mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
it's raining really hard outside :-( :rain: :storm: ☁️
This commit is contained in:
Joseph Redmon
@@ -23,7 +23,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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printf("%d\n", ngpus);
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network *nets = calloc(ngpus, sizeof(network));
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network **nets = calloc(ngpus, sizeof(network*));
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srand(time(0));
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int seed = rand();
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@@ -33,14 +33,14 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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cuda_set_device(gpus[i]);
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#endif
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nets[i] = load_network(cfgfile, weightfile, clear);
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nets[i].learning_rate *= ngpus;
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nets[i]->learning_rate *= ngpus;
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}
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srand(time(0));
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network net = nets[0];
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network *net = nets[0];
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int imgs = net.batch * net.subdivisions * ngpus;
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int imgs = net->batch * net->subdivisions * ngpus;
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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list *options = read_data_cfg(datacfg);
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char *backup_directory = option_find_str(options, "backup", "/backup/");
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@@ -56,19 +56,20 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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double time;
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.w = net->w;
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args.h = net->h;
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args.threads = 32;
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args.hierarchy = net.hierarchy;
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args.hierarchy = net->hierarchy;
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args.min = net.min_crop;
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args.max = net.max_crop;
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args.angle = net.angle;
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args.aspect = net.aspect;
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args.exposure = net.exposure;
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args.saturation = net.saturation;
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args.hue = net.hue;
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args.size = net.w;
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args.min = net->min_ratio*net->w;
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args.max = net->max_ratio*net->w;
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printf("%d %d\n", args.min, args.max);
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args.angle = net->angle;
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args.aspect = net->aspect;
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args.exposure = net->exposure;
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args.saturation = net->saturation;
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args.hue = net->hue;
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args.size = net->w;
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args.paths = paths;
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args.classes = classes;
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@@ -83,8 +84,32 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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args.d = &buffer;
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load_thread = load_data(args);
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int epoch = (*net.seen)/N;
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while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
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int count = 0;
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int epoch = (*net->seen)/N;
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while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
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if(net->random && count++%40 == 0){
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printf("Resizing\n");
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int dim = (rand() % 11 + 4) * 32;
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//if (get_current_batch(net)+200 > net->max_batches) dim = 608;
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//int dim = (rand() % 4 + 16) * 32;
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printf("%d\n", dim);
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args.w = dim;
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args.h = dim;
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args.size = dim;
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args.min = net->min_ratio*dim;
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args.max = net->max_ratio*dim;
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printf("%d %d\n", args.min, args.max);
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pthread_join(load_thread, 0);
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train = buffer;
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free_data(train);
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load_thread = load_data(args);
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for(i = 0; i < ngpus; ++i){
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resize_network(nets[i], dim, dim);
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}
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net = nets[0];
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}
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time = what_time_is_it_now();
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pthread_join(load_thread, 0);
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@@ -106,10 +131,10 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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#endif
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net.seen);
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printf("%ld, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net->seen)/N, loss, avg_loss, get_current_rate(net), what_time_is_it_now()-time, *net->seen);
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free_data(train);
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if(*net.seen/N > epoch){
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epoch = *net.seen/N;
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if(*net->seen/N > epoch){
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epoch = *net->seen/N;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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save_weights(net, buff);
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@@ -132,124 +157,10 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
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free(base);
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}
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/*
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void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
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{
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srand(time(0));
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float avg_loss = -1;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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if(clear) *net.seen = 0;
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int imgs = net.batch * net.subdivisions;
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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list *options = read_data_cfg(datacfg);
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char *backup_directory = option_find_str(options, "backup", "/backup/");
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char *label_list = option_find_str(options, "labels", "data/labels.list");
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char *train_list = option_find_str(options, "train", "data/train.list");
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int classes = option_find_int(options, "classes", 2);
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char **labels = get_labels(label_list);
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list *plist = get_paths(train_list);
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char **paths = (char **)list_to_array(plist);
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printf("%d\n", plist->size);
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int N = plist->size;
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clock_t time;
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.threads = 8;
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args.min = net.min_crop;
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args.max = net.max_crop;
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args.angle = net.angle;
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args.aspect = net.aspect;
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args.exposure = net.exposure;
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args.saturation = net.saturation;
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args.hue = net.hue;
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args.size = net.w;
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args.hierarchy = net.hierarchy;
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args.paths = paths;
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args.classes = classes;
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args.n = imgs;
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args.m = N;
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args.labels = labels;
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args.type = CLASSIFICATION_DATA;
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data train;
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data buffer;
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pthread_t load_thread;
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args.d = &buffer;
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load_thread = load_data(args);
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int epoch = (*net.seen)/N;
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while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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#ifdef OPENCV
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if(0){
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int u;
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for(u = 0; u < imgs; ++u){
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image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
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show_image(im, "loaded");
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cvWaitKey(0);
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}
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}
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#endif
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float loss = train_network(net, train);
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free_data(train);
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if(avg_loss == -1) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
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if(*net.seen/N > epoch){
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epoch = *net.seen/N;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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save_weights(net, buff);
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}
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if(get_current_batch(net)%100 == 0){
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char buff[256];
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sprintf(buff, "%s/%s.backup",backup_directory,base);
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save_weights(net, buff);
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}
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}
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char buff[256];
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sprintf(buff, "%s/%s.weights", backup_directory, base);
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save_weights(net, buff);
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free_network(net);
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free_ptrs((void**)labels, classes);
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free_ptrs((void**)paths, plist->size);
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free_list(plist);
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free(base);
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}
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*/
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void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
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{
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int i = 0;
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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network *net = load_network(filename, weightfile, 0);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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@@ -275,8 +186,8 @@ void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
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data val, buffer;
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.w = net->w;
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args.h = net->h;
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args.paths = paths;
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args.classes = classes;
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@@ -313,11 +224,8 @@ void validate_classifier_crop(char *datacfg, char *filename, char *weightfile)
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void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
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{
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int i, j;
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network net = parse_network_cfg(filename);
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set_batch_network(&net, 1);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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network *net = load_network(filename, weightfile, 0);
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set_batch_network(net, 1);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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@@ -347,8 +255,8 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
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break;
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}
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}
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int w = net.w;
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int h = net.h;
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int w = net->w;
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int h = net->h;
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int shift = 32;
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image im = load_image_color(paths[i], w+shift, h+shift);
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image images[10];
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@@ -366,7 +274,7 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
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float *pred = calloc(classes, sizeof(float));
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for(j = 0; j < 10; ++j){
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float *p = network_predict(net, images[j].data);
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if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1, 1);
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if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1, 1);
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axpy_cpu(classes, 1, p, 1, pred, 1);
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free_image(images[j]);
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}
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@@ -385,11 +293,8 @@ void validate_classifier_10(char *datacfg, char *filename, char *weightfile)
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void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
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{
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int i, j;
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network net = parse_network_cfg(filename);
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set_batch_network(&net, 1);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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network *net = load_network(filename, weightfile, 0);
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set_batch_network(net, 1);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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@@ -410,7 +315,7 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
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float avg_topk = 0;
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int *indexes = calloc(topk, sizeof(int));
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int size = net.w;
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int size = net->w;
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for(i = 0; i < m; ++i){
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int class = -1;
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char *path = paths[i];
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@@ -422,12 +327,12 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
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}
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image im = load_image_color(paths[i], 0, 0);
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image resized = resize_min(im, size);
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resize_network(&net, resized.w, resized.h);
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resize_network(net, resized.w, resized.h);
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//show_image(im, "orig");
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//show_image(crop, "cropped");
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//cvWaitKey(0);
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float *pred = network_predict(net, resized.data);
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if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1, 1);
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if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
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free_image(im);
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free_image(resized);
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@@ -446,18 +351,15 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
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void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
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{
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int i, j;
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network net = parse_network_cfg(filename);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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network *net = load_network(filename, weightfile, 0);
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set_batch_network(net, 1);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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char *label_list = option_find_str(options, "labels", "data/labels.list");
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char *leaf_list = option_find_str(options, "leaves", 0);
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if(leaf_list) change_leaves(net.hierarchy, leaf_list);
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if(leaf_list) change_leaves(net->hierarchy, leaf_list);
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char *valid_list = option_find_str(options, "valid", "data/train.list");
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int classes = option_find_int(options, "classes", 2);
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int topk = option_find_int(options, "top", 1);
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@@ -483,13 +385,13 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
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}
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}
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image im = load_image_color(paths[i], 0, 0);
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image resized = resize_min(im, net.w);
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image crop = crop_image(resized, (resized.w - net.w)/2, (resized.h - net.h)/2, net.w, net.h);
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image resized = resize_min(im, net->w);
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image crop = crop_image(resized, (resized.w - net->w)/2, (resized.h - net->h)/2, net->w, net->h);
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//show_image(im, "orig");
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//show_image(crop, "cropped");
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//cvWaitKey(0);
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float *pred = network_predict(net, crop.data);
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if(net.hierarchy) hierarchy_predictions(pred, net.outputs, net.hierarchy, 1, 1);
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if(net->hierarchy) hierarchy_predictions(pred, net->outputs, net->hierarchy, 1, 1);
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if(resized.data != im.data) free_image(resized);
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free_image(im);
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@@ -505,14 +407,11 @@ void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
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}
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}
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void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
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void validate_classifier_multi(char *datacfg, char *cfg, char *weights)
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{
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int i, j;
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network net = parse_network_cfg(filename);
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set_batch_network(&net, 1);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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network *net = load_network(cfg, weights, 0);
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set_batch_network(net, 1);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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@@ -524,7 +423,8 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
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char **labels = get_labels(label_list);
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list *plist = get_paths(valid_list);
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int scales[] = {224, 288, 320, 352, 384};
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//int scales[] = {224, 288, 320, 352, 384};
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int scales[] = {224, 256, 288, 320};
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int nscales = sizeof(scales)/sizeof(scales[0]);
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char **paths = (char **)list_to_array(plist);
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@@ -548,9 +448,9 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
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image im = load_image_color(paths[i], 0, 0);
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for(j = 0; j < nscales; ++j){
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image r = resize_min(im, scales[j]);
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resize_network(&net, r.w, r.h);
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resize_network(net, r.w, r.h);
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float *p = network_predict(net, r.data);
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if(net.hierarchy) hierarchy_predictions(p, net.outputs, net.hierarchy, 1 , 1);
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if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1 , 1);
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axpy_cpu(classes, 1, p, 1, pred, 1);
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flip_image(r);
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p = network_predict(net, r.data);
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@@ -571,11 +471,8 @@ void validate_classifier_multi(char *datacfg, char *filename, char *weightfile)
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void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int layer_num)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
|
||||
set_batch_network(&net, 1);
|
||||
network *net = load_network(cfgfile, weightfile, 0);
|
||||
set_batch_network(net, 1);
|
||||
srand(2222222);
|
||||
|
||||
list *options = read_data_cfg(datacfg);
|
||||
@@ -616,7 +513,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena
|
||||
time=clock();
|
||||
float *predictions = network_predict(net, X);
|
||||
|
||||
layer l = net.layers[layer_num];
|
||||
layer l = net->layers[layer_num];
|
||||
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]);
|
||||
}
|
||||
@@ -652,11 +549,8 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena
|
||||
|
||||
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
|
||||
{
|
||||
network net = parse_network_cfg(cfgfile);
|
||||
if(weightfile){
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
set_batch_network(&net, 1);
|
||||
network *net = load_network(cfgfile, weightfile, 0);
|
||||
set_batch_network(net, 1);
|
||||
srand(2222222);
|
||||
|
||||
list *options = read_data_cfg(datacfg);
|
||||
@@ -682,19 +576,19 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
|
||||
strtok(input, "\n");
|
||||
}
|
||||
image im = load_image_color(input, 0, 0);
|
||||
image r = letterbox_image(im, net.w, net.h);
|
||||
//resize_network(&net, r.w, r.h);
|
||||
image r = letterbox_image(im, net->w, net->h);
|
||||
//resize_network(net, r.w, r.h);
|
||||
//printf("%d %d\n", r.w, r.h);
|
||||
|
||||
float *X = r.data;
|
||||
time=clock();
|
||||
float *predictions = network_predict(net, X);
|
||||
if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1, 1);
|
||||
top_k(predictions, net.outputs, top, indexes);
|
||||
if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
|
||||
top_k(predictions, net->outputs, top, indexes);
|
||||
fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time));
|
||||
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");
|
||||
//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]);
|
||||
printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
|
||||
}
|
||||
@@ -708,11 +602,8 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
|
||||
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);
|
||||
}
|
||||
network *net = load_network(filename, weightfile, 0);
|
||||
set_batch_network(net, 1);
|
||||
srand(time(0));
|
||||
|
||||
list *options = read_data_cfg(datacfg);
|
||||
@@ -730,8 +621,8 @@ void label_classifier(char *datacfg, char *filename, char *weightfile)
|
||||
|
||||
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);
|
||||
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);
|
||||
@@ -747,10 +638,7 @@ void label_classifier(char *datacfg, char *filename, char *weightfile)
|
||||
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);
|
||||
}
|
||||
network *net = load_network(cfgfile, weightfile, 0);
|
||||
srand(time(0));
|
||||
|
||||
list *options = read_data_cfg(datacfg);
|
||||
@@ -769,18 +657,18 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_
|
||||
data val, buffer;
|
||||
|
||||
load_args args = {0};
|
||||
args.w = net.w;
|
||||
args.h = net.h;
|
||||
args.w = net->w;
|
||||
args.h = net->h;
|
||||
args.paths = paths;
|
||||
args.classes = classes;
|
||||
args.n = net.batch;
|
||||
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){
|
||||
for(curr = net->batch; curr < m; curr += net->batch){
|
||||
time=clock();
|
||||
|
||||
pthread_join(load_thread, 0);
|
||||
@@ -788,7 +676,7 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_
|
||||
|
||||
if(curr < m){
|
||||
args.paths = paths + curr;
|
||||
if (curr + net.batch > m) args.n = m - 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));
|
||||
@@ -798,11 +686,11 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_
|
||||
|
||||
int i, j;
|
||||
if (target_layer >= 0){
|
||||
//layer l = net.layers[target_layer];
|
||||
//layer l = net->layers[target_layer];
|
||||
}
|
||||
|
||||
for(i = 0; i < pred.rows; ++i){
|
||||
printf("%s", paths[curr-net.batch+i]);
|
||||
printf("%s", paths[curr-net->batch+i]);
|
||||
for(j = 0; j < pred.cols; ++j){
|
||||
printf("\t%g", pred.vals[i][j]);
|
||||
}
|
||||
@@ -824,11 +712,8 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
|
||||
float roll = .2;
|
||||
|
||||
printf("Classifier Demo\n");
|
||||
network net = parse_network_cfg(cfgfile);
|
||||
if(weightfile){
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
set_batch_network(&net, 1);
|
||||
network *net = load_network(cfgfile, weightfile, 0);
|
||||
set_batch_network(net, 1);
|
||||
list *options = read_data_cfg(datacfg);
|
||||
|
||||
srand(2222222);
|
||||
@@ -862,7 +747,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
|
||||
|
||||
image in = get_image_from_stream(cap);
|
||||
if(!in.data) break;
|
||||
image in_s = resize_image(in, net.w, net.h);
|
||||
image in_s = resize_image(in, net->w, net->h);
|
||||
|
||||
image out = in;
|
||||
int x1 = out.w / 20;
|
||||
@@ -956,11 +841,8 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
|
||||
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);
|
||||
network *net = load_network(cfgfile, weightfile, 0);
|
||||
set_batch_network(net, 1);
|
||||
list *options = read_data_cfg(datacfg);
|
||||
|
||||
srand(2222222);
|
||||
@@ -990,7 +872,7 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
|
||||
gettimeofday(&tval_before, NULL);
|
||||
|
||||
image in = get_image_from_stream(cap);
|
||||
image in_s = resize_image(in, net.w, net.h);
|
||||
image in_s = resize_image(in, net->w, net->h);
|
||||
show_image(in, "Threat Detection");
|
||||
|
||||
float *predictions = network_predict(net, in_s.data);
|
||||
@@ -1033,11 +915,8 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
|
||||
{
|
||||
#ifdef OPENCV
|
||||
printf("Classifier Demo\n");
|
||||
network net = parse_network_cfg(cfgfile);
|
||||
if(weightfile){
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
set_batch_network(&net, 1);
|
||||
network *net = load_network(cfgfile, weightfile, 0);
|
||||
set_batch_network(net, 1);
|
||||
list *options = read_data_cfg(datacfg);
|
||||
|
||||
srand(2222222);
|
||||
@@ -1067,11 +946,11 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
|
||||
gettimeofday(&tval_before, NULL);
|
||||
|
||||
image in = get_image_from_stream(cap);
|
||||
image in_s = resize_image(in, net.w, net.h);
|
||||
image in_s = resize_image(in, net->w, net->h);
|
||||
show_image(in, "Classifier");
|
||||
|
||||
float *predictions = network_predict(net, in_s.data);
|
||||
if(net.hierarchy) hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1, 1);
|
||||
if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
|
||||
top_predictions(net, top, indexes);
|
||||
|
||||
printf("\033[2J");
|
||||
|
||||
Reference in New Issue
Block a user