new font strategy

data/labels/make_labels.py

@@ -2,18 +2,22 @@ import os
 import string
 import pipes
 
-#l = ["person","bicycle","car","motorcycle","airplane","bus","train","truck","boat","traffic light","fire hydrant","stop sign","parking meter","bench","bird","cat","dog","horse","sheep","cow","elephant","bear","zebra","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza","donut","cake","chair","couch","potted plant","bed","dining table","toilet","tv","laptop","mouse","remote","keyboard","cell phone","microwave","oven","toaster","sink","refrigerator","book","clock","vase","scissors","teddy bear","hair drier","toothbrush", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
+font = 'futura-normal'
 
-l = string.printable
+def make_labels(s):
+    l = string.printable
+    for word in l:
+        if word == ' ':
+            os.system('convert -fill black -background white -bordercolor white -font %s -pointsize %d label:"\ " 32_%d.png'%(font,s,s/12-1))
+        if word == '@':
+            os.system('convert -fill black -background white -bordercolor white -font %s -pointsize %d label:"\@" 64_%d.png'%(font,s,s/12-1))
+        elif word == '\\':
+            os.system('convert -fill black -background white -bordercolor white -font %s -pointsize %d label:"\\\\\\\\" 92_%d.png'%(font,s,s/12-1))
+        elif ord(word) in [9,10,11,12,13,14]:
+            pass
+        else:
+            os.system("convert -fill black -background white -bordercolor white -font %s -pointsize %d label:%s \"%d_%d.png\""%(font,s,pipes.quote(word), ord(word),s/12-1))
 
-for word in l:
-    #os.system("convert -fill black -background white -bordercolor white -border 4 -font futura-normal -pointsize 18 label:\"%s\" \"%s.png\""%(word, word))
-    if word == ' ':
-        os.system('convert -fill black -background white -bordercolor white -font futura-normal -pointsize 64 label:"\ " 32.png')
-    elif word == '\\':
-        os.system('convert -fill black -background white -bordercolor white -font futura-normal -pointsize 64 label:"\\\\\\\\" 92.png')
-    elif ord(word) in [9,10,11,12,13,14]:
-        pass
-    else:
-        os.system("convert -fill black -background white -bordercolor white -font futura-normal -pointsize 64 label:%s \"%d.png\""%(pipes.quote(word), ord(word)))
+for i in [12,24,36,48,60,72,84,96]:
+    make_labels(i)
 

src/classifier.c

@@ -13,50 +13,6 @@
 image get_image_from_stream(CvCapture *cap);
 #endif
 
-list *read_data_cfg(char *filename)
-{
-    FILE *file = fopen(filename, "r");
-    if(file == 0) file_error(filename);
-    char *line;
-    int nu = 0;
-    list *options = make_list();
-    while((line=fgetl(file)) != 0){
-        ++ nu;
-        strip(line);
-        switch(line[0]){
-            case '\0':
-            case '#':
-            case ';':
-                free(line);
-                break;
-            default:
-                if(!read_option(line, options)){
-                    fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
-                    free(line);
-                }
-                break;
-        }
-    }
-    fclose(file);
-    return options;
-}
-
-void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves)
-{
-    int j;
-    for(j = 0; j < n; ++j){
-        int parent = hier->parent[j];
-        if(parent >= 0){
-            predictions[j] *= predictions[parent]; 
-        }
-    }
-    if(only_leaves){
-        for(j = 0; j < n; ++j){
-            if(!hier->leaf[j]) predictions[j] = 0;
-        }
-    }
-}
-
 float *get_regression_values(char **labels, int n)
 {
     float *v = calloc(n, sizeof(float));
@@ -488,26 +444,6 @@ void validate_classifier_full(char *datacfg, char *filename, char *weightfile)
     }
 }
 
-void change_leaves(tree *t, char *leaf_list)
-{
-    list *llist = get_paths(leaf_list);
-    char **leaves = (char **)list_to_array(llist);
-    int n = llist->size;
-    int i,j;
-    int found = 0;
-    for(i = 0; i < t->n; ++i){
-        t->leaf[i] = 0;
-        for(j = 0; j < n; ++j){
-            if (0==strcmp(t->name[i], leaves[j])){
-                t->leaf[i] = 1;
-                ++found;
-                break;
-            }
-        }
-    }
-    fprintf(stderr, "Found %d leaves.\n", found);
-}
-
 
 void validate_classifier_single(char *datacfg, char *filename, char *weightfile)
 {

src/coco.c

@@ -318,7 +318,7 @@ void validate_coco_recall(char *cfgfile, char *weightfile)
 
 void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
 {
-    image *alphabet = load_alphabet();
+    image **alphabet = load_alphabet();
     network net = parse_network_cfg(cfgfile);
     if(weightfile){
         load_weights(&net, weightfile);

src/convolutional_kernels.cu

@@ -215,6 +215,10 @@ void pull_convolutional_layer(convolutional_layer layer)
         cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
         cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
     }
+    if (layer.adam){
+        cuda_pull_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size);
+        cuda_pull_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size);
+    }
 }
 
 void push_convolutional_layer(convolutional_layer layer)
@@ -228,6 +232,10 @@ void push_convolutional_layer(convolutional_layer layer)
         cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
         cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
     }
+    if (layer.adam){
+        cuda_push_array(layer.m_gpu, layer.m, layer.c*layer.n*layer.size*layer.size);
+        cuda_push_array(layer.v_gpu, layer.v, layer.c*layer.n*layer.size*layer.size);
+    }
 }
 
 void update_convolutional_layer_gpu(convolutional_layer layer, int batch, float learning_rate, float momentum, float decay)

src/convolutional_layer.c

@@ -235,6 +235,11 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
         l.rolling_mean = calloc(n, sizeof(float));
         l.rolling_variance = calloc(n, sizeof(float));
     }
+    if(adam){
+        l.adam = 1;
+        l.m = calloc(c*n*size*size, sizeof(float));
+        l.v = calloc(c*n*size*size, sizeof(float));
+    }
 
 #ifdef GPU
     l.forward_gpu = forward_convolutional_layer_gpu;
@@ -243,9 +248,8 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
 
     if(gpu_index >= 0){
         if (adam) {
-            l.adam = 1;
-            l.m_gpu = cuda_make_array(l.weight_updates, c*n*size*size);
-            l.v_gpu = cuda_make_array(l.weight_updates, c*n*size*size);
+            l.m_gpu = cuda_make_array(l.m, c*n*size*size);
+            l.v_gpu = cuda_make_array(l.v, c*n*size*size);
         }
 
         l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);