this'll teach me to mess with maxpooling

Makefile

@@ -57,8 +57,8 @@ CFLAGS+= -DCUDNN
 LDFLAGS+= -lcudnn
 endif
 
-OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o  lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o
-EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o darknet.o
+EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o instance-segmenter.o darknet.o
 ifeq ($(GPU), 1) 
 LDFLAGS+= -lstdc++ 
 OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o avgpool_layer_kernels.o

examples/art.c

@@ -24,7 +24,6 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index)
     while(1){
         image in = get_image_from_stream(cap);
         image in_s = resize_image(in, net->w, net->h);
-        show_image(in, window);
 
         float *p = network_predict(net, in_s.data);
 
@@ -45,10 +44,9 @@ void demo_art(char *cfgfile, char *weightfile, int cam_index)
         }
         printf("]\n");
 
+        show_image(in, window, 1);
         free_image(in_s);
         free_image(in);
-
-        cvWaitKey(1);
     }
 #endif
 }

examples/classifier.c

@@ -645,6 +645,45 @@ void label_classifier(char *datacfg, char *filename, char *weightfile)
     }
 }
 
+void csv_classifier(char *datacfg, char *cfgfile, char *weightfile)
+{
+    int i,j;
+    network *net = load_network(cfgfile, weightfile, 0);
+    srand(time(0));
+
+    list *options = read_data_cfg(datacfg);
+
+    char *test_list = option_find_str(options, "test", "data/test.list");
+    int top = option_find_int(options, "top", 1);
+
+    list *plist = get_paths(test_list);
+
+    char **paths = (char **)list_to_array(plist);
+    int m = plist->size;
+    free_list(plist);
+    int *indexes = calloc(top, sizeof(int));
+
+    for(i = 0; i < m; ++i){
+        double time = what_time_is_it_now();
+        char *path = paths[i];
+        image im = load_image_color(path, 0, 0);
+        image r = letterbox_image(im, net->w, net->h);
+        float *predictions = network_predict(net, r.data);
+        if(net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
+        top_k(predictions, net->outputs, top, indexes);
+
+        printf("%s", path);
+        for(j = 0; j < top; ++j){
+            printf("\t%d", indexes[j]);
+        }
+        printf("\n");
+
+        free_image(im);
+        free_image(r);
+
+        fprintf(stderr, "%lf seconds, %d images, %d total\n", what_time_is_it_now() - time, i+1, m);
+    }
+}
 
 void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
 {
@@ -869,8 +908,7 @@ void threat_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_i
         }
 
         if(1){
-            show_image(out, "Threat");
+            show_image(out, "Threat", 10);
-            cvWaitKey(10);
         }
         free_image(in_s);
         free_image(in);
@@ -922,7 +960,6 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
 
         image in = get_image_from_stream(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);
@@ -947,11 +984,10 @@ void gun_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
             }
         }
 
+        show_image(in, "Threat Detection", 10);
         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);
@@ -1036,12 +1072,10 @@ void demo_classifier(char *datacfg, char *cfgfile, char *weightfile, int cam_ind
             free_image(label);
         }
 
-        show_image(in, base);
+        show_image(in, base, 10);
         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);
@@ -1080,6 +1114,7 @@ void run_classifier(int argc, char **argv)
     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], "csv")) csv_classifier(data, cfg, weights);
     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);
     else if(0==strcmp(argv[2], "validmulti")) validate_classifier_multi(data, cfg, weights);

examples/coco.c

@@ -325,14 +325,10 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
 
         draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80);
         save_image(im, "prediction");
-        show_image(im, "predictions");
+        show_image(im, "predictions", 0);
         free_detections(dets, nboxes);
         free_image(im);
         free_image(sized);
-#ifdef OPENCV
-        cvWaitKey(0);
-        cvDestroyAllWindows();
-#endif
         if (filename) break;
     }
 }

examples/darknet.c

@@ -14,6 +14,7 @@ extern void run_nightmare(int argc, char **argv);
 extern void run_classifier(int argc, char **argv);
 extern void run_regressor(int argc, char **argv);
 extern void run_segmenter(int argc, char **argv);
+extern void run_isegmenter(int argc, char **argv);
 extern void run_char_rnn(int argc, char **argv);
 extern void run_tag(int argc, char **argv);
 extern void run_cifar(int argc, char **argv);
@@ -452,6 +453,8 @@ int main(int argc, char **argv)
         run_classifier(argc, argv);
     } else if (0 == strcmp(argv[1], "regressor")){
         run_regressor(argc, argv);
+    } else if (0 == strcmp(argv[1], "isegmenter")){
+        run_isegmenter(argc, argv);
     } else if (0 == strcmp(argv[1], "segmenter")){
         run_segmenter(argc, argv);
     } else if (0 == strcmp(argv[1], "art")){

examples/detector.c

@@ -613,9 +613,7 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
             if(fullscreen){
                 cvSetWindowProperty("predictions", CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
             }
-            show_image(im, "predictions");
+            show_image(im, "predictions", 0);
-            cvWaitKey(0);
-            cvDestroyAllWindows();
 #endif
         }
 

examples/instance-segmenter.c

@@ -0,0 +1,265 @@
+#include "darknet.h"
+#include <sys/time.h>
+#include <assert.h>
+
+void train_isegmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int display)
+{
+    int i;
+
+    float avg_loss = -1;
+    char *base = basecfg(cfgfile);
+    printf("%s\n", base);
+    printf("%d\n", ngpus);
+    network **nets = 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
+        nets[i] = load_network(cfgfile, weightfile, clear);
+        nets[i]->learning_rate *= ngpus;
+    }
+    srand(time(0));
+    network *net = nets[0];
+    image pred = get_network_image(net);
+
+    int div = net->w/pred.w;
+    assert(pred.w * div == net->w);
+    assert(pred.h * div == net->h);
+
+    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 *train_list = option_find_str(options, "train", "data/train.list");
+
+    list *plist = get_paths(train_list);
+    char **paths = (char **)list_to_array(plist);
+    printf("%d\n", plist->size);
+    int N = plist->size;
+
+    load_args args = {0};
+    args.w = net->w;
+    args.h = net->h;
+    args.threads = 32;
+    args.scale = div;
+    args.num_boxes = 90;
+
+    args.min = net->min_crop;
+    args.max = net->max_crop;
+    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.classes = 80;
+
+    args.paths = paths;
+    args.n = imgs;
+    args.m = N;
+    args.type = ISEG_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){
+        double time = what_time_is_it_now();
+
+        pthread_join(load_thread, 0);
+        train = buffer;
+        load_thread = load_data(args);
+
+        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(nets, ngpus, train, 4);
+        }
+#else
+        loss = train_network(net, train);
+#endif
+        if(display){
+            image tr = float_to_image(net->w/div, net->h/div, 80, train.y.vals[net->batch*(net->subdivisions-1)]);
+            image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]);
+            pred.c = 80;
+            image mask = mask_to_rgb(tr);
+            image prmask = mask_to_rgb(pred);
+            show_image(im, "input", 1);
+            show_image(prmask, "pred", 1);
+            show_image(mask, "truth", 100);
+            free_image(mask);
+            free_image(prmask);
+        }
+        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)%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**)paths, plist->size);
+    free_list(plist);
+    free(base);
+}
+
+void predict_isegmenter(char *datafile, char *cfg, char *weights, char *filename)
+{
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+    srand(2222222);
+
+    clock_t time;
+    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 sized = letterbox_image(im, net->w, net->h);
+
+        float *X = sized.data;
+        time=clock();
+        float *predictions = network_predict(net, X);
+        image pred = get_network_image(net);
+        image prmask = mask_to_rgb(pred);
+        printf("Predicted: %f\n", predictions[0]);
+        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        show_image(sized, "orig", 1);
+        show_image(prmask, "pred", 0);
+        free_image(im);
+        free_image(sized);
+        free_image(prmask);
+        if (filename) break;
+    }
+}
+
+
+void demo_isegmenter(char *datacfg, char *cfg, char *weights, int cam_index, const char *filename)
+{
+#ifdef OPENCV
+    printf("Classifier Demo\n");
+    network *net = load_network(cfg, weights, 0);
+    set_batch_network(net, 1);
+
+    srand(2222222);
+    CvCapture * cap;
+
+    if(filename){
+        cap = cvCaptureFromFile(filename);
+    }else{
+        cap = cvCaptureFromCAM(cam_index);
+    }
+
+    if(!cap) error("Couldn't connect to webcam.\n");
+    cvNamedWindow("Segmenter", CV_WINDOW_NORMAL); 
+    cvResizeWindow("Segmenter", 512, 512);
+    float fps = 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 = letterbox_image(in, net->w, net->h);
+
+        network_predict(net, in_s.data);
+
+        printf("\033[2J");
+        printf("\033[1;1H");
+        printf("\nFPS:%.0f\n",fps);
+
+        image pred = get_network_image(net);
+        image prmask = mask_to_rgb(pred);
+        show_image(prmask, "Segmenter", 10);
+        
+        free_image(in_s);
+        free_image(in);
+        free_image(prmask);
+
+        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 run_isegmenter(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 *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 = calloc(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 cam_index = find_int_arg(argc, argv, "-c", 0);
+    int clear = find_arg(argc, argv, "-clear");
+    int display = find_arg(argc, argv, "-display");
+    char *data = argv[3];
+    char *cfg = argv[4];
+    char *weights = (argc > 5) ? argv[5] : 0;
+    char *filename = (argc > 6) ? argv[6]: 0;
+    if(0==strcmp(argv[2], "test")) predict_isegmenter(data, cfg, weights, filename);
+    else if(0==strcmp(argv[2], "train")) train_isegmenter(data, cfg, weights, gpus, ngpus, clear, display);
+    else if(0==strcmp(argv[2], "demo")) demo_isegmenter(data, cfg, weights, cam_index, filename);
+}
+
+

examples/lsd.c

@@ -460,13 +460,9 @@ void inter_dcgan(char *cfgfile, char *weightfile)
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
         //char buff[256];
         sprintf(buff, "out%05d", c);
-        show_image(out, "out");
         save_image(out, "out");
         save_image(out, buff);
-#ifdef OPENCV
+        show_image(out, "out", 0);
-        //cvWaitKey(0);
-#endif
-
     }
 }
 
@@ -499,11 +495,8 @@ void test_dcgan(char *cfgfile, char *weightfile)
         //yuv_to_rgb(out);
         normalize_image(out);
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
-        show_image(out, "out");
         save_image(out, "out");
-#ifdef OPENCV
+        show_image(out, "out", 0);
-        cvWaitKey(0);
-#endif
 
         free_image(im);
     }
@@ -639,11 +632,10 @@ void train_prog(char *cfg, char *weight, char *acfg, char *aweight, int clear, i
         if(display){
             image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
             image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
-            show_image(im, "gen");
+            show_image(im, "gen", 1);
-            show_image(im2, "train");
+            show_image(im2, "train", 1);
             save_image(im, "gen");
             save_image(im2, "train");
-            cvWaitKey(1);
         }
 #endif
 
@@ -826,11 +818,10 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
         if(display){
             image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
             image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
-            show_image(im, "gen");
+            show_image(im, "gen", 1);
-            show_image(im2, "train");
+            show_image(im2, "train", 1);
             save_image(im, "gen");
             save_image(im2, "train");
-            cvWaitKey(1);
         }
 #endif
 
@@ -1010,9 +1001,8 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
         if(display){
             image im = float_to_image(anet->w, anet->h, anet->c, gray.X.vals[0]);
             image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
-            show_image(im, "gen");
+            show_image(im, "gen", 1);
-            show_image(im2, "train");
+            show_image(im2, "train", 1);
-            cvWaitKey(1);
         }
 #endif
         free_data(merge);
@@ -1342,12 +1332,9 @@ void test_lsd(char *cfg, char *weights, char *filename, int gray)
         //yuv_to_rgb(out);
         constrain_image(out);
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
-        show_image(out, "out");
-        show_image(crop, "crop");
         save_image(out, "out");
-#ifdef OPENCV
+        show_image(out, "out", 1);
-        cvWaitKey(0);
+        show_image(crop, "crop", 0);
-#endif
 
         free_image(im);
         free_image(resized);

examples/nightmare.c

@@ -376,10 +376,7 @@ void run_nightmare(int argc, char **argv)
             if(reconstruct){
                 reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
                 //if ((n+1)%30 == 0) rate *= .5;
-                show_image(im, "reconstruction");
+                show_image(im, "reconstruction", 10);
-#ifdef OPENCV
-                cvWaitKey(10);
-#endif
             }else{
                 int layer = max_layer + rand()%range - range/2;
                 int octave = rand()%octaves;
@@ -400,8 +397,7 @@ void run_nightmare(int argc, char **argv)
         }
         printf("%d %s\n", e, buff);
         save_image(im, buff);
-        //show_image(im, buff);
+        //show_image(im, buff, 0);
-        //cvWaitKey(0);
 
         if(rotate){
             image rot = rotate_image(im, rotate);

examples/regressor.c

@@ -179,7 +179,6 @@ void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
         image in = get_image_from_stream(cap);
         image crop = center_crop_image(in, net->w, net->h);
         grayscale_image_3c(crop);
-        show_image(crop, "Regressor");
 
         float *predictions = network_predict(net, crop.data);
 
@@ -192,11 +191,10 @@ void demo_regressor(char *datacfg, char *cfgfile, char *weightfile, int cam_inde
             printf("%s: %f\n", names[i], predictions[i]);
         }
 
+        show_image(crop, "Regressor", 10);
         free_image(in);
         free_image(crop);
 
-        cvWaitKey(10);
-
         gettimeofday(&tval_after, NULL);
         timersub(&tval_after, &tval_before, &tval_result);
         float curr = 1000000.f/((long int)tval_result.tv_usec);

examples/segmenter.c

@@ -42,7 +42,6 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     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;
@@ -73,14 +72,14 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
 
     int epoch = (*net->seen)/N;
     while(get_current_batch(net) < net->max_batches || net->max_batches == 0){
-        time=clock();
+        double time = what_time_is_it_now();
 
         pthread_join(load_thread, 0);
         train = buffer;
         load_thread = load_data(args);
 
-        printf("Loaded: %lf seconds\n", sec(clock()-time));
+        printf("Loaded: %lf seconds\n", what_time_is_it_now()-time);
-        time=clock();
+        time = what_time_is_it_now();
 
         float loss = 0;
 #ifdef GPU
@@ -97,18 +96,15 @@ void train_segmenter(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
             image im = float_to_image(net->w, net->h, net->c, train.X.vals[net->batch*(net->subdivisions-1)]);
             image mask = mask_to_rgb(tr);
             image prmask = mask_to_rgb(pred);
-            show_image(im, "input");
+            show_image(im, "input", 1);
-            show_image(prmask, "pred");
+            show_image(prmask, "pred", 1);
-            show_image(mask, "truth");
+            show_image(mask, "truth", 100);
-#ifdef OPENCV
-            cvWaitKey(100);
-#endif
             free_image(mask);
             free_image(prmask);
         }
         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), sec(clock()-time), *net->seen);
+        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;
@@ -159,13 +155,10 @@ void predict_segmenter(char *datafile, char *cfg, char *weights, char *filename)
         float *predictions = network_predict(net, X);
         image pred = get_network_image(net);
         image prmask = mask_to_rgb(pred);
-        show_image(sized, "orig");
-        show_image(prmask, "pred");
-#ifdef OPENCV
-        cvWaitKey(0);
-#endif
         printf("Predicted: %f\n", predictions[0]);
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+        show_image(sized, "orig", 1);
+        show_image(prmask, "pred", 0);
         free_image(im);
         free_image(sized);
         free_image(prmask);
@@ -210,14 +203,12 @@ void demo_segmenter(char *datacfg, char *cfg, char *weights, int cam_index, cons
 
         image pred = get_network_image(net);
         image prmask = mask_to_rgb(pred);
-        show_image(prmask, "Segmenter");
+        show_image(prmask, "Segmenter", 10);
         
         free_image(in_s);
         free_image(in);
         free_image(prmask);
 
-        cvWaitKey(10);
-
         gettimeofday(&tval_after, NULL);
         timersub(&tval_after, &tval_before, &tval_result);
         float curr = 1000000.f/((long int)tval_result.tv_usec);

examples/super.c

@@ -93,7 +93,7 @@ void test_super(char *cfgfile, char *weightfile, char *filename)
         image out = get_network_image(net);
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
         save_image(out, "out");
-        show_image(out, "out");
+        show_image(out, "out", 0);
 
         free_image(im);
         if (filename) break;

examples/yolo.c

@@ -296,14 +296,10 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
 
         draw_detections(im, dets, l.side*l.side*l.n, thresh, voc_names, alphabet, 20);
         save_image(im, "predictions");
-        show_image(im, "predictions");
+        show_image(im, "predictions", 0);
         free_detections(dets, nboxes);
         free_image(im);
         free_image(sized);
-#ifdef OPENCV
-        cvWaitKey(0);
-        cvDestroyAllWindows();
-#endif
         if (filename) break;
     }
 }

include/darknet.h

@@ -86,6 +86,7 @@ typedef enum {
     XNOR,
     REGION,
     YOLO,
+    ISEG,
     REORG,
     UPSAMPLE,
     LOGXENT,
@@ -166,6 +167,7 @@ struct layer{
     float ratio;
     float learning_rate_scale;
     float clip;
+    int noloss;
     int softmax;
     int classes;
     int coords;
@@ -203,6 +205,7 @@ struct layer{
     int dontload;
     int dontsave;
     int dontloadscales;
+    int numload;
 
     float temperature;
     float probability;
@@ -213,6 +216,8 @@ struct layer{
     int   * input_layers;
     int   * input_sizes;
     int   * map;
+    int   * counts;
+    float ** sums;
     float * rand;
     float * cost;
     float * state;
@@ -540,7 +545,7 @@ typedef struct{
 } data;
 
 typedef enum {
-    CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA
+    CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA, ISEG_DATA
 } data_type;
 
 typedef struct load_args{
@@ -705,7 +710,7 @@ int resize_network(network *net, int w, int h);
 void free_matrix(matrix m);
 void test_resize(char *filename);
 void save_image(image p, const char *name);
-void show_image(image p, const char *name);
+int show_image(image p, const char *name, int ms);
 image copy_image(image p);
 void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b);
 float get_current_rate(network *net);

src/convolutional_layer.c

@@ -151,7 +151,7 @@ void cudnn_convolutional_setup(layer *l)
             l->convDesc,
             l->dstTensorDesc,
             CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-            4000000000,
+            2000000000,
             &l->fw_algo);
     cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
             l->weightDesc,
@@ -159,7 +159,7 @@ void cudnn_convolutional_setup(layer *l)
             l->convDesc,
             l->dsrcTensorDesc,
             CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
-            4000000000,
+            2000000000,
             &l->bd_algo);
     cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
             l->srcTensorDesc,
@@ -167,7 +167,7 @@ void cudnn_convolutional_setup(layer *l)
             l->convDesc,
             l->dweightDesc,
             CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
-            4000000000,
+            2000000000,
             &l->bf_algo);
 }
 #endif

src/data.c

@@ -361,6 +361,44 @@ box bound_image(image im)
 }
 
 void fill_truth_iseg(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh)
+{
+    char labelpath[4096];
+    find_replace(path, "images", "mask", labelpath);
+    find_replace(labelpath, "JPEGImages", "mask", labelpath);
+    find_replace(labelpath, ".jpg", ".txt", labelpath);
+    find_replace(labelpath, ".JPG", ".txt", labelpath);
+    find_replace(labelpath, ".JPEG", ".txt", labelpath);
+    FILE *file = fopen(labelpath, "r");
+    if(!file) file_error(labelpath);
+    char buff[32788];
+    int id;
+    int i = 0;
+    int j;
+    image part = make_image(w, h, 1);
+    while((fscanf(file, "%d %s", &id, buff) == 2) && i < num_boxes){
+        int n = 0;
+        int *rle = read_intlist(buff, &n, 0);
+        load_rle(part, rle, n);
+        image sized = rotate_crop_image(part, aug.rad, aug.scale, aug.w, aug.h, aug.dx, aug.dy, aug.aspect);
+        if(flip) flip_image(sized);
+
+        image mask = resize_image(sized, mw, mh);
+        truth[i*(mw*mh+1)] = id;
+        for(j = 0; j < mw*mh; ++j){
+            truth[i*(mw*mh + 1) + 1 + j] = mask.data[j];
+        }
+        ++i;
+
+        free_image(mask);
+        free_image(sized);
+        free(rle);
+    }
+    if(i < num_boxes) truth[i*(mw*mh+1)] = -1;
+    fclose(file);
+    free_image(part);
+}
+
+void fill_truth_mask(char *path, int num_boxes, float *truth, int classes, int w, int h, augment_args aug, int flip, int mw, int mh)
 {
     char labelpath[4096];
     find_replace(path, "images", "mask", labelpath);
@@ -743,7 +781,47 @@ data load_data_seg(int n, char **paths, int m, int w, int h, int classes, int mi
     return d;
 }
 
-data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int boxes, int coords, int min, int max, float angle, float aspect, float hue, float saturation, float exposure)
+data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int boxes, int div, int min, int max, float angle, float aspect, float hue, float saturation, float exposure)
+{
+    char **random_paths = get_random_paths(paths, n, m);
+    int i;
+    data d = {0};
+    d.shallow = 0;
+
+    d.X.rows = n;
+    d.X.vals = calloc(d.X.rows, sizeof(float*));
+    d.X.cols = h*w*3;
+
+    d.y = make_matrix(n, (((w/div)*(h/div))+1)*boxes);
+
+    for(i = 0; i < n; ++i){
+        image orig = load_image_color(random_paths[i], 0, 0);
+        augment_args a = random_augment_args(orig, angle, aspect, min, max, w, h);
+        image sized = rotate_crop_image(orig, a.rad, a.scale, a.w, a.h, a.dx, a.dy, a.aspect);
+
+        int flip = rand()%2;
+        if(flip) flip_image(sized);
+        random_distort_image(sized, hue, saturation, exposure);
+        d.X.vals[i] = sized.data;
+        //show_image(sized, "image");
+
+        fill_truth_iseg(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, w/div, h/div);
+
+        free_image(orig);
+
+        /*
+           image rgb = mask_to_rgb(sized_m, classes);
+           show_image(rgb, "part");
+           show_image(sized, "orig");
+           cvWaitKey(0);
+           free_image(rgb);
+         */
+    }
+    free(random_paths);
+    return d;
+}
+
+data load_data_mask(int n, char **paths, int m, int w, int h, int classes, int boxes, int coords, int min, int max, float angle, float aspect, float hue, float saturation, float exposure)
 {
     char **random_paths = get_random_paths(paths, n, m);
     int i;
@@ -767,7 +845,7 @@ data load_data_iseg(int n, char **paths, int m, int w, int h, int classes, int b
         d.X.vals[i] = sized.data;
         //show_image(sized, "image");
 
-        fill_truth_iseg(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, 14, 14);
+        fill_truth_mask(random_paths[i], boxes, d.y.vals[i], classes, orig.w, orig.h, a, flip, 14, 14);
 
         free_image(orig);
 
@@ -975,7 +1053,8 @@ data load_data_detection(int n, char **paths, int m, int w, int h, int boxes, in
         float dh = jitter * orig.h;
 
         float new_ar = (orig.w + rand_uniform(-dw, dw)) / (orig.h + rand_uniform(-dh, dh));
-        float scale = rand_uniform(.25, 2);
+        //float scale = rand_uniform(.25, 2);
+        float scale = 1;
 
         float nw, nh;
 
@@ -1025,8 +1104,10 @@ void *load_thread(void *ptr)
         *a.d = load_data_super(a.paths, a.n, a.m, a.w, a.h, a.scale);
     } else if (a.type == WRITING_DATA){
         *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.out_w, a.out_h);
+    } else if (a.type == ISEG_DATA){
+        *a.d = load_data_iseg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.scale, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
     } else if (a.type == INSTANCE_DATA){
-        *a.d = load_data_iseg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.coords, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
+        *a.d = load_data_mask(a.n, a.paths, a.m, a.w, a.h, a.classes, a.num_boxes, a.coords, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
     } else if (a.type == SEGMENTATION_DATA){
         *a.d = load_data_seg(a.n, a.paths, a.m, a.w, a.h, a.classes, a.min, a.max, a.angle, a.aspect, a.hue, a.saturation, a.exposure, a.scale);
     } else if (a.type == REGION_DATA){
@@ -1212,7 +1293,7 @@ data *tile_data(data orig, int divs, int size)
 {
     data *ds = calloc(divs*divs, sizeof(data));
     int i, j;
-    #pragma omp parallel for
+#pragma omp parallel for
     for(i = 0; i < divs*divs; ++i){
         data d;
         d.shallow = 0;
@@ -1223,7 +1304,7 @@ data *tile_data(data orig, int divs, int size)
         d.X.vals = calloc(d.X.rows, sizeof(float*));
 
         d.y = copy_matrix(orig.y);
-        #pragma omp parallel for
+#pragma omp parallel for
         for(j = 0; j < orig.X.rows; ++j){
             int x = (i%divs) * orig.w / divs - (d.w - orig.w/divs)/2;
             int y = (i/divs) * orig.h / divs - (d.h - orig.h/divs)/2;
@@ -1247,7 +1328,7 @@ data resize_data(data orig, int w, int h)
     d.X.vals = calloc(d.X.rows, sizeof(float*));
 
     d.y = copy_matrix(orig.y);
-    #pragma omp parallel for
+#pragma omp parallel for
     for(i = 0; i < orig.X.rows; ++i){
         image im = float_to_image(orig.w, orig.h, 3, orig.X.vals[i]);
         d.X.vals[i] = resize_image(im, w, h).data;

src/image.c

@@ -572,7 +572,7 @@ void show_image_cv(image p, const char *name, IplImage *disp)
 }
 #endif
 
-void show_image(image p, const char *name)
+int show_image(image p, const char *name, int ms)
 {
 #ifdef OPENCV
     IplImage *disp = cvCreateImage(cvSize(p.w,p.h), IPL_DEPTH_8U, p.c);
@@ -581,9 +581,13 @@ void show_image(image p, const char *name)
     show_image_cv(copy, name, disp);
     free_image(copy);
     cvReleaseImage(&disp);
+    int c = cvWaitKey(ms);
+    if (c != -1) c = c%256;
+    return c;
 #else
     fprintf(stderr, "Not compiled with OpenCV, saving to %s.png instead\n", name);
     save_image(p, name);
+    return 0;
 #endif
 }
 
@@ -727,7 +731,7 @@ void show_image_layers(image p, char *name)
     for(i = 0; i < p.c; ++i){
         sprintf(buff, "%s - Layer %d", name, i);
         image layer = get_image_layer(p, i);
-        show_image(layer, buff);
+        show_image(layer, buff, 1);
         free_image(layer);
     }
 }
@@ -735,7 +739,7 @@ void show_image_layers(image p, char *name)
 void show_image_collapsed(image p, char *name)
 {
     image c = collapse_image_layers(p, 1);
-    show_image(c, name);
+    show_image(c, name, 1);
     free_image(c);
 }
 
@@ -1406,16 +1410,16 @@ void test_resize(char *filename)
     distort_image(c4, .1, .66666, 1.5);
 
 
-    show_image(im,   "Original");
+    show_image(im,   "Original", 1);
-    show_image(gray, "Gray");
+    show_image(gray, "Gray", 1);
-    show_image(c1, "C1");
+    show_image(c1, "C1", 1);
-    show_image(c2, "C2");
+    show_image(c2, "C2", 1);
-    show_image(c3, "C3");
+    show_image(c3, "C3", 1);
-    show_image(c4, "C4");
+    show_image(c4, "C4", 1);
 #ifdef OPENCV
     while(1){
         image aug = random_augment_image(im, 0, .75, 320, 448, 320, 320);
-        show_image(aug, "aug");
+        show_image(aug, "aug", 1);
         free_image(aug);
 
 
@@ -1430,7 +1434,7 @@ void test_resize(char *filename)
         float dhue = rand_uniform(-hue, hue);
 
         distort_image(c, dhue, dsat, dexp);
-        show_image(c, "rand");
+        show_image(c, "rand", 1);
         printf("%f %f %f\n", dhue, dsat, dexp);
         free_image(c);
         cvWaitKey(0);
@@ -1585,7 +1589,7 @@ void show_image_normalized(image im, const char *name)
 {
     image c = copy_image(im);
     normalize_image(c);
-    show_image(c, name);
+    show_image(c, name, 1);
     free_image(c);
 }
 
@@ -1603,7 +1607,7 @@ void show_images(image *ims, int n, char *window)
      */
     normalize_image(m);
     save_image(m, window);
-    show_image(m, window);
+    show_image(m, window, 1);
     free_image(m);
 }
 

src/iseg_layer.c

@@ -0,0 +1,219 @@
+#include "iseg_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "cuda.h"
+#include "utils.h"
+
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+layer make_iseg_layer(int batch, int w, int h, int classes, int ids)
+{
+    layer l = {0};
+    l.type = ISEG;
+
+    l.h = h;
+    l.w = w;
+    l.c = classes + ids;
+    l.out_w = l.w;
+    l.out_h = l.h;
+    l.out_c = l.c;
+    l.classes = classes;
+    l.batch = batch;
+    l.extra = ids;
+    l.cost = calloc(1, sizeof(float));
+    l.outputs = h*w*l.c;
+    l.inputs = l.outputs;
+    l.truths = 90*(l.w*l.h+1);
+    l.delta = calloc(batch*l.outputs, sizeof(float));
+    l.output = calloc(batch*l.outputs, sizeof(float));
+
+    l.counts = calloc(90, sizeof(int));
+    l.sums = calloc(90, sizeof(float*));
+    if(ids){
+        int i;
+        for(i = 0; i < 90; ++i){
+            l.sums[i] = calloc(ids, sizeof(float));
+        }
+    }
+
+    l.forward = forward_iseg_layer;
+    l.backward = backward_iseg_layer;
+#ifdef GPU
+    l.forward_gpu = forward_iseg_layer_gpu;
+    l.backward_gpu = backward_iseg_layer_gpu;
+    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
+#endif
+
+    fprintf(stderr, "iseg\n");
+    srand(0);
+
+    return l;
+}
+
+void resize_iseg_layer(layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+
+    l->outputs = h*w*l->c;
+    l->inputs = l->outputs;
+
+    l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
+    l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
+
+#ifdef GPU
+    cuda_free(l->delta_gpu);
+    cuda_free(l->output_gpu);
+
+    l->delta_gpu =     cuda_make_array(l->delta, l->batch*l->outputs);
+    l->output_gpu =    cuda_make_array(l->output, l->batch*l->outputs);
+#endif
+}
+
+void forward_iseg_layer(const layer l, network net)
+{
+
+    double time = what_time_is_it_now();
+    int i,b,j,k;
+    int ids = l.extra;
+    memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float));
+    memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+
+#ifndef GPU
+    for (b = 0; b < l.batch; ++b){
+        int index = b*l.outputs;
+        activate_array(l.output + index, l.classes*l.w*l.h, LOGISTIC);
+    }
+#endif
+
+    for (b = 0; b < l.batch; ++b){
+        // a priori, each pixel has no class
+        for(i = 0; i < l.classes; ++i){
+            for(k = 0; k < l.w*l.h; ++k){
+                int index = b*l.outputs + i*l.w*l.h + k;
+                l.delta[index] = 0 - l.output[index];
+            }
+        }
+
+        // a priori, embedding should be small magnitude
+        for(i = 0; i < ids; ++i){
+            for(k = 0; k < l.w*l.h; ++k){
+                int index = b*l.outputs + (i+l.classes)*l.w*l.h + k;
+                l.delta[index] = .1 * (0 - l.output[index]);
+            }
+        }
+
+
+        memset(l.counts, 0, 90*sizeof(float));
+        for(i = 0; i < 90; ++i){
+            l.counts[i] = 0;
+            fill_cpu(ids, 0, l.sums[i], 1);
+            
+            int c = net.truth[b*l.truths + i*(l.w*l.h+1)];
+            if(c < 0) break;
+            // add up metric embeddings for each instance
+            for(k = 0; k < l.w*l.h; ++k){
+                int index = b*l.outputs + c*l.w*l.h + k;
+                float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k];
+                if(v){
+                    l.delta[index] = v - l.output[index];
+                    axpy_cpu(ids, 1, l.output + b*l.outputs + l.classes*l.w*l.h + k, l.w*l.h, l.sums[i], 1);
+                    ++l.counts[i];
+                }
+            }
+        }
+
+        float *mse = calloc(90, sizeof(float));
+        for(i = 0; i < 90; ++i){
+            int c = net.truth[b*l.truths + i*(l.w*l.h+1)];
+            if(c < 0) break;
+            for(k = 0; k < l.w*l.h; ++k){
+                float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k];
+                if(v){
+                    int z;
+                    float sum = 0;
+                    for(z = 0; z < ids; ++z){
+                        int index = b*l.outputs + (l.classes + z)*l.w*l.h + k;
+                        sum += pow(l.sums[i][z]/l.counts[i] - l.output[index], 2);
+                    }
+                    mse[i] += sum;
+                }
+            }
+            mse[i] /= l.counts[i];
+        }
+
+        // Calculate average embedding
+        for(i = 0; i < 90; ++i){
+            if(!l.counts[i]) continue;
+            scal_cpu(ids, 1.f/l.counts[i], l.sums[i], 1);
+            if(b == 0 && net.gpu_index == 0){
+                printf("%4d, %6.3f, ", l.counts[i], mse[i]);
+                for(j = 0; j < ids/4; ++j){
+                    printf("%6.3f,", l.sums[i][j]);
+                }
+                printf("\n");
+            }
+        }
+        free(mse);
+
+        // Calculate embedding loss
+        for(i = 0; i < 90; ++i){
+            if(!l.counts[i]) continue;
+            for(k = 0; k < l.w*l.h; ++k){
+                float v = net.truth[b*l.truths + i*(l.w*l.h + 1) + 1 + k];
+                if(v){
+                    for(j = 0; j < 90; ++j){
+                        if(!l.counts[j])continue;
+                        int z;
+                        for(z = 0; z < ids; ++z){
+                            int index = b*l.outputs + (l.classes + z)*l.w*l.h + k;
+                            float diff = l.sums[j][z] - l.output[index];
+                            if (j == i) l.delta[index] +=   diff < 0? -.1 : .1;
+                            else        l.delta[index] += -(diff < 0? -.1 : .1);
+                        }
+                    }
+                }
+            }
+        }
+    }
+
+    *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+    printf("took %lf sec\n", what_time_is_it_now() - time);
+}
+
+void backward_iseg_layer(const layer l, network net)
+{
+    axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1);
+}
+
+#ifdef GPU
+
+void forward_iseg_layer_gpu(const layer l, network net)
+{
+    copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1);
+    int b;
+    for (b = 0; b < l.batch; ++b){
+        activate_array_gpu(l.output_gpu + b*l.outputs, l.classes*l.w*l.h, LOGISTIC);
+        //if(l.extra) activate_array_gpu(l.output_gpu + b*l.outputs + l.classes*l.w*l.h, l.extra*l.w*l.h, LOGISTIC);
+    }
+
+    cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs);
+    forward_iseg_layer(l, net);
+    cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
+}
+
+void backward_iseg_layer_gpu(const layer l, network net)
+{
+    int b;
+    for (b = 0; b < l.batch; ++b){
+        //if(l.extra) gradient_array_gpu(l.output_gpu + b*l.outputs + l.classes*l.w*l.h, l.extra*l.w*l.h, LOGISTIC, l.delta_gpu + b*l.outputs + l.classes*l.w*l.h);
+    }
+    axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
+}
+#endif
+

src/iseg_layer.h

@@ -0,0 +1,19 @@
+#ifndef ISEG_LAYER_H
+#define ISEG_LAYER_H
+
+#include "darknet.h"
+#include "layer.h"
+#include "network.h"
+
+layer make_iseg_layer(int batch, int w, int h, int classes, int ids);
+void forward_iseg_layer(const layer l, network net);
+void backward_iseg_layer(const layer l, network net);
+void resize_iseg_layer(layer *l, int w, int h);
+int iseg_num_detections(layer l, float thresh);
+
+#ifdef GPU
+void forward_iseg_layer_gpu(const layer l, network net);
+void backward_iseg_layer_gpu(layer l, network net);
+#endif
+
+#endif

src/maxpool_layer.c

@@ -27,8 +27,8 @@ maxpool_layer make_maxpool_layer(int batch, int h, int w, int c, int size, int s
     l.w = w;
     l.c = c;
     l.pad = padding;
-    l.out_w = (w + 2*padding - size)/stride + 1;
+    l.out_w = (w + padding - size)/stride + 1;
-    l.out_h = (h + 2*padding - size)/stride + 1;
+    l.out_h = (h + padding - size)/stride + 1;
     l.out_c = c;
     l.outputs = l.out_h * l.out_w * l.out_c;
     l.inputs = h*w*c;
@@ -57,8 +57,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h)
     l->w = w;
     l->inputs = h*w*l->c;
 
-    l->out_w = (w + 2*l->pad - l->size)/l->stride + 1;
+    l->out_w = (w + l->pad - l->size)/l->stride + 1;
-    l->out_h = (h + 2*l->pad - l->size)/l->stride + 1;
+    l->out_h = (h + l->pad - l->size)/l->stride + 1;
     l->outputs = l->out_w * l->out_h * l->c;
     int output_size = l->outputs * l->batch;
 
@@ -79,8 +79,8 @@ void resize_maxpool_layer(maxpool_layer *l, int w, int h)
 void forward_maxpool_layer(const maxpool_layer l, network net)
 {
     int b,i,j,k,m,n;
-    int w_offset = -l.pad;
+    int w_offset = -l.pad/l.stride;
-    int h_offset = -l.pad;
+    int h_offset = -l.pad/l.stride;
 
     int h = l.out_h;
     int w = l.out_w;

src/maxpool_layer_kernels.cu

@@ -9,8 +9,8 @@ extern "C" {
 
 __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *input, float *output, int *indexes)
 {
-    int h = (in_h + 2*pad - size)/stride + 1;
+    int h = (in_h + pad - size)/stride + 1;
-    int w = (in_w + 2*pad - size)/stride + 1;
+    int w = (in_w + pad - size)/stride + 1;
     int c = in_c;
 
     int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
@@ -24,8 +24,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c
     id /= c;
     int b = id;
 
-    int w_offset = -pad;
+    int w_offset = -pad/2;
-    int h_offset = -pad;
+    int h_offset = -pad/2;
 
     int out_index = j + w*(i + h*(k + c*b));
     float max = -INFINITY;
@@ -49,8 +49,8 @@ __global__ void forward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c
 
 __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_c, int stride, int size, int pad, float *delta, float *prev_delta, int *indexes)
 {
-    int h = (in_h + 2*pad - size)/stride + 1;
+    int h = (in_h + pad - size)/stride + 1;
-    int w = (in_w + 2*pad - size)/stride + 1;
+    int w = (in_w + pad - size)/stride + 1;
     int c = in_c;
     int area = (size-1)/stride;
 
@@ -66,8 +66,8 @@ __global__ void backward_maxpool_layer_kernel(int n, int in_h, int in_w, int in_
     id /= in_c;
     int b = id;
 
-    int w_offset = -pad;
+    int w_offset = -pad/2;
-    int h_offset = -pad;
+    int h_offset = -pad/2;
 
     float d = 0;
     int l, m;

src/parser.c

@@ -27,6 +27,7 @@
 #include "parser.h"
 #include "region_layer.h"
 #include "yolo_layer.h"
+#include "iseg_layer.h"
 #include "reorg_layer.h"
 #include "rnn_layer.h"
 #include "route_layer.h"
@@ -52,6 +53,7 @@ LAYER_TYPE string_to_layer_type(char * type)
     if (strcmp(type, "[detection]")==0) return DETECTION;
     if (strcmp(type, "[region]")==0) return REGION;
     if (strcmp(type, "[yolo]")==0) return YOLO;
+    if (strcmp(type, "[iseg]")==0) return ISEG;
     if (strcmp(type, "[local]")==0) return LOCAL;
     if (strcmp(type, "[conv]")==0
             || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
@@ -265,18 +267,19 @@ layer parse_connected(list *options, size_params params)
     return l;
 }
 
-softmax_layer parse_softmax(list *options, size_params params)
+layer parse_softmax(list *options, size_params params)
 {
     int groups = option_find_int_quiet(options, "groups",1);
-    softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
+    layer l = make_softmax_layer(params.batch, params.inputs, groups);
-    layer.temperature = option_find_float_quiet(options, "temperature", 1);
+    l.temperature = option_find_float_quiet(options, "temperature", 1);
     char *tree_file = option_find_str(options, "tree", 0);
-    if (tree_file) layer.softmax_tree = read_tree(tree_file);
+    if (tree_file) l.softmax_tree = read_tree(tree_file);
-    layer.w = params.w;
+    l.w = params.w;
-    layer.h = params.h;
+    l.h = params.h;
-    layer.c = params.c;
+    l.c = params.c;
-    layer.spatial = option_find_float_quiet(options, "spatial", 0);
+    l.spatial = option_find_float_quiet(options, "spatial", 0);
-    return layer;
+    l.noloss =  option_find_int_quiet(options, "noloss", 0);
+    return l;
 }
 
 int *parse_yolo_mask(char *a, int *num)
@@ -338,6 +341,15 @@ layer parse_yolo(list *options, size_params params)
     return l;
 }
 
+layer parse_iseg(list *options, size_params params)
+{
+    int classes = option_find_int(options, "classes", 20);
+    int ids = option_find_int(options, "ids", 32);
+    layer l = make_iseg_layer(params.batch, params.w, params.h, classes, ids);
+    assert(l.outputs == params.inputs);
+    return l;
+}
+
 layer parse_region(list *options, size_params params)
 {
     int coords = option_find_int(options, "coords", 4);
@@ -472,7 +484,7 @@ maxpool_layer parse_maxpool(list *options, size_params params)
 {
     int stride = option_find_int(options, "stride",1);
     int size = option_find_int(options, "size",stride);
-    int padding = option_find_int_quiet(options, "padding", (size-1)/2);
+    int padding = option_find_int_quiet(options, "padding", size-1);
 
     int batch,h,w,c;
     h = params.h;
@@ -791,6 +803,8 @@ network *parse_network_cfg(char *filename)
             l = parse_region(options, params);
         }else if(lt == YOLO){
             l = parse_yolo(options, params);
+        }else if(lt == ISEG){
+            l = parse_iseg(options, params);
         }else if(lt == DETECTION){
             l = parse_detection(options, params);
         }else if(lt == SOFTMAX){
@@ -829,6 +843,7 @@ network *parse_network_cfg(char *filename)
         l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
         l.dontsave = option_find_int_quiet(options, "dontsave", 0);
         l.dontload = option_find_int_quiet(options, "dontload", 0);
+        l.numload = option_find_int_quiet(options, "numload", 0);
         l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
         l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1);
         l.smooth = option_find_float_quiet(options, "smooth", 0);
@@ -1152,7 +1167,8 @@ void load_convolutional_weights(layer l, FILE *fp)
         //load_convolutional_weights_binary(l, fp);
         //return;
     }
-    int num = l.nweights;
+    if(l.numload) l.n = l.numload;
+    int num = l.c/l.groups*l.n*l.size*l.size;
     fread(l.biases, sizeof(float), l.n, fp);
     if (l.batch_normalize && (!l.dontloadscales)){
         fread(l.scales, sizeof(float), l.n, fp);

src/softmax_layer.c

@@ -50,7 +50,7 @@ void forward_softmax_layer(const softmax_layer l, network net)
         softmax_cpu(net.input, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output);
     }
 
-    if(net.truth){
+    if(net.truth && !l.noloss){
         softmax_x_ent_cpu(l.batch*l.inputs, l.output, net.truth, l.delta, l.loss);
         l.cost[0] = sum_array(l.loss, l.batch*l.inputs);
     }
@@ -88,7 +88,7 @@ void forward_softmax_layer_gpu(const softmax_layer l, network net)
             softmax_gpu(net.input_gpu, l.inputs/l.groups, l.batch, l.inputs, l.groups, l.inputs/l.groups, 1, l.temperature, l.output_gpu);
         }
     }
-    if(net.truth){
+    if(net.truth && !l.noloss){
         softmax_x_ent_gpu(l.batch*l.inputs, l.output_gpu, net.truth_gpu, l.delta_gpu, l.loss_gpu);
         if(l.softmax_tree){
             mask_gpu(l.batch*l.inputs, l.delta_gpu, SECRET_NUM, net.truth_gpu, 0);