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1138 lines
37 KiB
1138 lines
37 KiB
#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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|
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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.jitter = jitter; |
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args.num_boxes = l.max_boxes; |
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args.small_object = l.small_object; |
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args.d = &buffer; |
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args.type = DETECTION_DATA; |
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args.threads = 64; // 8 |
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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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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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clock_t 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=clock(); |
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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", sec(clock()-time)); |
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time=clock(); |
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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 = loss; |
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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("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-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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return atoi(p+1); |
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} |
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static void print_cocos(FILE *fp, char *image_path, 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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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 = 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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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 (probs[i][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, probs[i][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, box *boxes, float **probs, 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 = 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 print_imagenet_detections(FILE *fp, int id, box *boxes, float **probs, 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 = 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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int class_id = j; |
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if (probs[i][class_id]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class_id], |
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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) |
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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); |
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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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char *base = "comp4_det_test_"; |
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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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snprintf(buff, 1024, "%s/coco_results.json", prefix); |
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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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} else if(0==strcmp(type, "imagenet")){ |
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snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix); |
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fp = fopen(buff, "w"); |
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imagenet = 1; |
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classes = 200; |
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} else { |
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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, base, names[j]); |
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fps[j] = fopen(buff, "w"); |
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} |
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} |
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box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); |
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float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); |
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for(j = 0; j < l.w*l.h*l.n; ++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 = .005; |
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float nms = .45; |
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int detection_count = 0; |
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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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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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get_region_boxes(l, w, h, thresh, probs, boxes, 0, map); |
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if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms); |
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int x, y; |
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for (x = 0; x < (l.w*l.h*l.n); ++x) { |
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for (y = 0; y < classes; ++y) |
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{ |
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if (probs[x][y]) ++detection_count; |
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} |
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} |
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if (coco){ |
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print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h); |
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} else if (imagenet){ |
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print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h); |
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} else { |
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print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h); |
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} |
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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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printf("\n detection_count = %d \n", detection_count); |
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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 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); |
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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 *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 classes = l.classes; |
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int j, k; |
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box *boxes = calloc(l.w*l.h*l.n, sizeof(box)); |
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float **probs = calloc(l.w*l.h*l.n, sizeof(float *)); |
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for(j = 0; j < l.w*l.h*l.n; ++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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float thresh = .001;// .001; // .2; |
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float iou_thresh = .5; |
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float nms = .4; |
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int detection_count = 0, truth_count = 0; |
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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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get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0); |
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if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 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, ".JPEG", ".txt", labelpath); |
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find_replace(labelpath, ".png", ".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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truth_count += num_labels; |
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for(k = 0; k < l.w*l.h*l.n; ++k){ |
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if(probs[k][0] > 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 }; |
|
float best_iou = 0; |
|
for (k = 0; k < l.w*l.h*l.n; ++k) { |
|
float iou = box_iou(boxes[k], t); |
|
if (probs[k][0] > thresh && iou > best_iou) { |
|
best_iou = iou; |
|
} |
|
} |
|
avg_iou += best_iou; |
|
if(best_iou > iou_thresh){ |
|
++correct; |
|
} |
|
} |
|
|
|
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); |
|
free(id); |
|
free_image(orig); |
|
free_image(sized); |
|
} |
|
printf("\n truth_count = %d \n", truth_count); |
|
} |
|
|
|
typedef struct { |
|
box b; |
|
float p; |
|
int class_id; |
|
int image_index; |
|
int truth_flag; |
|
int unique_truth_index; |
|
} box_prob; |
|
|
|
int detections_comparator(const void *pa, const void *pb) |
|
{ |
|
box_prob a = *(box_prob *)pa; |
|
box_prob b = *(box_prob *)pb; |
|
float diff = a.p - b.p; |
|
if (diff < 0) return 1; |
|
else if (diff > 0) return -1; |
|
return 0; |
|
} |
|
|
|
void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou) |
|
{ |
|
int j; |
|
list *options = read_data_cfg(datacfg); |
|
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); |
|
if (weightfile) { |
|
load_weights(&net, weightfile); |
|
} |
|
set_batch_network(&net, 1); |
|
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay); |
|
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; |
|
|
|
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(classes, sizeof(float *)); |
|
|
|
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; |
|
|
|
//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); |
|
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map); |
|
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, 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, ".JPEG", ".txt", labelpath); |
|
find_replace(labelpath, ".png", ".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); |
|
} |
|
|
|
for (i = 0; i < (l.w*l.h*l.n); ++i) { |
|
|
|
int class_id; |
|
for (class_id = 0; class_id < classes; ++class_id) { |
|
float prob = probs[i][class_id]; |
|
if (prob > 0) { |
|
detections_count++; |
|
detections = realloc(detections, detections_count * sizeof(box_prob)); |
|
detections[detections_count - 1].b = boxes[i]; |
|
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(boxes[i], t), prob, class_id, truth[j].id); |
|
float current_iou = box_iou(boxes[i], 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(boxes[i], 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) { |
|
if (truth_index > -1) { |
|
avg_iou += max_iou; |
|
++tp_for_thresh; |
|
} |
|
else |
|
fp_for_thresh++; |
|
} |
|
} |
|
} |
|
} |
|
|
|
unique_truth_count += num_labels; |
|
|
|
free(id); |
|
free_image(val[t]); |
|
free_image(val_resized[t]); |
|
} |
|
} |
|
|
|
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 |
|
void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show) |
|
{ |
|
printf("\n num_of_clusters = %d, final_width = %d, final_height = %d \n", num_of_clusters, final_width, final_height); |
|
|
|
//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, ".JPEG", ".txt", labelpath); |
|
find_replace(labelpath, ".png", ".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 * final_width; |
|
rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * final_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); |
|
|
|
//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("\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 / final_width; |
|
pt2.y = centers->data.fl[j * 2 + 1] * img_size / final_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 / final_width; |
|
pt.y = points->data.fl[i * 2 + 1] * img_size / final_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); |
|
//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 final_width, int final_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); |
|
if(weightfile){ |
|
load_weights(&net, weightfile); |
|
} |
|
set_batch_network(&net, 1); |
|
srand(2222222); |
|
clock_t time; |
|
char buff[256]; |
|
char *input = buff; |
|
int j; |
|
float nms=.45; // 0.4F |
|
while(1){ |
|
if(filename){ |
|
strncpy(input, filename, 256); |
|
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 letter = 0; |
|
//image sized = resize_image(im, net.w, net.h); |
|
image sized = letterbox_image(im, net.w, net.h); letter = 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 *)); |
|
|
|
float *X = sized.data; |
|
time=clock(); |
|
network_predict(net, X); |
|
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time)); |
|
//get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, 0); |
|
// if (nms) do_nms_sort(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, letter); |
|
if (nms) do_nms_sort_v3(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; |
|
} |
|
} |
|
|
|
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 *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 final_width = find_int_arg(argc, argv, "-final_width", 13); |
|
int final_heigh = find_int_arg(argc, argv, "-final_heigh", 13); |
|
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 (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); |
|
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, final_width, final_heigh, 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 (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); |
|
} |
|
}
|
|
|