size = net.w;
+   args.hierarchy = net.hierarchy;
+
+   args.paths = paths;
+   args.classes = classes;
+   args.n = imgs;
+   args.m = N;
+   args.labels = labels;
+   args.type = CLASSIFICATION_DATA;
+
+   data train;
+   data buffer;
+   pthread_t load_thread;
+   args.d = &buffer;
+   load_thread = load_data(args);
+
+   int epoch = (*net.seen)/N;
+   while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
+   time=clock();
+
+   pthread_join(load_thread, 0);
+   train = buffer;
+   load_thread = load_data(args);
+
+   printf("Loaded: %lf seconds\n", sec(clock()-time));
+   time=clock();
+
+#ifdef OPENCV
+if(0){
+int u;
+for(u = 0; u < imgs; ++u){
+    image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
+    show_image(im, "loaded");
+    cvWaitKey(0);
+}
+}
+#endif
+
+float loss = train_network(net, train);
+free_data(train);
+
+if(avg_loss == -1) avg_loss = loss;
+avg_loss = avg_loss*.9 + loss*.1;
+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);
+if(*net.seen/N > epoch){
+    epoch = *net.seen/N;
+    char buff[256];
+    sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+    save_weights(net, buff);
+}
+if(get_current_batch(net)%100 == 0){
+    char buff[256];
+    sprintf(buff, "%s/%s.backup",backup_directory,base);
+    save_weights(net, buff);
+}
+}
+char buff[256];
+sprintf(buff, "%s/%s.weights", backup_directory, base);
+save_weights(net, buff);
+
+free_network(net);
+free_ptrs((void**)labels, classes);
+free_ptrs((void**)paths, plist->size);
+free_list(plist);
+free(base);
+}
+*/
+
+void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
+{
+    int i = 0;
+    network net = parse_network_cfg(filename);
+    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);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+
+    clock_t time;
+    float avg_acc = 0;
+    float avg_topk = 0;
+    int splits = m/1000;
+    int num = (i+1)*m/splits - i*m/splits;
+
+    data val, buffer;
+
+    load_args args = {0};
+    args.w = net.w;
+    args.h = net.h;
+
+    args.paths = paths;
+    args.classes = classes;
+    args.n = num;
+    args.m = 0;
+    args.labels = labels;
+    args.d = &buffer;
+    args.type = OLD_CLASSIFICATION_DATA;
+
+    pthread_t load_thread = load_data_in_thread(args);
+    for(i = 1; i <= splits; ++i){
+        time=clock();
+
+        pthread_join(load_thread, 0);
+        val = buffer;
+
+        num = (i+1)*m/splits - i*m/splits;
+        char **part = paths+(i*m/splits);
+        if(i != splits){
+            args.paths = part;
+            load_thread = load_data_in_thread(args);
+        }
+        printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+
+        time=clock();
+        float *acc = network_accuracies(net, val, topk);
+        avg_acc += acc[0];
+        avg_topk += acc[1];
+        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);
+        free_data(val);
+    }
+}
+
+void validate_classifier_10(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);
+
+    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;
+            }
+        }
+        int w = net.w;
+        int h = net.h;
+        int shift = 32;
+        image im = load_image_color(paths[i], w+shift, h+shift);
+        image images[10];
+        images[0] = crop_image(im, -shift, -shift, w, h);
+        images[1] = crop_image(im, shift, -shift, w, h);
+        images[2] = crop_image(im, 0, 0, w, h);
+        images[3] = crop_image(im, -shift, shift, w, h);
+        images[4] = crop_image(im, shift, shift, w, h);
+        flip_image(im);
+        images[5] = crop_image(im, -shift, -shift, w, h);
+        images[6] = crop_image(im, shift, -shift, w, h);
+        images[7] = crop_image(im, 0, 0, w, h);
+        images[8] = crop_image(im, -shift, shift, w, h);
+        images[9] = crop_image(im, shift, shift, w, h);
+        float* pred = (float*)xcalloc(classes, sizeof(float));
+        for(j = 0; j < 10; ++j){
+            float *p = network_predict(net, images[j].data);
+            if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
+            axpy_cpu(classes, 1, p, 1, pred, 1);
+            free_image(images[j]);
+        }
+        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 validate_classifier_full(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);
+
+    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));
+
+    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);
+}

--
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