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WHOOOPS! 🔥 🔥 🔥
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Joseph Redmon
parent
cd5d393b46
commit
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326
examples/attention.c
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326
examples/attention.c
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#include "darknet.h"
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#include <sys/time.h>
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#include <assert.h>
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void train_attention(char *datacfg, char *cfgfile, char *weightfile, char *cfgfile2, char *weightfile2, int *gpus, int ngpus, int clear)
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{
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int i;
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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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printf("%d\n", ngpus);
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network **attnets = calloc(ngpus, sizeof(network*));
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network **clsnets = calloc(ngpus, sizeof(network*));
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srand(time(0));
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int seed = rand();
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for(i = 0; i < ngpus; ++i){
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srand(seed);
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#ifdef GPU
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cuda_set_device(gpus[i]);
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#endif
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attnets[i] = load_network(cfgfile, weightfile, clear);
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attnets[i]->learning_rate *= ngpus;
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clsnets[i] = load_network(cfgfile2, weightfile2, clear);
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clsnets[i]->learning_rate *= ngpus;
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}
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srand(time(0));
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network *net = attnets[0];
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//network *clsnet = clsnets[0];
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int imgs = net->batch * net->subdivisions * ngpus;
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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list *options = read_data_cfg(datacfg);
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char *backup_directory = option_find_str(options, "backup", "/backup/");
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char *label_list = option_find_str(options, "labels", "data/labels.list");
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char *train_list = option_find_str(options, "train", "data/train.list");
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int classes = option_find_int(options, "classes", 2);
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char **labels = get_labels(label_list);
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list *plist = get_paths(train_list);
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char **paths = (char **)list_to_array(plist);
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printf("%d\n", plist->size);
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int N = plist->size;
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double time;
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load_args args = {0};
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args.w = 4*net->w;
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args.h = 4*net->h;
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args.size = 4*net->w;
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args.threads = 32;
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args.hierarchy = net->hierarchy;
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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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args.angle = net->angle;
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args.aspect = net->aspect;
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args.exposure = net->exposure;
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args.saturation = net->saturation;
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args.hue = net->hue;
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args.paths = paths;
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args.classes = classes;
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args.n = imgs;
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args.m = N;
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args.labels = labels;
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args.type = CLASSIFICATION_DATA;
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data train;
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data buffer;
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pthread_t load_thread;
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args.d = &buffer;
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load_thread = load_data(args);
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int epoch = (*net->seen)/N;
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while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
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time = what_time_is_it_now();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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data resized = resize_data(train, net->w, net->h);
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printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
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time = what_time_is_it_now();
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float loss = 0;
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#ifdef GPU
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if(ngpus == 1){
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loss = train_network(net, train);
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} else {
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loss = train_networks(attnets, ngpus, train, 4);
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}
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#else
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loss = train_network(net, train);
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#endif
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free_data(resized);
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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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free_data(train);
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if(*net->seen/N > epoch){
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epoch = *net->seen/N;
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
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save_weights(net, buff);
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}
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if(get_current_batch(net)%1000 == 0){
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char buff[256];
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sprintf(buff, "%s/%s.backup",backup_directory,base);
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save_weights(net, buff);
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}
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}
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char buff[256];
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sprintf(buff, "%s/%s.weights", backup_directory, base);
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save_weights(net, buff);
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pthread_join(load_thread, 0);
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free_network(net);
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free_ptrs((void**)labels, classes);
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free_ptrs((void**)paths, plist->size);
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free_list(plist);
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free(base);
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}
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void validate_attention_single(char *datacfg, char *filename, char *weightfile)
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{
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int i, j;
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network *net = load_network(filename, weightfile, 0);
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set_batch_network(net, 1);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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char *label_list = option_find_str(options, "labels", "data/labels.list");
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char *leaf_list = option_find_str(options, "leaves", 0);
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if(leaf_list) change_leaves(net->hierarchy, leaf_list);
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char *valid_list = option_find_str(options, "valid", "data/train.list");
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int classes = option_find_int(options, "classes", 2);
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int topk = option_find_int(options, "top", 1);
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char **labels = get_labels(label_list);
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list *plist = get_paths(valid_list);
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char **paths = (char **)list_to_array(plist);
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int m = plist->size;
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free_list(plist);
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float avg_acc = 0;
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float avg_topk = 0;
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int *indexes = calloc(topk, sizeof(int));
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for(i = 0; i < m; ++i){
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int class = -1;
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char *path = paths[i];
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for(j = 0; j < classes; ++j){
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if(strstr(path, labels[j])){
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class = j;
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break;
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}
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}
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image im = load_image_color(paths[i], 0, 0);
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image resized = resize_min(im, net->w);
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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(resized.data != im.data) free_image(resized);
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free_image(im);
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free_image(crop);
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top_k(pred, classes, topk, indexes);
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if(indexes[0] == class) avg_acc += 1;
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for(j = 0; j < topk; ++j){
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if(indexes[j] == class) avg_topk += 1;
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}
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printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
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}
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}
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void validate_attention_multi(char *datacfg, char *filename, char *weightfile)
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{
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int i, j;
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network *net = load_network(filename, weightfile, 0);
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set_batch_network(net, 1);
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srand(time(0));
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list *options = read_data_cfg(datacfg);
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char *label_list = option_find_str(options, "labels", "data/labels.list");
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char *valid_list = option_find_str(options, "valid", "data/train.list");
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int classes = option_find_int(options, "classes", 2);
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int topk = option_find_int(options, "top", 1);
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char **labels = get_labels(label_list);
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list *plist = get_paths(valid_list);
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int scales[] = {224, 288, 320, 352, 384};
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int nscales = sizeof(scales)/sizeof(scales[0]);
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char **paths = (char **)list_to_array(plist);
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int m = plist->size;
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free_list(plist);
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float avg_acc = 0;
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float avg_topk = 0;
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int *indexes = calloc(topk, sizeof(int));
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for(i = 0; i < m; ++i){
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int class = -1;
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char *path = paths[i];
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for(j = 0; j < classes; ++j){
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if(strstr(path, labels[j])){
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class = j;
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break;
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}
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}
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float *pred = calloc(classes, sizeof(float));
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image im = load_image_color(paths[i], 0, 0);
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for(j = 0; j < nscales; ++j){
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image r = resize_min(im, scales[j]);
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resize_network(net, r.w, r.h);
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float *p = network_predict(net, r.data);
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if(net->hierarchy) hierarchy_predictions(p, net->outputs, net->hierarchy, 1 , 1);
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axpy_cpu(classes, 1, p, 1, pred, 1);
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flip_image(r);
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p = network_predict(net, r.data);
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axpy_cpu(classes, 1, p, 1, pred, 1);
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if(r.data != im.data) free_image(r);
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}
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free_image(im);
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top_k(pred, classes, topk, indexes);
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free(pred);
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if(indexes[0] == class) avg_acc += 1;
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for(j = 0; j < topk; ++j){
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if(indexes[j] == class) avg_topk += 1;
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}
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printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
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}
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}
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void predict_attention(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
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{
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network *net = load_network(cfgfile, weightfile, 0);
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set_batch_network(net, 1);
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srand(2222222);
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list *options = read_data_cfg(datacfg);
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char *name_list = option_find_str(options, "names", 0);
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if(!name_list) name_list = option_find_str(options, "labels", "data/labels.list");
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if(top == 0) top = option_find_int(options, "top", 1);
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int i = 0;
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char **names = get_labels(name_list);
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clock_t time;
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int *indexes = calloc(top, sizeof(int));
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char buff[256];
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char *input = buff;
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while(1){
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if(filename){
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strncpy(input, filename, 256);
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}else{
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printf("Enter Image Path: ");
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fflush(stdout);
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input = fgets(input, 256, stdin);
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if(!input) return;
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strtok(input, "\n");
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}
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image im = load_image_color(input, 0, 0);
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image r = letterbox_image(im, net->w, net->h);
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//resize_network(&net, r.w, r.h);
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//printf("%d %d\n", r.w, r.h);
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float *X = r.data;
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time=clock();
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float *predictions = network_predict(net, X);
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if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
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top_k(predictions, net->outputs, top, indexes);
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fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time));
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for(i = 0; i < top; ++i){
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int index = indexes[i];
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//if(net->hierarchy) printf("%d, %s: %f, parent: %s \n",index, names[index], predictions[index], (net->hierarchy->parent[index] >= 0) ? names[net->hierarchy->parent[index]] : "Root");
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//else printf("%s: %f\n",names[index], predictions[index]);
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printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
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}
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if(r.data != im.data) free_image(r);
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free_image(im);
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if (filename) break;
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}
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}
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void run_attention(int argc, char **argv)
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{
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if(argc < 4){
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fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
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return;
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}
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char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
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int ngpus;
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int *gpus = read_intlist(gpu_list, &ngpus, gpu_index);
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int top = find_int_arg(argc, argv, "-t", 0);
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int clear = find_arg(argc, argv, "-clear");
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char *data = argv[3];
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char *cfg = argv[4];
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char *weights = (argc > 5) ? argv[5] : 0;
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char *filename = (argc > 6) ? argv[6]: 0;
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char *layer_s = (argc > 7) ? argv[7]: 0;
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if(0==strcmp(argv[2], "predict")) predict_attention(data, cfg, weights, filename, top);
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else if(0==strcmp(argv[2], "train")) train_attention(data, cfg, weights, filename, layer_s, gpus, ngpus, clear);
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else if(0==strcmp(argv[2], "valid")) validate_attention_single(data, cfg, weights);
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else if(0==strcmp(argv[2], "validmulti")) validate_attention_multi(data, cfg, weights);
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}
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