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examples/attention.c

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+#include "darknet.h"
+
+#include <sys/time.h>
+#include <assert.h>
+
+void train_attention(char *datacfg, char *cfgfile, char *weightfile, char *cfgfile2, char *weightfile2, int *gpus, int ngpus, int clear)
+{
+    int i;
+
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **attnets = calloc(ngpus, sizeof(network*));
+    network **clsnets = calloc(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
+        attnets[i] = load_network(cfgfile, weightfile, clear);
+        attnets[i]->learning_rate *= ngpus;
+        clsnets[i] = load_network(cfgfile2, weightfile2, clear);
+        clsnets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = attnets[0];
+    //network *clsnet = clsnets[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);
+
+    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;
+    double time;
+
+    load_args args = {0};
+    args.w = 4*net->w;
+    args.h = 4*net->h;
+    args.size = 4*net->w;
+    args.threads = 32;
+    args.hierarchy = net->hierarchy;
+
+    args.min = net->min_ratio*net->w;
+    args.max = net->max_ratio*net->w;
+    args.angle = net->angle;
+    args.aspect = net->aspect;
+    args.exposure = net->exposure;
+    args.saturation = net->saturation;
+    args.hue = net->hue;
+
+    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 = what_time_is_it_now();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+        data resized = resize_data(train, net->w, net->h);
+
+        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
+        time = what_time_is_it_now();
+
+        float loss = 0;
+#ifdef GPU
+        if(ngpus == 1){
+            loss = train_network(net, train);
+        } else {
+            loss = train_networks(attnets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        free_data(resized);
+        if(avg_loss == -1) avg_loss = loss;
+        avg_loss = avg_loss*.9 + loss*.1;
+        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);
+        free_data(train);
+        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)%1000 == 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);
+    pthread_join(load_thread, 0);
+
+    free_network(net);
+    free_ptrs((void**)labels, classes);
+    free_ptrs((void**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void validate_attention_single(char *datacfg, char *filename, char *weightfile)
+{
+    int i, j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    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);
+
+    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 = calloc(topk, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        int class = -1;
+        char *path = paths[i];
+        for(j = 0; j < classes; ++j){
+            if(strstr(path, labels[j])){
+                class = 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, 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) avg_acc += 1;
+        for(j = 0; j < topk; ++j){
+            if(indexes[j] == class) avg_topk += 1;
+        }
+
+        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+    }
+}
+
+void validate_attention_multi(char *datacfg, char *filename, char *weightfile)
+{
+    int i, j;
+    network *net = load_network(filename, weightfile, 0);
+    set_batch_network(net, 1);
+    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);
+
+    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 = calloc(topk, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        int class = -1;
+        char *path = paths[i];
+        for(j = 0; j < classes; ++j){
+            if(strstr(path, labels[j])){
+                class = j;
+                break;
+            }
+        }
+        float *pred = calloc(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 , 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) avg_acc += 1;
+        for(j = 0; j < topk; ++j){
+            if(indexes[j] == class) avg_topk += 1;
+        }
+
+        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc/(i+1), topk, avg_topk/(i+1));
+    }
+}
+
+void predict_attention(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
+{
+    network *net = load_network(cfgfile, weightfile, 0);
+    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");
+    if(top == 0) top = option_find_int(options, "top", 1);
+
+    int i = 0;
+    char **names = get_labels(name_list);
+    clock_t time;
+    int *indexes = calloc(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) return;
+            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);
+        //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);
+        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");
+            //else printf("%s: %f\n",names[index], predictions[index]);
+            printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
+        }
+        if(r.data != im.data) free_image(r);
+        free_image(im);
+        if (filename) break;
+    }
+}
+
+
+void run_attention(int argc, char **argv)
+{
+    if(argc < 4){
+        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+        return;
+    }
+
+    char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
+    int ngpus;
+    int *gpus = read_intlist(gpu_list, &ngpus, gpu_index);
+
+
+    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;
+    if(0==strcmp(argv[2], "predict")) predict_attention(data, cfg, weights, filename, top);
+    else if(0==strcmp(argv[2], "train")) train_attention(data, cfg, weights, filename, layer_s, gpus, ngpus, clear);
+    else if(0==strcmp(argv[2], "valid")) validate_attention_single(data, cfg, weights);
+    else if(0==strcmp(argv[2], "validmulti")) validate_attention_multi(data, cfg, weights);
+}
+
+