GUYS I KNOW HOW TO MULTITHREAD :SNAKE:

Makefile

@@ -1,6 +1,7 @@
 GPU=0
 CUDNN=0
 OPENCV=0
+OPENMP=0
 DEBUG=0
 
 ARCH= -gencode arch=compute_20,code=[sm_20,sm_21] \
@@ -19,13 +20,17 @@ EXEC=darknet
 OBJDIR=./obj/
 
 CC=gcc
-NVCC=nvcc --compiler-options '-fPIC'
+NVCC=nvcc 
 AR=ar
 ARFLAGS=rcs
 OPTS=-Ofast
 LDFLAGS= -lm -pthread 
 COMMON= -Iinclude/ -Isrc/
-CFLAGS=-Wall -Wfatal-errors -fPIC
+CFLAGS=-Wall -Wno-unknown-pragmas -Wfatal-errors -fPIC
+
+ifeq ($(OPENMP), 1) 
+COMMON+= -fopenmp
+endif
 
 ifeq ($(DEBUG), 1) 
 OPTS=-O0 -g

cfg/imagenet1k.data

@@ -1,6 +1,6 @@
 classes=1000
 train  = /data/imagenet/imagenet1k.train.list
-valid  = /data/imagenet/imagenet1k.train.list
+valid  = /data/imagenet/imagenet1k.valid.list
 backup = /home/pjreddie/backup/
 labels = data/imagenet.labels.list
 names  = data/imagenet.shortnames.list

examples/classifier.c

@@ -58,7 +58,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
     load_args args = {0};
     args.w = net.w;
     args.h = net.h;
-    args.threads = 32;
+    args.threads = 64;
     args.hierarchy = net.hierarchy;
 
     args.min = net.min_crop;
@@ -670,7 +670,6 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
     int *indexes = calloc(top, sizeof(int));
     char buff[256];
     char *input = buff;
-    int size = net.w;
     while(1){
         if(filename){
             strncpy(input, filename, 256);
@@ -682,8 +681,8 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
             strtok(input, "\n");
         }
         image im = load_image_color(input, 0, 0);
-        image r = resize_min(im, size);
-        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;

examples/coco.c

@@ -342,7 +342,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
         get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
         if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
-        draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, alphabet, 80);
+        draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, 0, coco_classes, alphabet, 80);
         save_image(im, "prediction");
         show_image(im, "predictions");
         free_image(im);

examples/detector.c

@@ -41,9 +41,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     //int N = plist->size;
     char **paths = (char **)list_to_array(plist);
 
-    load_args args = {0};
-    args.w = net.w;
-    args.h = net.h;
+    load_args args = get_base_args(net);
+    args.coords = l.coords;
     args.paths = paths;
     args.n = imgs;
     args.m = plist->size;
@@ -52,13 +51,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     args.num_boxes = l.max_boxes;
     args.d = &buffer;
     args.type = DETECTION_DATA;
+    //args.type = INSTANCE_DATA;
     args.threads = 8;
 
-    args.angle = net.angle;
-    args.exposure = net.exposure;
-    args.saturation = net.saturation;
-    args.hue = net.hue;
-
     pthread_t load_thread = load_data(args);
     clock_t time;
     int count = 0;
@@ -102,7 +97,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
             image im = float_to_image(net.w, net.h, 3, train.X.vals[zz]);
             int k;
             for(k = 0; k < l.max_boxes; ++k){
-                box b = float_to_box(train.y.vals[zz] + k*5);
+                box b = float_to_box(train.y.vals[zz] + k*5, 1);
                 printf("%f %f %f %f\n", b.x, b.y, b.w, b.h);
                 draw_bbox(im, b, 1, 1,0,0);
             }
@@ -130,7 +125,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
 
         i = get_current_batch(net);
         printf("%ld: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
-        if(i%1000==0){
+        if(i%100==0){
 #ifdef GPU
             if(ngpus != 1) sync_nets(nets, ngpus, 0);
 #endif
@@ -342,7 +337,7 @@ void validate_detector_flip(char *datacfg, char *cfgfile, char *weightfile, char
             network_predict(net, input.data);
             int w = val[t].w;
             int h = val[t].h;
-            get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, map, .5, 0);
+            get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, 0, map, .5, 0);
             if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
             if (coco){
                 print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
@@ -473,7 +468,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *out
             network_predict(net, X);
             int w = val[t].w;
             int h = val[t].h;
-            get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, map, .5, 0);
+            get_region_boxes(l, w, h, net.w, net.h, thresh, probs, boxes, 0, 0, map, .5, 0);
             if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
             if (coco){
                 print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
@@ -537,7 +532,7 @@ void validate_detector_recall(char *cfgfile, char *weightfile)
         image sized = resize_image(orig, net.w, net.h);
         char *id = basecfg(path);
         network_predict(net, sized.data);
-        get_region_boxes(l, sized.w, sized.h, net.w, net.h, thresh, probs, boxes, 1, 0, .5, 1);
+        get_region_boxes(l, sized.w, sized.h, net.w, net.h, thresh, probs, boxes, 0, 1, 0, .5, 1);
         if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, nms);
 
         char labelpath[4096];
@@ -589,11 +584,11 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
     }
     set_batch_network(&net, 1);
     srand(2222222);
-    clock_t time;
+    double time;
     char buff[256];
     char *input = buff;
     int j;
-    float nms=.4;
+    float nms=.3;
     while(1){
         if(filename){
             strncpy(input, filename, 256);
@@ -615,15 +610,20 @@ void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filenam
         box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
         float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
         for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes + 1, sizeof(float *));
+        float **masks = 0;
+        if (l.coords > 4){
+            masks = calloc(l.w*l.h*l.n, sizeof(float*));
+            for(j = 0; j < l.w*l.h*l.n; ++j) masks[j] = calloc(l.coords-4, sizeof(float *));
+        }
 
         float *X = sized.data;
-        time=clock();
+        time=what_time_is_it_now();
         network_predict(net, X);
-        printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
-        get_region_boxes(l, im.w, im.h, net.w, net.h, thresh, probs, boxes, 0, 0, hier_thresh, 1);
+        printf("%s: Predicted in %f seconds.\n", input, what_time_is_it_now()-time);
+        get_region_boxes(l, im.w, im.h, net.w, net.h, thresh, probs, boxes, masks, 0, 0, hier_thresh, 1);
         if (nms) do_nms_obj(boxes, probs, l.w*l.h*l.n, l.classes, nms);
         //else if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
-        draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
+        draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, masks, names, alphabet, l.classes);
         if(outfile){
             save_image(im, outfile);
         }

examples/rnn.c

@@ -279,6 +279,54 @@ void test_char_rnn(char *cfgfile, char *weightfile, int num, char *seed, float t
     printf("\n");
 }
 
+void test_tactic_rnn_multi(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
+{
+    char **tokens = 0;
+    if(token_file){
+        size_t n;
+        tokens = read_tokens(token_file, &n);
+    }
+
+    srand(rseed);
+    char *base = basecfg(cfgfile);
+    fprintf(stderr, "%s\n", base);
+
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    int inputs = net.inputs;
+
+    int i, j;
+    for(i = 0; i < net.n; ++i) net.layers[i].temperature = temp;
+    int c = 0;
+    float *input = calloc(inputs, sizeof(float));
+    float *out = 0;
+
+    while(1){
+        reset_rnn_state(net, 0);
+        while((c = getc(stdin)) != EOF && c != 0){
+            input[c] = 1;
+            out = network_predict(net, input);
+            input[c] = 0;
+        }
+        for(i = 0; i < num; ++i){
+            for(j = 0; j < inputs; ++j){
+                if (out[j] < .0001) out[j] = 0;
+            }
+            int next = sample_array(out, inputs);
+            if(c == '.' && next == '\n') break;
+            c = next;
+            print_symbol(c, tokens);
+
+            input[c] = 1;
+            out = network_predict(net, input);
+            input[c] = 0;
+        }
+        printf("\n");
+    }
+}
+
 void test_tactic_rnn(char *cfgfile, char *weightfile, int num, float temp, int rseed, char *token_file)
 {
     char **tokens = 0;

examples/yolo.c

@@ -308,8 +308,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
         get_detection_boxes(l, 1, 1, thresh, probs, boxes, 0);
         if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
-        //draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
-        draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, alphabet, 20);
+        draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, 0, voc_names, alphabet, 20);
         save_image(im, "predictions");
         show_image(im, "predictions");
 

include/darknet.h

@@ -175,6 +175,7 @@ struct layer{
     float coord_scale;
     float object_scale;
     float noobject_scale;
+    float mask_scale;
     float class_scale;
     int bias_match;
     int random;
@@ -508,7 +509,7 @@ 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
+    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
 } data_type;
 
 typedef struct load_args{
@@ -530,6 +531,7 @@ typedef struct load_args{
     int background;
     int scale;
     int center;
+    int coords;
     float jitter;
     float angle;
     float aspect;
@@ -642,7 +644,7 @@ void save_weights_upto(network net, char *filename, int cutoff);
 void load_weights_upto(network *net, char *filename, int start, int cutoff);
 
 void zero_objectness(layer l);
-void get_region_boxes(layer l, int w, int h, int netw, int neth, float thresh, float **probs, box *boxes, int only_objectness, int *map, float tree_thresh, int relative);
+void get_region_boxes(layer l, int w, int h, int netw, int neth, float thresh, float **probs, box *boxes, float **masks, int only_objectness, int *map, float tree_thresh, int relative);
 void free_network(network net);
 void set_batch_network(network *net, int b);
 image load_image(char *filename, int w, int h, int c);
@@ -677,13 +679,15 @@ void random_distort_image(image im, float hue, float saturation, float exposure)
 void fill_image(image m, float s);
 image grayscale_image(image im);
 void rotate_image_cw(image im, int times);
+double what_time_is_it_now();
 image rotate_image(image m, float rad);
 void visualize_network(network net);
 float box_iou(box a, box b);
 void do_nms(box *boxes, float **probs, int total, int classes, float thresh);
 data load_all_cifar10();
 box_label *read_boxes(char *filename, int *n);
-void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **labels, int classes);
+box float_to_box(float *f, int stride);
+void draw_detections(image im, int num, float thresh, box *boxes, float **probs, float **masks, char **names, image **alphabet, int classes);
 
 matrix network_predict_data(network net, data test);
 image **load_alphabet();
@@ -709,6 +713,7 @@ image get_image_from_stream(CvCapture *cap);
 void free_image(image m);
 float train_network(network net, data d);
 pthread_t load_data_in_thread(load_args args);
+void load_data_blocking(load_args args);
 list *get_paths(char *filename);
 void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride);
 void change_leaves(tree *t, char *leaf_list);

src/blas_kernels.cu

@@ -698,9 +698,6 @@ extern "C" void shortcut_gpu(int batch, int w1, int h1, int c1, float *add, int
     int minw = (w1 < w2) ? w1 : w2;
     int minh = (h1 < h2) ? h1 : h2;
     int minc = (c1 < c2) ? c1 : c2;
-    assert(w1 == w2);
-    assert(h1 == h2);
-    assert(c1 == c2);
 
     int stride = w1/w2;
     int sample = w2/w1;
@@ -892,19 +889,21 @@ __global__ void softmax_tree_kernel(float *input, int spatial, int batch, int st
 
 extern "C" void softmax_tree(float *input, int spatial, int batch, int stride, float temp, float *output, tree hier)
 {
-    //int *tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups);
-    //int *tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups);
+    int *tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups);
+    int *tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups);
+    /*
     static int *tree_groups_size = 0;
     static int *tree_groups_offset = 0;
     if(!tree_groups_size){
         tree_groups_size = cuda_make_int_array(hier.group_size, hier.groups);
         tree_groups_offset = cuda_make_int_array(hier.group_offset, hier.groups);
     }
+    */
     int num = spatial*batch*hier.groups;
     softmax_tree_kernel<<<cuda_gridsize(num), BLOCK>>>(input, spatial, batch, stride, temp, output, hier.groups, tree_groups_size, tree_groups_offset);
     check_error(cudaPeekAtLastError());
-    //cuda_free((float *)tree_groups_size);
-    //cuda_free((float *)tree_groups_offset);
+    cuda_free((float *)tree_groups_size);
+    cuda_free((float *)tree_groups_offset);
 }
 
 __global__ void softmax_kernel(float *input, int n, int batch, int batch_offset, int groups, int group_offset, int stride, float temp, float *output)

src/box.h

@@ -6,7 +6,6 @@ typedef struct{
     float dx, dy, dw, dh;
 } dbox;
 
-box float_to_box(float *f, int stride);
 float box_rmse(box a, box b);
 dbox diou(box a, box b);
 box decode_box(box b, box anchor);

src/convolutional_layer.c

@@ -130,7 +130,11 @@ void cudnn_convolutional_setup(layer *l)
     cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w); 
     cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1); 
     cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c, l->size, l->size); 
+    #if CUDNN_MAJOR >= 6
+    cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT);
+    #else
     cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);
+    #endif
     cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
             l->srcTensorDesc,
             l->weightDesc,