#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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#include "box.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, int objectness, char *label)
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{
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int classes = 20;
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int elems = 4+classes+objectness;
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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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float scale = 1;
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if(objectness) scale = 1 - box[j++];
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int class = max_index(box+j, classes);
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if(scale * box[j+class] > 0.2){
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int width = box[j+class]*5 + 1;
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printf("%f %s\n", scale * 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_width(im, left, top, right, bot, width, 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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char *train_images = "/home/pjreddie/data/voc/test/train.txt";
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char *backup_directory = "/home/pjreddie/backup/";
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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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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.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 = get_paths(train_images);
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int N = plist->size;
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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(i*imgs < N*130){
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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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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-1)*imgs <= N && i*imgs > N){
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fprintf(stderr, "First stage done\n");
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net.learning_rate *= 10;
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char buff[256];
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sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base);
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save_weights(net, buff);
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}
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if((i-1)*imgs <= 80*N && i*imgs > N*80){
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fprintf(stderr, "Second stage done.\n");
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net.learning_rate *= .1;
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char buff[256];
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sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
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save_weights(net, buff);
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return;
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}
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if((i-1)*imgs <= 120*N && i*imgs > N*120){
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fprintf(stderr, "Third stage done.\n");
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char buff[256];
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sprintf(buff, "%s/%s_third_stage.weights", backup_directory, base);
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net.layers[net.n-1].rescore = 1;
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save_weights(net, buff);
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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, "%s/%s_%d.weights", backup_directory, 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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char buff[256];
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sprintf(buff, "%s/%s_final.weights", backup_directory, base);
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save_weights(net, buff);
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}
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void convert_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes)
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{
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int i,j;
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int per_box = 4+classes+(background || objectness);
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for (i = 0; i < num_boxes*num_boxes; ++i){
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float scale = 1;
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if(objectness) scale = 1-predictions[i*per_box];
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int offset = i*per_box+(background||objectness);
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for(j = 0; j < classes; ++j){
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float prob = scale*predictions[offset+j];
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probs[i][j] = (prob > thresh) ? prob : 0;
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}
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int row = i / num_boxes;
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int col = i % num_boxes;
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offset += classes;
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boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w;
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boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h;
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boxes[i].w = pow(predictions[offset + 2], 2) * w;
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boxes[i].h = pow(predictions[offset + 3], 2) * h;
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}
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}
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void print_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
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{
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int i, j;
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for(i = 0; i < num_boxes*num_boxes; ++i){
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float xmin = boxes[i].x - boxes[i].w/2.;
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float xmax = boxes[i].x + boxes[i].w/2.;
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float ymin = boxes[i].y - boxes[i].h/2.;
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float ymax = boxes[i].y + boxes[i].h/2.;
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if (xmin < 0) xmin = 0;
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if (ymin < 0) ymin = 0;
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if (xmax > w) xmax = w;
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if (ymax > h) ymax = h;
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for(j = 0; j < classes; ++j){
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if (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
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xmin, ymin, xmax, ymax);
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}
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}
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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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set_batch_network(&net, 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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char *base = "results/comp4_det_test_";
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list *plist = get_paths("/home/pjreddie/data/voc/test/2007_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 j;
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FILE **fps = calloc(classes, sizeof(FILE *));
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for(j = 0; j < classes; ++j){
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char buff[1024];
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snprintf(buff, 1024, "%s%s.txt", base, class_names[j]);
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fps[j] = fopen(buff, "w");
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}
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box *boxes = calloc(num_boxes*num_boxes, sizeof(box));
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float **probs = calloc(num_boxes*num_boxes, sizeof(float *));
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for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *));
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int m = plist->size;
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int i=0;
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int t;
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float thresh = .001;
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int nms = 1;
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float iou_thresh = .5;
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int nthreads = 8;
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image *val = calloc(nthreads, sizeof(image));
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image *val_resized = calloc(nthreads, sizeof(image));
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image *buf = calloc(nthreads, sizeof(image));
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image *buf_resized = calloc(nthreads, sizeof(image));
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pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
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for(t = 0; t < nthreads; ++t){
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thr[t] = load_image_thread(paths[i+t], &buf[t], &buf_resized[t], net.w, net.h);
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}
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time_t start = time(0);
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for(i = nthreads; i < m+nthreads; i += nthreads){
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fprintf(stderr, "%d\n", i);
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for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
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pthread_join(thr[t], 0);
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val[t] = buf[t];
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val_resized[t] = buf_resized[t];
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}
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for(t = 0; t < nthreads && i+t < m; ++t){
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thr[t] = load_image_thread(paths[i+t], &buf[t], &buf_resized[t], net.w, net.h);
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}
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for(t = 0; t < nthreads && i+t-nthreads < m; ++t){
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char *path = paths[i+t-nthreads];
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char *id = basecfg(path);
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float *X = val_resized[t].data;
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float *predictions = network_predict(net, X);
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int w = val[t].w;
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int h = val[t].h;
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convert_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
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if (nms) do_nms(boxes, probs, num_boxes, classes, iou_thresh);
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print_detections(fps, id, boxes, probs, num_boxes, classes, w, h);
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free(id);
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free_image(val[t]);
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free_image(val_resized[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, char *filename)
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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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set_batch_network(&net, 1);
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srand(2222222);
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clock_t time;
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char input[256];
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while(1){
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if(filename){
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strncpy(input, filename, 256);
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} else {
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printf("Enter Image Path: ");
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fflush(stdout);
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fgets(input, 256, stdin);
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strtok(input, "\n");
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}
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image im = load_image_color(input,0,0);
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image sized = resize_image(im, net.w, net.h);
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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", input, sec(clock()-time));
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draw_detection(im, predictions, 7, layer.objectness, "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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cvDestroyAllWindows();
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#endif
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if (filename) break;
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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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char *filename = (argc > 5) ? argv[5]: 0;
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if(0==strcmp(argv[2], "test")) test_detection(cfg, weights, filename);
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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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