#ifdef _DEBUG
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#include <stdlib.h>
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#include <crtdbg.h>
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#endif
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#include <sys/time.h>
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#include "network.h"
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#include "region_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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#include "demo.h"
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#include "option_list.h"
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#ifdef OPENCV
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#include "opencv2/highgui/highgui_c.h"
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#include "opencv2/core/core_c.h"
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//#include "opencv2/core/core.hpp"
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#include "opencv2/core/version.hpp"
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#include "opencv2/imgproc/imgproc_c.h"
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#ifndef CV_VERSION_EPOCH
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#include "opencv2/videoio/videoio_c.h"
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#define OPENCV_VERSION CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR)""CVAUX_STR(CV_VERSION_REVISION)
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#pragma comment(lib, "opencv_world" OPENCV_VERSION ".lib")
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#else
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#define OPENCV_VERSION CVAUX_STR(CV_VERSION_EPOCH)""CVAUX_STR(CV_VERSION_MAJOR)""CVAUX_STR(CV_VERSION_MINOR)
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#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
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#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
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#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
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#endif
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IplImage* draw_train_chart(float max_img_loss, int max_batches, int number_of_lines, int img_size);
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void draw_train_loss(IplImage* img, int img_size, float avg_loss, float max_img_loss, int current_batch, int max_batches);
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#endif // OPENCV
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static int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
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void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show)
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{
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list *options = read_data_cfg(datacfg);
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char *train_images = option_find_str(options, "train", "data/train.list");
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char *backup_directory = option_find_str(options, "backup", "/backup/");
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srand(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 *nets = calloc(ngpus, sizeof(network));
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srand(time(0));
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int seed = rand();
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int i;
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for(i = 0; i < ngpus; ++i){
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srand(seed);
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#ifdef GPU
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cuda_set_device(gpus[i]);
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#endif
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nets[i] = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&nets[i], weightfile);
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}
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if(clear) *nets[i].seen = 0;
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nets[i].learning_rate *= ngpus;
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}
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srand(time(0));
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network net = nets[0];
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int imgs = net.batch * net.subdivisions * ngpus;
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
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data train, buffer;
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layer l = net.layers[net.n - 1];
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int classes = l.classes;
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float jitter = l.jitter;
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list *plist = get_paths(train_images);
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//int N = plist->size;
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char **paths = (char **)list_to_array(plist);
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int init_w = net.w;
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int init_h = net.h;
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int iter_save;
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iter_save = get_current_batch(net);
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load_args args = {0};
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args.w = net.w;
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args.h = net.h;
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args.paths = paths;
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args.n = imgs;
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args.m = plist->size;
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args.classes = classes;
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args.flip = net.flip;
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args.jitter = jitter;
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args.num_boxes = l.max_boxes;
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args.small_object = net.small_object;
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args.d = &buffer;
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args.type = DETECTION_DATA;
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args.threads = 16; // 64
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args.angle = net.angle;
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args.exposure = net.exposure;
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args.saturation = net.saturation;
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args.hue = net.hue;
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#ifdef OPENCV
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args.threads = 3;
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IplImage* img = NULL;
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float max_img_loss = 5;
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int number_of_lines = 100;
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int img_size = 1000;
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if (!dont_show)
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img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size);
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#endif //OPENCV
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pthread_t load_thread = load_data(args);
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double time;
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int count = 0;
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//while(i*imgs < N*120){
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while(get_current_batch(net) < net.max_batches){
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if(l.random && count++%10 == 0){
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printf("Resizing\n");
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int dim = (rand() % 12 + (init_w/32 - 5)) * 32; // +-160
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//if (get_current_batch(net)+100 > net.max_batches) dim = 544;
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//int dim = (rand() % 4 + 16) * 32;
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printf("%d\n", dim);
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args.w = dim;
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args.h = dim;
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pthread_join(load_thread, 0);
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train = buffer;
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free_data(train);
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load_thread = load_data(args);
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for(i = 0; i < ngpus; ++i){
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resize_network(nets + i, dim, dim);
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}
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net = nets[0];
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}
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time=what_time_is_it_now();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data(args);
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/*
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int k;
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for(k = 0; k < l.max_boxes; ++k){
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box b = float_to_box(train.y.vals[10] + 1 + k*5);
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if(!b.x) break;
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printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
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}
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image im = float_to_image(448, 448, 3, train.X.vals[10]);
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int k;
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for(k = 0; k < l.max_boxes; ++k){
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box b = float_to_box(train.y.vals[10] + 1 + k*5);
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printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
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draw_bbox(im, b, 8, 1,0,0);
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}
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save_image(im, "truth11");
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*/
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printf("Loaded: %lf seconds\n", (what_time_is_it_now()-time));
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time=what_time_is_it_now();
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float loss = 0;
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#ifdef GPU
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if(ngpus == 1){
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loss = train_network(net, train);
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} else {
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loss = train_networks(nets, ngpus, train, 4);
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}
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#else
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loss = train_network(net, train);
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#endif
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if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan)
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avg_loss = avg_loss*.9 + loss*.1;
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i = get_current_batch(net);
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printf("\n %d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now()-time), i*imgs);
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#ifdef OPENCV
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if(!dont_show)
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draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches);
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#endif // OPENCV
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//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
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//if (i % 100 == 0) {
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if(i >= (iter_save + 100)) {
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iter_save = i;
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#ifdef GPU
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if (ngpus != 1) sync_nets(nets, ngpus, 0);
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#endif
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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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#ifdef GPU
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if(ngpus != 1) sync_nets(nets, ngpus, 0);
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#endif
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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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//cvReleaseImage(&img);
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//cvDestroyAllWindows();
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}
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static int get_coco_image_id(char *filename)
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{
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char *p = strrchr(filename, '/');
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char *c = strrchr(filename, '_');
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if (c) p = c;
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return atoi(p + 1);
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}
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static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
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{
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int i, j;
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int image_id = get_coco_image_id(image_path);
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for (i = 0; i < num_boxes; ++i) {
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float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
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float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
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float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
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float ymax = dets[i].bbox.y + dets[i].bbox.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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float bx = xmin;
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float by = ymin;
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float bw = xmax - xmin;
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float bh = ymax - ymin;
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for (j = 0; j < classes; ++j) {
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if (dets[i].prob[j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
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}
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}
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}
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void print_detector_detections(FILE **fps, char *id, detection *dets, int total, 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 < total; ++i) {
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float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1;
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float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1;
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float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1;
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float ymax = dets[i].bbox.y + dets[i].bbox.h / 2. + 1;
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if (xmin < 1) xmin = 1;
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if (ymin < 1) ymin = 1;
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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 (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[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 print_imagenet_detections(FILE *fp, int id, detection *dets, int total, 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 < total; ++i) {
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float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
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float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
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float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
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float ymax = dets[i].bbox.y + dets[i].bbox.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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int class = j;
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if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[class],
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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_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
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{
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int j;
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list *options = read_data_cfg(datacfg);
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char *valid_images = option_find_str(options, "valid", "data/train.list");
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char *name_list = option_find_str(options, "names", "data/names.list");
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char *prefix = option_find_str(options, "results", "results");
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char **names = get_labels(name_list);
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char *mapf = option_find_str(options, "map", 0);
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int *map = 0;
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if (mapf) map = read_map(mapf);
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network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
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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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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(valid_images);
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char **paths = (char **)list_to_array(plist);
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layer l = net.layers[net.n - 1];
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int classes = l.classes;
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char buff[1024];
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char *type = option_find_str(options, "eval", "voc");
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FILE *fp = 0;
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FILE **fps = 0;
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int coco = 0;
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int imagenet = 0;
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if (0 == strcmp(type, "coco")) {
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if (!outfile) outfile = "coco_results";
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snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
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fp = fopen(buff, "w");
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fprintf(fp, "[\n");
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coco = 1;
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}
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else if (0 == strcmp(type, "imagenet")) {
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if (!outfile) outfile = "imagenet-detection";
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snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
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fp = fopen(buff, "w");
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imagenet = 1;
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classes = 200;
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}
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else {
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if (!outfile) outfile = "comp4_det_test_";
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fps = calloc(classes, sizeof(FILE *));
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for (j = 0; j < classes; ++j) {
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snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
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fps[j] = fopen(buff, "w");
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}
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}
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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 = .005;
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float nms = .45;
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int nthreads = 4;
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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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load_args args = { 0 };
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args.w = net.w;
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args.h = net.h;
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args.type = IMAGE_DATA;
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//args.type = LETTERBOX_DATA;
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for (t = 0; t < nthreads; ++t) {
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args.path = paths[i + t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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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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args.path = paths[i + t];
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args.im = &buf[t];
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args.resized = &buf_resized[t];
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thr[t] = load_data_in_thread(args);
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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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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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int nboxes = 0;
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int letterbox = (args.type == LETTERBOX_DATA);
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detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox);
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if (nms) do_nms_sort(dets, nboxes, classes, nms);
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if (coco) {
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print_cocos(fp, path, dets, nboxes, classes, w, h);
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}
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else if (imagenet) {
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print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h);
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}
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else {
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print_detector_detections(fps, id, dets, nboxes, classes, w, h);
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}
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free_detections(dets, nboxes);
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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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for (j = 0; j < classes; ++j) {
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if (fps) fclose(fps[j]);
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}
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if (coco) {
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fseek(fp, -2, SEEK_CUR);
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fprintf(fp, "\n]\n");
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fclose(fp);
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}
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fprintf(stderr, "Total Detection Time: %f Seconds\n", time(0) - start);
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}
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void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
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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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fuse_conv_batchnorm(net);
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srand(time(0));
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//list *plist = get_paths("data/coco_val_5k.list");
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list *options = read_data_cfg(datacfg);
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char *valid_images = option_find_str(options, "valid", "data/train.txt");
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list *plist = get_paths(valid_images);
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char **paths = (char **)list_to_array(plist);
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layer l = net.layers[net.n - 1];
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int j, k;
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int m = plist->size;
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int i = 0;
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float thresh = .001;
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float iou_thresh = .5;
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float nms = .4;
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int total = 0;
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int correct = 0;
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int proposals = 0;
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float avg_iou = 0;
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for (i = 0; i < m; ++i) {
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char *path = paths[i];
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image orig = load_image_color(path, 0, 0);
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image sized = resize_image(orig, net.w, net.h);
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char *id = basecfg(path);
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network_predict(net, sized.data);
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int nboxes = 0;
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int letterbox = 0;
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detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox);
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if (nms) do_nms_obj(dets, nboxes, 1, nms);
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char labelpath[4096];
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find_replace(path, "images", "labels", labelpath);
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find_replace(labelpath, "JPEGImages", "labels", labelpath);
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find_replace(labelpath, ".jpg", ".txt", labelpath);
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find_replace(labelpath, ".png", ".txt", labelpath);
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find_replace(labelpath, ".bmp", ".txt", labelpath);
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find_replace(labelpath, ".JPG", ".txt", labelpath);
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find_replace(labelpath, ".JPEG", ".txt", labelpath);
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int num_labels = 0;
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box_label *truth = read_boxes(labelpath, &num_labels);
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for (k = 0; k < nboxes; ++k) {
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if (dets[k].objectness > thresh) {
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++proposals;
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}
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}
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for (j = 0; j < num_labels; ++j) {
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++total;
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box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
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float best_iou = 0;
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for (k = 0; k < nboxes; ++k) {
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float iou = box_iou(dets[k].bbox, t);
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if (dets[k].objectness > thresh && iou > best_iou) {
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best_iou = iou;
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}
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}
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avg_iou += best_iou;
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if (best_iou > iou_thresh) {
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++correct;
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}
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}
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//fprintf(stderr, " %s - %s - ", paths[i], labelpath);
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fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals / (i + 1), avg_iou * 100 / total, 100.*correct / total);
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free(id);
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free_image(orig);
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free_image(sized);
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}
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}
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typedef struct {
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box b;
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float p;
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int class_id;
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int image_index;
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int truth_flag;
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int unique_truth_index;
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} box_prob;
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int detections_comparator(const void *pa, const void *pb)
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{
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box_prob a = *(box_prob *)pa;
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box_prob b = *(box_prob *)pb;
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float diff = a.p - b.p;
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if (diff < 0) return 1;
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else if (diff > 0) return -1;
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return 0;
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}
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void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou)
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{
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int j;
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list *options = read_data_cfg(datacfg);
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char *valid_images = option_find_str(options, "valid", "data/train.txt");
|
char *difficult_valid_images = option_find_str(options, "difficult", NULL);
|
char *name_list = option_find_str(options, "names", "data/names.list");
|
char **names = get_labels(name_list);
|
char *mapf = option_find_str(options, "map", 0);
|
int *map = 0;
|
if (mapf) map = read_map(mapf);
|
|
network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
|
if (weightfile) {
|
load_weights(&net, weightfile);
|
}
|
//set_batch_network(&net, 1);
|
fuse_conv_batchnorm(net);
|
srand(time(0));
|
|
list *plist = get_paths(valid_images);
|
char **paths = (char **)list_to_array(plist);
|
|
char **paths_dif = NULL;
|
if (difficult_valid_images) {
|
list *plist_dif = get_paths(difficult_valid_images);
|
paths_dif = (char **)list_to_array(plist_dif);
|
}
|
|
|
layer l = net.layers[net.n - 1];
|
int classes = l.classes;
|
|
int m = plist->size;
|
int i = 0;
|
int t;
|
|
const float thresh = .005;
|
const float nms = .45;
|
const float iou_thresh = 0.5;
|
|
int nthreads = 4;
|
image *val = calloc(nthreads, sizeof(image));
|
image *val_resized = calloc(nthreads, sizeof(image));
|
image *buf = calloc(nthreads, sizeof(image));
|
image *buf_resized = calloc(nthreads, sizeof(image));
|
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
|
|
load_args args = { 0 };
|
args.w = net.w;
|
args.h = net.h;
|
args.type = IMAGE_DATA;
|
//args.type = LETTERBOX_DATA;
|
|
//const float thresh_calc_avg_iou = 0.24;
|
float avg_iou = 0;
|
int tp_for_thresh = 0;
|
int fp_for_thresh = 0;
|
|
box_prob *detections = calloc(1, sizeof(box_prob));
|
int detections_count = 0;
|
int unique_truth_count = 0;
|
|
int *truth_classes_count = calloc(classes, sizeof(int));
|
|
for (t = 0; t < nthreads; ++t) {
|
args.path = paths[i + t];
|
args.im = &buf[t];
|
args.resized = &buf_resized[t];
|
thr[t] = load_data_in_thread(args);
|
}
|
time_t start = time(0);
|
for (i = nthreads; i < m + nthreads; i += nthreads) {
|
fprintf(stderr, "%d\n", i);
|
for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
|
pthread_join(thr[t], 0);
|
val[t] = buf[t];
|
val_resized[t] = buf_resized[t];
|
}
|
for (t = 0; t < nthreads && i + t < m; ++t) {
|
args.path = paths[i + t];
|
args.im = &buf[t];
|
args.resized = &buf_resized[t];
|
thr[t] = load_data_in_thread(args);
|
}
|
for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
|
const int image_index = i + t - nthreads;
|
char *path = paths[image_index];
|
char *id = basecfg(path);
|
float *X = val_resized[t].data;
|
network_predict(net, X);
|
|
int nboxes = 0;
|
int letterbox = (args.type == LETTERBOX_DATA);
|
float hier_thresh = 0;
|
detection *dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letterbox);
|
//detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letterbox); // for letterbox=1
|
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
|
|
char labelpath[4096];
|
find_replace(path, "images", "labels", labelpath);
|
find_replace(labelpath, "JPEGImages", "labels", labelpath);
|
find_replace(labelpath, ".jpg", ".txt", labelpath);
|
find_replace(labelpath, ".png", ".txt", labelpath);
|
find_replace(labelpath, ".bmp", ".txt", labelpath);
|
find_replace(labelpath, ".JPG", ".txt", labelpath);
|
find_replace(labelpath, ".JPEG", ".txt", labelpath);
|
int num_labels = 0;
|
box_label *truth = read_boxes(labelpath, &num_labels);
|
int i, j;
|
for (j = 0; j < num_labels; ++j) {
|
truth_classes_count[truth[j].id]++;
|
}
|
|
// difficult
|
box_label *truth_dif = NULL;
|
int num_labels_dif = 0;
|
if (paths_dif)
|
{
|
char *path_dif = paths_dif[image_index];
|
|
char labelpath_dif[4096];
|
find_replace(path_dif, "images", "labels", labelpath_dif);
|
find_replace(labelpath_dif, "JPEGImages", "labels", labelpath_dif);
|
find_replace(labelpath_dif, ".jpg", ".txt", labelpath_dif);
|
find_replace(labelpath_dif, ".JPEG", ".txt", labelpath_dif);
|
find_replace(labelpath_dif, ".png", ".txt", labelpath_dif);
|
truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
|
}
|
|
const int checkpoint_detections_count = detections_count;
|
|
for (i = 0; i < nboxes; ++i) {
|
|
int class_id;
|
for (class_id = 0; class_id < classes; ++class_id) {
|
float prob = dets[i].prob[class_id];
|
if (prob > 0) {
|
detections_count++;
|
detections = realloc(detections, detections_count * sizeof(box_prob));
|
detections[detections_count - 1].b = dets[i].bbox;
|
detections[detections_count - 1].p = prob;
|
detections[detections_count - 1].image_index = image_index;
|
detections[detections_count - 1].class_id = class_id;
|
detections[detections_count - 1].truth_flag = 0;
|
detections[detections_count - 1].unique_truth_index = -1;
|
|
int truth_index = -1;
|
float max_iou = 0;
|
for (j = 0; j < num_labels; ++j)
|
{
|
box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
|
//printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d \n",
|
// box_iou(dets[i].bbox, t), prob, class_id, truth[j].id);
|
float current_iou = box_iou(dets[i].bbox, t);
|
if (current_iou > iou_thresh && class_id == truth[j].id) {
|
if (current_iou > max_iou) {
|
max_iou = current_iou;
|
truth_index = unique_truth_count + j;
|
}
|
}
|
}
|
|
// best IoU
|
if (truth_index > -1) {
|
detections[detections_count - 1].truth_flag = 1;
|
detections[detections_count - 1].unique_truth_index = truth_index;
|
}
|
else {
|
// if object is difficult then remove detection
|
for (j = 0; j < num_labels_dif; ++j) {
|
box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h };
|
float current_iou = box_iou(dets[i].bbox, t);
|
if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
|
--detections_count;
|
break;
|
}
|
}
|
}
|
|
// calc avg IoU, true-positives, false-positives for required Threshold
|
if (prob > thresh_calc_avg_iou) {
|
int z, found = 0;
|
for (z = checkpoint_detections_count; z < detections_count-1; ++z)
|
if (detections[z].unique_truth_index == truth_index) {
|
found = 1; break;
|
}
|
|
if(truth_index > -1 && found == 0) {
|
avg_iou += max_iou;
|
++tp_for_thresh;
|
}
|
else
|
fp_for_thresh++;
|
}
|
}
|
}
|
}
|
|
unique_truth_count += num_labels;
|
|
free_detections(dets, nboxes);
|
free(id);
|
free_image(val[t]);
|
free_image(val_resized[t]);
|
}
|
}
|
|
if((tp_for_thresh + fp_for_thresh) > 0)
|
avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
|
|
|
// SORT(detections)
|
qsort(detections, detections_count, sizeof(box_prob), detections_comparator);
|
|
typedef struct {
|
double precision;
|
double recall;
|
int tp, fp, fn;
|
} pr_t;
|
|
// for PR-curve
|
pr_t **pr = calloc(classes, sizeof(pr_t*));
|
for (i = 0; i < classes; ++i) {
|
pr[i] = calloc(detections_count, sizeof(pr_t));
|
}
|
printf("detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count);
|
|
|
int *truth_flags = calloc(unique_truth_count, sizeof(int));
|
|
int rank;
|
for (rank = 0; rank < detections_count; ++rank) {
|
if(rank % 100 == 0)
|
printf(" rank = %d of ranks = %d \r", rank, detections_count);
|
|
if (rank > 0) {
|
int class_id;
|
for (class_id = 0; class_id < classes; ++class_id) {
|
pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
|
pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
|
}
|
}
|
|
box_prob d = detections[rank];
|
// if (detected && isn't detected before)
|
if (d.truth_flag == 1) {
|
if (truth_flags[d.unique_truth_index] == 0)
|
{
|
truth_flags[d.unique_truth_index] = 1;
|
pr[d.class_id][rank].tp++; // true-positive
|
}
|
}
|
else {
|
pr[d.class_id][rank].fp++; // false-positive
|
}
|
|
for (i = 0; i < classes; ++i)
|
{
|
const int tp = pr[i][rank].tp;
|
const int fp = pr[i][rank].fp;
|
const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive
|
pr[i][rank].fn = fn;
|
|
if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp);
|
else pr[i][rank].precision = 0;
|
|
if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn);
|
else pr[i][rank].recall = 0;
|
}
|
}
|
|
free(truth_flags);
|
|
|
double mean_average_precision = 0;
|
|
for (i = 0; i < classes; ++i) {
|
double avg_precision = 0;
|
int point;
|
for (point = 0; point < 11; ++point) {
|
double cur_recall = point * 0.1;
|
double cur_precision = 0;
|
for (rank = 0; rank < detections_count; ++rank)
|
{
|
if (pr[i][rank].recall >= cur_recall) { // > or >=
|
if (pr[i][rank].precision > cur_precision) {
|
cur_precision = pr[i][rank].precision;
|
}
|
}
|
}
|
//printf("class_id = %d, point = %d, cur_recall = %.4f, cur_precision = %.4f \n", i, point, cur_recall, cur_precision);
|
|
avg_precision += cur_precision;
|
}
|
avg_precision = avg_precision / 11;
|
printf("class_id = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision*100);
|
mean_average_precision += avg_precision;
|
}
|
|
const float cur_precision = (float)tp_for_thresh / ((float)tp_for_thresh + (float)fp_for_thresh);
|
const float cur_recall = (float)tp_for_thresh / ((float)tp_for_thresh + (float)(unique_truth_count - tp_for_thresh));
|
const float f1_score = 2.F * cur_precision * cur_recall / (cur_precision + cur_recall);
|
printf(" for thresh = %1.2f, precision = %1.2f, recall = %1.2f, F1-score = %1.2f \n",
|
thresh_calc_avg_iou, cur_precision, cur_recall, f1_score);
|
|
printf(" for thresh = %0.2f, TP = %d, FP = %d, FN = %d, average IoU = %2.2f %% \n",
|
thresh_calc_avg_iou, tp_for_thresh, fp_for_thresh, unique_truth_count - tp_for_thresh, avg_iou * 100);
|
|
mean_average_precision = mean_average_precision / classes;
|
printf("\n mean average precision (mAP) = %f, or %2.2f %% \n", mean_average_precision, mean_average_precision*100);
|
|
|
for (i = 0; i < classes; ++i) {
|
free(pr[i]);
|
}
|
free(pr);
|
free(detections);
|
free(truth_classes_count);
|
|
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
|
}
|
|
#ifdef OPENCV
|
typedef struct {
|
float w, h;
|
} anchors_t;
|
|
int anchors_comparator(const void *pa, const void *pb)
|
{
|
anchors_t a = *(anchors_t *)pa;
|
anchors_t b = *(anchors_t *)pb;
|
float diff = b.w*b.h - a.w*a.h;
|
if (diff < 0) return 1;
|
else if (diff > 0) return -1;
|
return 0;
|
}
|
|
void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show)
|
{
|
printf("\n num_of_clusters = %d, width = %d, height = %d \n", num_of_clusters, width, height);
|
if (width < 0 || height < 0) {
|
printf("Usage: darknet detector calc_anchors data/voc.data -num_of_clusters 9 -width 416 -height 416 \n");
|
printf("Error: set width and height \n");
|
return;
|
}
|
|
//float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 };
|
float *rel_width_height_array = calloc(1000, sizeof(float));
|
|
list *options = read_data_cfg(datacfg);
|
char *train_images = option_find_str(options, "train", "data/train.list");
|
list *plist = get_paths(train_images);
|
int number_of_images = plist->size;
|
char **paths = (char **)list_to_array(plist);
|
|
int number_of_boxes = 0;
|
printf(" read labels from %d images \n", number_of_images);
|
|
int i, j;
|
for (i = 0; i < number_of_images; ++i) {
|
char *path = paths[i];
|
char labelpath[4096];
|
find_replace(path, "images", "labels", labelpath);
|
find_replace(labelpath, "JPEGImages", "labels", labelpath);
|
find_replace(labelpath, ".jpg", ".txt", labelpath);
|
find_replace(labelpath, ".png", ".txt", labelpath);
|
find_replace(labelpath, ".bmp", ".txt", labelpath);
|
find_replace(labelpath, ".JPG", ".txt", labelpath);
|
find_replace(labelpath, ".JPEG", ".txt", labelpath);
|
int num_labels = 0;
|
box_label *truth = read_boxes(labelpath, &num_labels);
|
//printf(" new path: %s \n", labelpath);
|
for (j = 0; j < num_labels; ++j)
|
{
|
number_of_boxes++;
|
rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float));
|
rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * width;
|
rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * height;
|
printf("\r loaded \t image: %d \t box: %d", i+1, number_of_boxes);
|
}
|
}
|
printf("\n all loaded. \n");
|
|
CvMat* points = cvCreateMat(number_of_boxes, 2, CV_32FC1);
|
CvMat* centers = cvCreateMat(num_of_clusters, 2, CV_32FC1);
|
CvMat* labels = cvCreateMat(number_of_boxes, 1, CV_32SC1);
|
|
for (i = 0; i < number_of_boxes; ++i) {
|
points->data.fl[i * 2] = rel_width_height_array[i * 2];
|
points->data.fl[i * 2 + 1] = rel_width_height_array[i * 2 + 1];
|
//cvSet1D(points, i * 2, cvScalar(rel_width_height_array[i * 2], 0, 0, 0));
|
//cvSet1D(points, i * 2 + 1, cvScalar(rel_width_height_array[i * 2 + 1], 0, 0, 0));
|
}
|
|
|
const int attemps = 10;
|
double compactness;
|
|
enum {
|
KMEANS_RANDOM_CENTERS = 0,
|
KMEANS_USE_INITIAL_LABELS = 1,
|
KMEANS_PP_CENTERS = 2
|
};
|
|
printf("\n calculating k-means++ ...");
|
// Should be used: distance(box, centroid) = 1 - IoU(box, centroid)
|
cvKMeans2(points, num_of_clusters, labels,
|
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10000, 0), attemps,
|
0, KMEANS_PP_CENTERS,
|
centers, &compactness);
|
|
// sort anchors
|
qsort(centers->data.fl, num_of_clusters, 2*sizeof(float), anchors_comparator);
|
|
//orig 2.0 anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
|
//float orig_anch[] = { 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 };
|
// worse than ours (even for 19x19 final size - for input size 608x608)
|
|
//orig anchors = 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071
|
//float orig_anch[] = { 1.3221,1.73145, 3.19275,4.00944, 5.05587,8.09892, 9.47112,4.84053, 11.2364,10.0071 };
|
// orig (IoU=59.90%) better than ours (59.75%)
|
|
//gen_anchors.py = 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66
|
//float orig_anch[] = { 1.19, 1.99, 2.79, 4.60, 4.53, 8.92, 8.06, 5.29, 10.32, 10.66 };
|
|
// ours: anchors = 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595
|
//float orig_anch[] = { 9.3813,6.0095, 3.3999,5.3505, 10.9476,11.1992, 5.0161,9.8314, 1.5003,2.1595 };
|
//for (i = 0; i < num_of_clusters * 2; ++i) centers->data.fl[i] = orig_anch[i];
|
|
//for (i = 0; i < number_of_boxes; ++i)
|
// printf("%2.2f,%2.2f, ", points->data.fl[i * 2], points->data.fl[i * 2 + 1]);
|
|
float avg_iou = 0;
|
for (i = 0; i < number_of_boxes; ++i) {
|
float box_w = points->data.fl[i * 2];
|
float box_h = points->data.fl[i * 2 + 1];
|
//int cluster_idx = labels->data.i[i];
|
int cluster_idx = 0;
|
float min_dist = FLT_MAX;
|
for (j = 0; j < num_of_clusters; ++j) {
|
float anchor_w = centers->data.fl[j * 2];
|
float anchor_h = centers->data.fl[j * 2 + 1];
|
float w_diff = anchor_w - box_w;
|
float h_diff = anchor_h - box_h;
|
float distance = sqrt(w_diff*w_diff + h_diff*h_diff);
|
if (distance < min_dist) min_dist = distance, cluster_idx = j;
|
}
|
|
float anchor_w = centers->data.fl[cluster_idx * 2];
|
float anchor_h = centers->data.fl[cluster_idx * 2 + 1];
|
float min_w = (box_w < anchor_w) ? box_w : anchor_w;
|
float min_h = (box_h < anchor_h) ? box_h : anchor_h;
|
float box_intersect = min_w*min_h;
|
float box_union = box_w*box_h + anchor_w*anchor_h - box_intersect;
|
float iou = box_intersect / box_union;
|
if (iou > 1 || iou < 0) {
|
printf(" i = %d, box_w = %d, box_h = %d, anchor_w = %d, anchor_h = %d, iou = %f \n",
|
i, box_w, box_h, anchor_w, anchor_h, iou);
|
}
|
else avg_iou += iou;
|
}
|
avg_iou = 100 * avg_iou / number_of_boxes;
|
printf("\n avg IoU = %2.2f %% \n", avg_iou);
|
|
char buff[1024];
|
FILE* fw = fopen("anchors.txt", "wb");
|
printf("\nSaving anchors to the file: anchors.txt \n");
|
printf("anchors = ");
|
for (i = 0; i < num_of_clusters; ++i) {
|
sprintf(buff, "%2.4f,%2.4f", centers->data.fl[i * 2], centers->data.fl[i * 2 + 1]);
|
printf("%s", buff);
|
fwrite(buff, sizeof(char), strlen(buff), fw);
|
if (i + 1 < num_of_clusters) {
|
fwrite(", ", sizeof(char), 2, fw);
|
printf(", ");
|
}
|
}
|
printf("\n");
|
fclose(fw);
|
|
if (show) {
|
size_t img_size = 700;
|
IplImage* img = cvCreateImage(cvSize(img_size, img_size), 8, 3);
|
cvZero(img);
|
for (j = 0; j < num_of_clusters; ++j) {
|
CvPoint pt1, pt2;
|
pt1.x = pt1.y = 0;
|
pt2.x = centers->data.fl[j * 2] * img_size / width;
|
pt2.y = centers->data.fl[j * 2 + 1] * img_size / height;
|
cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0);
|
}
|
|
for (i = 0; i < number_of_boxes; ++i) {
|
CvPoint pt;
|
pt.x = points->data.fl[i * 2] * img_size / width;
|
pt.y = points->data.fl[i * 2 + 1] * img_size / height;
|
int cluster_idx = labels->data.i[i];
|
int red_id = (cluster_idx * (uint64_t)123 + 55) % 255;
|
int green_id = (cluster_idx * (uint64_t)321 + 33) % 255;
|
int blue_id = (cluster_idx * (uint64_t)11 + 99) % 255;
|
cvCircle(img, pt, 1, CV_RGB(red_id, green_id, blue_id), CV_FILLED, 8, 0);
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//if(pt.x > img_size || pt.y > img_size) printf("\n pt.x = %d, pt.y = %d \n", pt.x, pt.y);
|
}
|
cvShowImage("clusters", img);
|
cvWaitKey(0);
|
cvReleaseImage(&img);
|
cvDestroyAllWindows();
|
}
|
|
free(rel_width_height_array);
|
cvReleaseMat(&points);
|
cvReleaseMat(¢ers);
|
cvReleaseMat(&labels);
|
}
|
#else
|
void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) {
|
printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n");
|
}
|
#endif // OPENCV
|
|
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, float hier_thresh, int dont_show)
|
{
|
list *options = read_data_cfg(datacfg);
|
char *name_list = option_find_str(options, "names", "data/names.list");
|
char **names = get_labels(name_list);
|
|
image **alphabet = load_alphabet();
|
network net = parse_network_cfg_custom(cfgfile, 1); // set batch=1
|
if(weightfile){
|
load_weights(&net, weightfile);
|
}
|
//set_batch_network(&net, 1);
|
fuse_conv_batchnorm(net);
|
srand(2222222);
|
double time;
|
char buff[256];
|
char *input = buff;
|
int j;
|
float nms=.45; // 0.4F
|
while(1){
|
if(filename){
|
strncpy(input, filename, 256);
|
if(strlen(input) > 0)
|
if (input[strlen(input) - 1] == 0x0d) input[strlen(input) - 1] = 0;
|
} else {
|
printf("Enter Image Path: ");
|
fflush(stdout);
|
input = fgets(input, 256, stdin);
|
if(!input) return;
|
strtok(input, "\n");
|
}
|
|
|
image im = load_image_color(input,0,0);
|
int letterbox = 0;
|
//image sized = resize_image(im, net.w, net.h);
|
|
|
image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
|
layer l = net.layers[net.n-1];
|
|
//box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
|
//float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
|
//for(j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
|
|
struct timeval a, b;
|
gettimeofday(&a, NULL);
|
|
|
float *X = sized.data;
|
time= what_time_is_it_now();
|
network_predict(net, X);
|
//network_predict_image(&net, im); letterbox = 1;
|
printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time));
|
//get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0);
|
// if (nms) do_nms_sort_v2(boxes, probs, l.w*l.h*l.n, l.classes, nms);
|
//draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
|
|
int nboxes = 0;
|
detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
|
|
gettimeofday(&b, NULL);
|
printf("get_network_boxes Use Time: %lu\n", b.tv_sec*1000+b.tv_usec/1000-a.tv_sec*1000-a.tv_usec/1000);
|
|
if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
|
draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes);
|
free_detections(dets, nboxes);
|
save_image(im, "predictions");
|
if (!dont_show) {
|
show_image(im, "predictions");
|
}
|
|
free_image(im);
|
free_image(sized);
|
//free(boxes);
|
//free_ptrs((void **)probs, l.w*l.h*l.n);
|
#ifdef OPENCV
|
if (!dont_show) {
|
cvWaitKey(0);
|
cvDestroyAllWindows();
|
}
|
#endif
|
if (filename) break;
|
}
|
|
// free memory
|
free_ptrs(names, net.layers[net.n - 1].classes);
|
free_list(options);
|
|
int i;
|
const int nsize = 8;
|
for (j = 0; j < nsize; ++j) {
|
for (i = 32; i < 127; ++i) {
|
free_image(alphabet[j][i]);
|
}
|
free(alphabet[j]);
|
}
|
free(alphabet);
|
|
free_network(net);
|
}
|
|
void run_detector(int argc, char **argv)
|
{
|
int dont_show = find_arg(argc, argv, "-dont_show");
|
int show = find_arg(argc, argv, "-show");
|
int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);
|
char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
|
char *outfile = find_char_arg(argc, argv, "-out", 0);
|
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
|
float thresh = find_float_arg(argc, argv, "-thresh", .25); // 0.24
|
float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
|
int cam_index = find_int_arg(argc, argv, "-c", 0);
|
int frame_skip = find_int_arg(argc, argv, "-s", 0);
|
int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
|
int width = find_int_arg(argc, argv, "-width", -1);
|
int height = find_int_arg(argc, argv, "-height", -1);
|
if(argc < 4){
|
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
|
return;
|
}
|
char *gpu_list = find_char_arg(argc, argv, "-gpus", 0);
|
int *gpus = 0;
|
int gpu = 0;
|
int ngpus = 0;
|
if(gpu_list){
|
printf("%s\n", gpu_list);
|
int len = strlen(gpu_list);
|
ngpus = 1;
|
int i;
|
for(i = 0; i < len; ++i){
|
if (gpu_list[i] == ',') ++ngpus;
|
}
|
gpus = calloc(ngpus, sizeof(int));
|
for(i = 0; i < ngpus; ++i){
|
gpus[i] = atoi(gpu_list);
|
gpu_list = strchr(gpu_list, ',')+1;
|
}
|
} else {
|
gpu = gpu_index;
|
gpus = &gpu;
|
ngpus = 1;
|
}
|
|
int clear = find_arg(argc, argv, "-clear");
|
|
char *datacfg = argv[3];
|
char *cfg = argv[4];
|
char *weights = (argc > 5) ? argv[5] : 0;
|
if(weights)
|
if(strlen(weights) > 0)
|
if (weights[strlen(weights) - 1] == 0x0d) weights[strlen(weights) - 1] = 0;
|
char *filename = (argc > 6) ? argv[6]: 0;
|
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show);
|
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show);
|
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights, outfile);
|
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
|
else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights, thresh);
|
else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
|
else if(0==strcmp(argv[2], "demo")) {
|
list *options = read_data_cfg(datacfg);
|
int classes = option_find_int(options, "classes", 20);
|
char *name_list = option_find_str(options, "names", "data/names.list");
|
char **names = get_labels(name_list);
|
if(filename)
|
if(strlen(filename) > 0)
|
if (filename[strlen(filename) - 1] == 0x0d) filename[strlen(filename) - 1] = 0;
|
demo(cfg, weights, thresh, hier_thresh, cam_index, filename, names, classes, frame_skip, prefix, out_filename,
|
http_stream_port, dont_show);
|
}
|
else printf(" There isn't such command: %s", argv[2]);
|
}
|
