#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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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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int class = max_index(box+j, classes);
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if(box[j+class] > .2){
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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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j += classes;
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float x = box[j+0];
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float y = 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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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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draw_box(im, left+1, top+1, right+1, bot+1, red, green, blue);
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draw_box(im, left-1, top-1, right-1, bot-1, red, green, blue);
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}
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}
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}
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show_image(im, label);
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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 || layer.objectness);
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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/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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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, "truth");
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cvWaitKey(0);
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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%1000==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 objectness, 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 (objectness) 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 || objectness);
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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 || objectness)];
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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.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 objectness = layer.objectness;
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int background = layer.background;
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int num_boxes = sqrt(get_detection_layer_locations(layer));
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int per_box = 4+classes+(background || objectness);
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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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time_t start = time(0);
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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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for(i = nthreads; i <= splits; i += nthreads){
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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\n", i);
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for(t = 0; t < nthreads; ++t){
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predict_detections(net, val[t], .001, (i-nthreads+t)*m/splits, classes, objectness, background, num_boxes, per_box);
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free_data(val[t]);
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}
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}
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fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
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}
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void test_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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if (!layer.joint) fprintf(stderr, "Detection layer should use joint prediction to draw correctly.\n");
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int im_size = 448;
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set_batch_network(&net, 1);
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srand(2222222);
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clock_t time;
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char filename[256];
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while(1){
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printf("Image Path: ");
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fgets(filename, 256, stdin);
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strtok(filename, "\n");
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image im = load_image_color(filename,0,0);
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image sized = resize_image(im, im_size, im_size);
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float *X = sized.data;
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time=clock();
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float *predictions = network_predict(net, X);
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printf("%s: Predicted in %f seconds.\n", filename, sec(clock()-time));
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draw_detection(im, predictions, 7, "predictions");
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free_image(im);
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free_image(sized);
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#ifdef OPENCV
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cvWaitKey(0);
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#endif
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}
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}
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void run_detection(int argc, char **argv)
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{
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if(argc < 4){
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fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
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return;
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}
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char *cfg = argv[3];
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char *weights = (argc > 4) ? argv[4] : 0;
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if(0==strcmp(argv[2], "test")) test_detection(cfg, weights);
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else if(0==strcmp(argv[2], "train")) train_detection(cfg, weights);
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else if(0==strcmp(argv[2], "valid")) validate_detection(cfg, weights);
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}
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