detection better?

src/data.c

@@ -108,8 +108,9 @@ void randomize_boxes(box *b, int n)
 
 void fill_truth_detection(char *path, float *truth, int classes, int num_boxes, int flip, int background, float dx, float dy, float sx, float sy)
 {
-    char *labelpath = find_replace(path, "VOC2012/JPEGImages", "labels");
+    char *labelpath = find_replace(path, "detection_images", "labels");
     labelpath = find_replace(labelpath, ".jpg", ".txt");
+    labelpath = find_replace(labelpath, ".JPEG", ".txt");
     int count = 0;
     box *boxes = read_boxes(labelpath, &count);
     randomize_boxes(boxes, count);
@@ -293,8 +294,6 @@ void free_data(data d)
 
 data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int num_boxes, int background)
 {
-    //float minscale = 0.85;
-    //float maxscale = 1.15;
     char **random_paths = get_random_paths(paths, n, m);
     int i;
     data d;
@@ -310,10 +309,14 @@ data load_data_detection_jitter_random(int n, char **paths, int m, int classes,
         image orig = load_image_color(random_paths[i], 0, 0);
         int oh = orig.h;
         int ow = orig.w;
-        int pleft  = (rand_uniform() * 64. - 32.);
+
-        int pright = (rand_uniform() * 64. - 32.);
+        int dw = ow/10;
-        int ptop   = (rand_uniform() * 64. - 32.);
+        int dh = oh/10;
-        int pbot   = (rand_uniform() * 64. - 32.);
+
+        int pleft  = (rand_uniform() * 2*dw - dw);
+        int pright = (rand_uniform() * 2*dw - dw);
+        int ptop   = (rand_uniform() * 2*dh - dh);
+        int pbot   = (rand_uniform() * 2*dh - dh);
 
         int swidth =  ow - pleft - pright;
         int sheight = oh - ptop - pbot;

src/detection.c

@@ -1,13 +1,15 @@
 #include "network.h"
+#include "detection_layer.h"
 #include "utils.h"
 #include "parser.h"
 
 
 char *class_names[] = {"bg", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
+char *inet_class_names[] = {"bg", "accordion", "airplane", "ant", "antelope", "apple", "armadillo", "artichoke", "axe", "baby bed", "backpack", "bagel", "balance beam", "banana", "band aid", "banjo", "baseball", "basketball", "bathing cap", "beaker", "bear", "bee", "bell pepper", "bench", "bicycle", "binder", "bird", "bookshelf", "bow tie", "bow", "bowl", "brassiere", "burrito", "bus", "butterfly", "camel", "can opener", "car", "cart", "cattle", "cello", "centipede", "chain saw", "chair", "chime", "cocktail shaker", "coffee maker", "computer keyboard", "computer mouse", "corkscrew", "cream", "croquet ball", "crutch", "cucumber", "cup or mug", "diaper", "digital clock", "dishwasher", "dog", "domestic cat", "dragonfly", "drum", "dumbbell", "electric fan", "elephant", "face powder", "fig", "filing cabinet", "flower pot", "flute", "fox", "french horn", "frog", "frying pan", "giant panda", "goldfish", "golf ball", "golfcart", "guacamole", "guitar", "hair dryer", "hair spray", "hamburger", "hammer", "hamster", "harmonica", "harp", "hat with a wide brim", "head cabbage", "helmet", "hippopotamus", "horizontal bar", "horse", "hotdog", "iPod", "isopod", "jellyfish", "koala bear", "ladle", "ladybug", "lamp", "laptop", "lemon", "lion", "lipstick", "lizard", "lobster", "maillot", "maraca", "microphone", "microwave", "milk can", "miniskirt", "monkey", "motorcycle", "mushroom", "nail", "neck brace", "oboe", "orange", "otter", "pencil box", "pencil sharpener", "perfume", "person", "piano", "pineapple", "ping-pong ball", "pitcher", "pizza", "plastic bag", "plate rack", "pomegranate", "popsicle", "porcupine", "power drill", "pretzel", "printer", "puck", "punching bag", "purse", "rabbit", "racket", "ray", "red panda", "refrigerator", "remote control", "rubber eraser", "rugby ball", "ruler", "salt or pepper shaker", "saxophone", "scorpion", "screwdriver", "seal", "sheep", "ski", "skunk", "snail", "snake", "snowmobile", "snowplow", "soap dispenser", "soccer ball", "sofa", "spatula", "squirrel", "starfish", "stethoscope", "stove", "strainer", "strawberry", "stretcher", "sunglasses", "swimming trunks", "swine", "syringe", "table", "tape player", "tennis ball", "tick", "tie", "tiger", "toaster", "traffic light", "train", "trombone", "trumpet", "turtle", "tv or monitor", "unicycle", "vacuum", "violin", "volleyball", "waffle iron", "washer", "water bottle", "watercraft", "whale", "wine bottle", "zebra"};
 #define AMNT 3
 void draw_detection(image im, float *box, int side)
 {
-    int classes = 21;
+    int classes = 201;
     int elems = 4+classes;
     int j;
     int r, c;
@@ -21,7 +23,7 @@ void draw_detection(image im, float *box, int side)
             if(box[j+class] > .02 || 1){
                 //int z;
                 //for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+z], class_names[z]);
-                printf("%f %s\n", box[j+class], class_names[class]);
+                printf("%f %s\n", box[j+class], inet_class_names[class]);
                 float red = get_color(0,class,classes);
                 float green = get_color(1,class,classes);
                 float blue = get_color(2,class,classes);
@@ -35,6 +37,8 @@ void draw_detection(image im, float *box, int side)
                 y = (y+r)/side;
                 float h = box[j+2]; //*maxheight;
                 float w = box[j+3]; //*maxwidth;
+                h = h*h;
+                w = w*w;
                 //printf("coords %f %f %f %f\n", x, y, w, h);
 
                 int left  = (x-w/2)*im.w;
@@ -52,6 +56,8 @@ void draw_detection(image im, float *box, int side)
 
 void train_detection(char *cfgfile, char *weightfile)
 {
+    srand(time(0));
+    int imgnet = 0;
     char *base = basecfg(cfgfile);
     printf("%s\n", base);
     float avg_loss = -1;
@@ -59,30 +65,37 @@ void train_detection(char *cfgfile, char *weightfile)
     if(weightfile){
         load_weights(&net, weightfile);
     }
-    //net.seen = 0;
+    detection_layer *layer = get_network_detection_layer(net);
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 128;
-    srand(time(0));
-    //srand(23410);
     int i = net.seen/imgs;
-    list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
-    char **paths = (char **)list_to_array(plist);
-    printf("%d\n", plist->size);
     data train, buffer;
-    int im_dim = 448;
+
-    int classes = 20;
+    int classes = layer->classes;
-    int background = 1;
+    int background = layer->background;
-    pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, background, &buffer);
+    int side = sqrt(get_detection_layer_locations(*layer));
+
+    char **paths;
+    list *plist;
+    if (imgnet){
+        plist = get_paths("/home/pjreddie/data/imagenet/det.train.list");
+    }else{
+        plist = get_paths("/home/pjreddie/data/voc/trainall.txt");
+    }
+    paths = (char **)list_to_array(plist);
+    pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.h, net.w, side, side, background, &buffer);
     clock_t time;
     while(1){
         i += 1;
         time=clock();
         pthread_join(load_thread, 0);
         train = buffer;
-        load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, background, &buffer);
+        load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.h, net.w, side, side, background, &buffer);
 
-           //image im = float_to_image(im_dim, im_dim, 3, train.X.vals[114]);
+/*
-           //draw_detection(im, train.y.vals[114], 7);
+           image im = float_to_image(im_dim, im_dim, 3, train.X.vals[114]);
+           draw_detection(im, train.y.vals[114], 7);
+*/
 
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
@@ -106,17 +119,19 @@ void validate_detection(char *cfgfile, char *weightfile)
     if(weightfile){
         load_weights(&net, weightfile);
     }
+    detection_layer *layer = get_network_detection_layer(net);
     fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     srand(time(0));
 
     list *plist = get_paths("/home/pjreddie/data/voc/val.txt");
     //list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
     char **paths = (char **)list_to_array(plist);
-    int im_size = 448;
+
-    int classes = 20;
+    int classes = layer->classes;
-    int background = 0;
+    int nuisance = layer->nuisance;
-    int nuisance = 1;
+    int background = (layer->background && !nuisance);
-    int num_boxes = 7;
+    int num_boxes = sqrt(get_detection_layer_locations(*layer));
+
     int per_box = 4+classes+background+nuisance;
     int num_output = num_boxes*num_boxes*per_box;
 
@@ -127,7 +142,7 @@ void validate_detection(char *cfgfile, char *weightfile)
 
     fprintf(stderr, "%d\n", m);
     data val, buffer;
-    pthread_t load_thread = load_data_thread(paths, num, 0, 0, num_output, im_size, im_size, &buffer);
+    pthread_t load_thread = load_data_thread(paths, num, 0, 0, num_output, net.h, net.w, &buffer);
     clock_t time;
     for(i = 1; i <= splits; ++i){
         time=clock();
@@ -136,7 +151,7 @@ void validate_detection(char *cfgfile, char *weightfile)
 
         num = (i+1)*m/splits - i*m/splits;
         char **part = paths+(i*m/splits);
-        if(i != splits) load_thread = load_data_thread(part, num, 0, 0, num_output, im_size, im_size, &buffer);
+        if(i != splits) load_thread = load_data_thread(part, num, 0, 0, num_output, net.h, net.w, &buffer);
 
         fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
         matrix pred = network_predict_data(net, val);

src/detection_layer.h

@@ -23,6 +23,7 @@ detection_layer *make_detection_layer(int batch, int inputs, int classes, int co
 void forward_detection_layer(const detection_layer layer, network_state state);
 void backward_detection_layer(const detection_layer layer, network_state state);
 int get_detection_layer_output_size(detection_layer layer);
+int get_detection_layer_locations(detection_layer layer);
 
 #ifdef GPU
 void forward_detection_layer_gpu(const detection_layer layer, network_state state);

src/network.c

@@ -500,6 +500,18 @@ int get_network_input_size(network net)
     return get_network_input_size_layer(net, 0);
 }
 
+detection_layer *get_network_detection_layer(network net)
+{
+    int i;
+    for(i = 0; i < net.n; ++i){
+        if(net.types[i] == DETECTION){
+            detection_layer *layer = (detection_layer *)net.layers[i];
+            return layer;
+        }
+    }
+    return 0;
+}
+
 image get_network_image_layer(network net, int i)
 {
     if(net.types[i] == CONVOLUTIONAL){

src/network.h

@@ -3,6 +3,7 @@
 #define NETWORK_H
 
 #include "image.h"
+#include "detection_layer.h"
 #include "params.h"
 #include "data.h"
 
@@ -81,6 +82,10 @@ int resize_network(network net, int h, int w, int c);
 void set_batch_network(network *net, int b);
 int get_network_input_size(network net);
 float get_network_cost(network net);
+detection_layer *get_network_detection_layer(network net);
+
+int get_network_nuisance(network net);
+int get_network_background(network net);
 
 #endif