#include "network.h"
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#include "detection_layer.h"
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#include "cost_layer.h"
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#include "utils.h"
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#include "parser.h"
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char *class_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
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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"};
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#define AMNT 3
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void draw_detection(image im, float *box, int side, char *label)
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{
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int classes = 20;
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int elems = 4+classes;
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int j;
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int r, c;
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for(r = 0; r < side; ++r){
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for(c = 0; c < side; ++c){
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j = (r*side + c) * elems;
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//printf("%d\n", j);
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//printf("Prob: %f\n", box[j]);
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int class = max_index(box+j, classes);
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if(box[j+class] > .4){
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//int z;
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//for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+z], class_names[z]);
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printf("%f %s\n", box[j+class], class_names[class]);
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float red = get_color(0,class,classes);
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float green = get_color(1,class,classes);
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float blue = get_color(2,class,classes);
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//float maxheight = distance_from_edge(r, side);
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//float maxwidth = distance_from_edge(c, side);
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j += classes;
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float y = box[j+0];
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float x = box[j+1];
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x = (x+c)/side;
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y = (y+r)/side;
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float w = box[j+2]; //*maxwidth;
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float h = box[j+3]; //*maxheight;
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h = h*h;
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w = w*w;
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//printf("coords %f %f %f %f\n", x, y, w, h);
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int left = (x-w/2)*im.w;
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int right = (x+w/2)*im.w;
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int top = (y-h/2)*im.h;
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int bot = (y+h/2)*im.h;
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draw_box(im, left, top, right, bot, red, green, blue);
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}
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}
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}
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//printf("Done\n");
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show_image(im, label);
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}
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void draw_localization(image im, float *box)
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{
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int classes = 20;
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int class;
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for(class = 0; class < classes; ++class){
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//int z;
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//for(z = 0; z < classes; ++z) printf("%f %s\n", box[j+z], class_names[z]);
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float red = get_color(0,class,classes);
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float green = get_color(1,class,classes);
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float blue = get_color(2,class,classes);
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int j = class*4;
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float x = box[j+0];
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float y = box[j+1];
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float w = box[j+2]; //*maxheight;
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float h = box[j+3]; //*maxwidth;
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//printf("coords %f %f %f %f\n", x, y, w, h);
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int left = (x-w/2)*im.w;
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int right = (x+w/2)*im.w;
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int top = (y-h/2)*im.h;
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int bot = (y+h/2)*im.h;
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draw_box(im, left, top, right, bot, red, green, blue);
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}
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//printf("Done\n");
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}
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void train_localization(char *cfgfile, char *weightfile)
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{
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srand(time(0));
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data_seed = time(0);
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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float avg_loss = -1;
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 128;
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int classes = 20;
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int i = net.seen/imgs;
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data train, buffer;
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char **paths;
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list *plist;
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plist = get_paths("/home/pjreddie/data/voc/loc.2012val.txt");
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paths = (char **)list_to_array(plist);
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pthread_t load_thread = load_data_localization_thread(imgs, paths, plist->size, classes, net.w, net.h, &buffer);
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clock_t time;
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while(1){
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_localization_thread(imgs, paths, plist->size, classes, net.w, net.h, &buffer);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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float loss = train_network(net, train);
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//TODO
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float *out = get_network_output_gpu(net);
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image im = float_to_image(net.w, net.h, 3, train.X.vals[127]);
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image copy = copy_image(im);
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draw_localization(copy, &(out[63*80]));
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draw_localization(copy, train.y.vals[127]);
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show_image(copy, "box");
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cvWaitKey(0);
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free_image(copy);
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net.seen += imgs;
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if (avg_loss < 0) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
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if(i%100==0){
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
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save_weights(net, buff);
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}
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free_data(train);
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}
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}
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void train_detection_teststuff(char *cfgfile, char *weightfile)
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{
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srand(time(0));
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data_seed = time(0);
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int imgnet = 0;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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float avg_loss = -1;
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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detection_layer layer = get_network_detection_layer(net);
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net.learning_rate = 0;
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net.decay = 0;
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 128;
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int i = net.seen/imgs;
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data train, buffer;
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int classes = layer.classes;
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int background = layer.background;
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int side = sqrt(get_detection_layer_locations(layer));
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char **paths;
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list *plist;
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if (imgnet){
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plist = get_paths("/home/pjreddie/data/imagenet/det.train.list");
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}else{
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plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
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//plist = get_paths("/home/pjreddie/data/voc/no_2007_test.txt");
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//plist = get_paths("/home/pjreddie/data/coco/trainval.txt");
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//plist = get_paths("/home/pjreddie/data/voc/all2007-2012.txt");
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}
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paths = (char **)list_to_array(plist);
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pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
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clock_t time;
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cost_layer clayer = net.layers[net.n-1];
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while(1){
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
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/*
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image im = float_to_image(net.w, net.h, 3, train.X.vals[114]);
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image copy = copy_image(im);
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draw_detection(copy, train.y.vals[114], 7);
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free_image(copy);
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*/
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int z;
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int count = 0;
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float sx, sy, sw, sh;
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sx = sy = sw = sh = 0;
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for(z = 0; z < clayer.batch*clayer.inputs; z += 24){
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if(clayer.delta[z+20]){
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++count;
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sx += fabs(clayer.delta[z+20])*64;
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sy += fabs(clayer.delta[z+21])*64;
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sw += fabs(clayer.delta[z+22])*448;
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sh += fabs(clayer.delta[z+23])*448;
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}
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}
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printf("Avg error: %f, %f, %f x %f\n", sx/count, sy/count, sw/count, sh/count);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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float loss = train_network(net, train);
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net.seen += imgs;
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if (avg_loss < 0) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
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if(i == 100){
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net.learning_rate *= 10;
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}
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if(i%100==0){
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
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save_weights(net, buff);
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}
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free_data(train);
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}
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}
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void train_detection(char *cfgfile, char *weightfile)
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{
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srand(time(0));
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data_seed = time(0);
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int imgnet = 0;
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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float avg_loss = -1;
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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detection_layer layer = get_network_detection_layer(net);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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int imgs = 128;
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int i = net.seen/imgs;
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data train, buffer;
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int classes = layer.classes;
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int background = layer.background;
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int side = sqrt(get_detection_layer_locations(layer));
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char **paths;
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list *plist;
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if (imgnet){
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plist = get_paths("/home/pjreddie/data/imagenet/det.train.list");
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}else{
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plist = get_paths("/home/pjreddie/data/voc/no_2012_val.txt");
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//plist = get_paths("/home/pjreddie/data/voc/no_2007_test.txt");
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//plist = get_paths("/home/pjreddie/data/coco/trainval.txt");
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//plist = get_paths("/home/pjreddie/data/voc/all2007-2012.txt");
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}
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paths = (char **)list_to_array(plist);
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pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
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clock_t time;
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while(1){
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
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/*
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image im = float_to_image(net.w, net.h, 3, train.X.vals[114]);
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image copy = copy_image(im);
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draw_detection(copy, train.y.vals[114], 7);
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free_image(copy);
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*/
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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time=clock();
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float loss = train_network(net, train);
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net.seen += imgs;
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if (avg_loss < 0) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
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if(i == 100){
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net.learning_rate *= 10;
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}
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if(i%100==0){
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char buff[256];
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sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
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save_weights(net, buff);
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}
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free_data(train);
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}
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}
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void predict_detections(network net, data d, float threshold, int offset, int classes, int nuisance, int background, int num_boxes, int per_box)
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{
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matrix pred = network_predict_data(net, d);
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int j, k, class;
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for(j = 0; j < pred.rows; ++j){
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for(k = 0; k < pred.cols; k += per_box){
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float scale = 1.;
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int index = k/per_box;
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int row = index / num_boxes;
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int col = index % num_boxes;
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if (nuisance) scale = 1.-pred.vals[j][k];
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for (class = 0; class < classes; ++class){
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int ci = k+classes+background+nuisance;
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float x = (pred.vals[j][ci + 0] + col)/num_boxes;
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float y = (pred.vals[j][ci + 1] + row)/num_boxes;
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float w = pred.vals[j][ci + 2]; //* distance_from_edge(row, num_boxes);
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float h = pred.vals[j][ci + 3]; //* distance_from_edge(col, num_boxes);
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w = pow(w, 2);
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h = pow(h, 2);
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float prob = scale*pred.vals[j][k+class+background+nuisance];
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if(prob < threshold) continue;
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printf("%d %d %f %f %f %f %f\n", offset + j, class, prob, x, y, w, h);
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}
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}
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}
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free_matrix(pred);
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}
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void validate_detection(char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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detection_layer layer = get_network_detection_layer(net);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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//list *plist = get_paths("/home/pjreddie/data/voc/test_2007.txt");
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list *plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
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//list *plist = get_paths("/home/pjreddie/data/voc/test.txt");
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//list *plist = get_paths("/home/pjreddie/data/voc/val.expanded.txt");
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//list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
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char **paths = (char **)list_to_array(plist);
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int classes = layer.classes;
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int nuisance = layer.nuisance;
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int background = (layer.background && !nuisance);
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int num_boxes = sqrt(get_detection_layer_locations(layer));
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int per_box = 4+classes+background+nuisance;
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int num_output = num_boxes*num_boxes*per_box;
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int m = plist->size;
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int i = 0;
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int splits = 100;
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int nthreads = 4;
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int t;
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data *val = calloc(nthreads, sizeof(data));
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data *buf = calloc(nthreads, sizeof(data));
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pthread_t *thr = calloc(nthreads, sizeof(data));
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for(t = 0; t < nthreads; ++t){
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int num = (i+1+t)*m/splits - (i+t)*m/splits;
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char **part = paths+((i+t)*m/splits);
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thr[t] = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &(buf[t]));
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}
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clock_t time;
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for(i = nthreads; i <= splits; i += nthreads){
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time=clock();
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for(t = 0; t < nthreads; ++t){
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pthread_join(thr[t], 0);
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val[t] = buf[t];
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}
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for(t = 0; t < nthreads && i < splits; ++t){
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int num = (i+1+t)*m/splits - (i+t)*m/splits;
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char **part = paths+((i+t)*m/splits);
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thr[t] = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &(buf[t]));
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}
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fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
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for(t = 0; t < nthreads; ++t){
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predict_detections(net, val[t], .01, (i-nthreads+t)*m/splits, classes, nuisance, background, num_boxes, per_box);
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free_data(val[t]);
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}
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time=clock();
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}
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}
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void validate_detection_post(char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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set_batch_network(&net, 1);
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network post = parse_network_cfg("cfg/localize.cfg");
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load_weights(&post, "/home/pjreddie/imagenet_backup/localize_1000.weights");
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set_batch_network(&post, 1);
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detection_layer layer = get_network_detection_layer(net);
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fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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srand(time(0));
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//list *plist = get_paths("/home/pjreddie/data/voc/test_2007.txt");
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list *plist = get_paths("/home/pjreddie/data/voc/val_2012.txt");
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//list *plist = get_paths("/home/pjreddie/data/voc/test.txt");
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//list *plist = get_paths("/home/pjreddie/data/voc/val.expanded.txt");
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//list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
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char **paths = (char **)list_to_array(plist);
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int classes = layer.classes;
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int nuisance = layer.nuisance;
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int background = (layer.background && !nuisance);
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int num_boxes = sqrt(get_detection_layer_locations(layer));
|
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int per_box = 4+classes+background+nuisance;
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int m = plist->size;
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int i = 0;
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float threshold = .01;
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clock_t time = clock();
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for(i = 0; i < m; ++i){
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image im = load_image_color(paths[i], 0, 0);
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if(i % 100 == 0) {
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fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
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time = clock();
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}
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image sized = resize_image(im, net.w, net.h);
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float *out = network_predict(net, sized.data);
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free_image(sized);
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int k, class;
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//show_image(im, "original");
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int num_output = num_boxes*num_boxes*per_box;
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//image cp1 = copy_image(im);
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//draw_detection(cp1, out, 7, "before");
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for(k = 0; k < num_output; k += per_box){
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float *post_out = 0;
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float scale = 1.;
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int index = k/per_box;
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int row = index / num_boxes;
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int col = index % num_boxes;
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if (nuisance) scale = 1.-out[k];
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for (class = 0; class < classes; ++class){
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int ci = k+classes+background+nuisance;
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float x = (out[ci + 0] + col)/num_boxes;
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float y = (out[ci + 1] + row)/num_boxes;
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float w = out[ci + 2]; //* distance_from_edge(row, num_boxes);
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float h = out[ci + 3]; //* distance_from_edge(col, num_boxes);
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w = w*w;
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h = h*h;
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float prob = scale*out[k+class+background+nuisance];
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if (prob >= threshold) {
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x *= im.w;
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y *= im.h;
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w *= im.w;
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h *= im.h;
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w += 32;
|
h += 32;
|
int left = (x - w/2);
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int top = (y - h/2);
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int right = (x + w/2);
|
int bot = (y+h/2);
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if (left < 0) left = 0;
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if (right > im.w) right = im.w;
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if (top < 0) top = 0;
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if (bot > im.h) bot = im.h;
|
|
image crop = crop_image(im, left, top, right-left, bot-top);
|
image resize = resize_image(crop, post.w, post.h);
|
if (!post_out){
|
post_out = network_predict(post, resize.data);
|
}
|
/*
|
draw_localization(resize, post_out);
|
show_image(resize, "second");
|
fprintf(stderr, "%s\n", class_names[class]);
|
cvWaitKey(0);
|
*/
|
int index = 4*class;
|
float px = post_out[index+0];
|
float py = post_out[index+1];
|
float pw = post_out[index+2];
|
float ph = post_out[index+3];
|
px = (px * crop.w + left) / im.w;
|
py = (py * crop.h + top) / im.h;
|
pw = (pw * crop.w) / im.w;
|
ph = (ph * crop.h) / im.h;
|
|
out[ci + 0] = px*num_boxes - col;
|
out[ci + 1] = py*num_boxes - row;
|
out[ci + 2] = sqrt(pw);
|
out[ci + 3] = sqrt(ph);
|
/*
|
show_image(crop, "cropped");
|
cvWaitKey(0);
|
*/
|
free_image(crop);
|
free_image(resize);
|
printf("%d %d %f %f %f %f %f\n", i, class, prob, px, py, pw, ph);
|
}
|
}
|
}
|
/*
|
image cp2 = copy_image(im);
|
draw_detection(cp2, out, 7, "after");
|
cvWaitKey(0);
|
*/
|
}
|
}
|
|
void test_detection(char *cfgfile, char *weightfile)
|
{
|
network net = parse_network_cfg(cfgfile);
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
int im_size = 448;
|
set_batch_network(&net, 1);
|
srand(2222222);
|
clock_t time;
|
char filename[256];
|
while(1){
|
fgets(filename, 256, stdin);
|
strtok(filename, "\n");
|
image im = load_image_color(filename, im_size, im_size);
|
printf("%d %d %d\n", im.h, im.w, im.c);
|
float *X = im.data;
|
time=clock();
|
float *predictions = network_predict(net, X);
|
printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
|
draw_detection(im, predictions, 7, "detections");
|
free_image(im);
|
}
|
}
|
|
void run_detection(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 *cfg = argv[3];
|
char *weights = (argc > 4) ? argv[4] : 0;
|
if(0==strcmp(argv[2], "test")) test_detection(cfg, weights);
|
else if(0==strcmp(argv[2], "train")) train_detection(cfg, weights);
|
else if(0==strcmp(argv[2], "teststuff")) train_detection_teststuff(cfg, weights);
|
else if(0==strcmp(argv[2], "trainloc")) train_localization(cfg, weights);
|
else if(0==strcmp(argv[2], "valid")) validate_detection(cfg, weights);
|
else if(0==strcmp(argv[2], "validpost")) validate_detection_post(cfg, weights);
|
}
|
