From 0126e953b3f293b111179e4777103c64f778870c Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 17 六月 2021 09:57:28 +0800
Subject: [PATCH] bug
---
lib/detecter_tools/darknet/detector.c | 4012 ++++++++++++++++++++++++++++++-----------------------------
1 files changed, 2,042 insertions(+), 1,970 deletions(-)
diff --git a/lib/detecter_tools/darknet/detector.c b/lib/detecter_tools/darknet/detector.c
index 4977e9d..a2fdf0b 100644
--- a/lib/detecter_tools/darknet/detector.c
+++ b/lib/detecter_tools/darknet/detector.c
@@ -1,1970 +1,2042 @@
-#include <stdlib.h>
-#include "darknet.h"
-#include "network.h"
-#include "region_layer.h"
-#include "cost_layer.h"
-#include "utils.h"
-#include "parser.h"
-#include "box.h"
-#include "demo.h"
-#include "option_list.h"
-
-#ifndef __COMPAR_FN_T
-#define __COMPAR_FN_T
-typedef int (*__compar_fn_t)(const void*, const void*);
-#ifdef __USE_GNU
-typedef __compar_fn_t comparison_fn_t;
-#endif
-#endif
-
-#include "http_stream.h"
-
-int check_mistakes = 0;
-
-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 };
-
-void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path)
-{
- list *options = read_data_cfg(datacfg);
- char *train_images = option_find_str(options, "train", "data/train.txt");
- char *valid_images = option_find_str(options, "valid", train_images);
- char *backup_directory = option_find_str(options, "backup", "/backup/");
-
- network net_map;
- if (calc_map) {
- FILE* valid_file = fopen(valid_images, "r");
- if (!valid_file) {
- printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg);
- getchar();
- exit(-1);
- }
- else fclose(valid_file);
-
- cuda_set_device(gpus[0]);
- printf(" Prepare additional network for mAP calculation...\n");
- net_map = parse_network_cfg_custom(cfgfile, 1, 1);
- net_map.benchmark_layers = benchmark_layers;
- const int net_classes = net_map.layers[net_map.n - 1].classes;
-
- int k; // free memory unnecessary arrays
- for (k = 0; k < net_map.n - 1; ++k) free_layer_custom(net_map.layers[k], 1);
-
- char *name_list = option_find_str(options, "names", "data/names.list");
- int names_size = 0;
- char **names = get_labels_custom(name_list, &names_size);
- if (net_classes != names_size) {
- printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
- name_list, names_size, net_classes, cfgfile);
- if (net_classes > names_size) getchar();
- }
- free_ptrs((void**)names, net_map.layers[net_map.n - 1].classes);
- }
-
- srand(time(0));
- char *base = basecfg(cfgfile);
- printf("%s\n", base);
- float avg_loss = -1;
- network* nets = (network*)xcalloc(ngpus, sizeof(network));
-
- srand(time(0));
- int seed = rand();
- int k;
- for (k = 0; k < ngpus; ++k) {
- srand(seed);
-#ifdef GPU
- cuda_set_device(gpus[k]);
-#endif
- nets[k] = parse_network_cfg(cfgfile);
- nets[k].benchmark_layers = benchmark_layers;
- if (weightfile) {
- load_weights(&nets[k], weightfile);
- }
- if (clear) {
- *nets[k].seen = 0;
- *nets[k].cur_iteration = 0;
- }
- nets[k].learning_rate *= ngpus;
- }
- srand(time(0));
- network net = nets[0];
-
- const int actual_batch_size = net.batch * net.subdivisions;
- if (actual_batch_size == 1) {
- printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n");
- getchar();
- }
- else if (actual_batch_size < 8) {
- printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size);
- }
-
- int imgs = net.batch * net.subdivisions * ngpus;
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- data train, buffer;
-
- layer l = net.layers[net.n - 1];
-
- int classes = l.classes;
-
- list *plist = get_paths(train_images);
- int train_images_num = plist->size;
- char **paths = (char **)list_to_array(plist);
-
- const int init_w = net.w;
- const int init_h = net.h;
- const int init_b = net.batch;
- int iter_save, iter_save_last, iter_map;
- iter_save = get_current_iteration(net);
- iter_save_last = get_current_iteration(net);
- iter_map = get_current_iteration(net);
- float mean_average_precision = -1;
- float best_map = mean_average_precision;
-
- load_args args = { 0 };
- args.w = net.w;
- args.h = net.h;
- args.c = net.c;
- args.paths = paths;
- args.n = imgs;
- args.m = plist->size;
- args.classes = classes;
- args.flip = net.flip;
- args.jitter = l.jitter;
- args.resize = l.resize;
- args.num_boxes = l.max_boxes;
- net.num_boxes = args.num_boxes;
- net.train_images_num = train_images_num;
- args.d = &buffer;
- args.type = DETECTION_DATA;
- args.threads = 64; // 16 or 64
-
- args.angle = net.angle;
- args.gaussian_noise = net.gaussian_noise;
- args.blur = net.blur;
- args.mixup = net.mixup;
- args.exposure = net.exposure;
- args.saturation = net.saturation;
- args.hue = net.hue;
- args.letter_box = net.letter_box;
- if (dont_show && show_imgs) show_imgs = 2;
- args.show_imgs = show_imgs;
-
-#ifdef OPENCV
- //int num_threads = get_num_threads();
- //if(num_threads > 2) args.threads = get_num_threads() - 2;
- args.threads = 6 * ngpus; // 3 for - Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge
- //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores)
- mat_cv* img = NULL;
- float max_img_loss = 5;
- int number_of_lines = 100;
- int img_size = 1000;
- char windows_name[100];
- sprintf(windows_name, "chart_%s.png", base);
- img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path);
-#endif //OPENCV
- if (net.track) {
- args.track = net.track;
- args.augment_speed = net.augment_speed;
- if (net.sequential_subdivisions) args.threads = net.sequential_subdivisions * ngpus;
- else args.threads = net.subdivisions * ngpus;
- args.mini_batch = net.batch / net.time_steps;
- printf("\n Tracking! batch = %d, subdiv = %d, time_steps = %d, mini_batch = %d \n", net.batch, net.subdivisions, net.time_steps, args.mini_batch);
- }
- //printf(" imgs = %d \n", imgs);
-
- pthread_t load_thread = load_data(args);
-
- int count = 0;
- double time_remaining, avg_time = -1, alpha_time = 0.01;
-
- //while(i*imgs < N*120){
- while (get_current_iteration(net) < net.max_batches) {
- if (l.random && count++ % 10 == 0) {
- float rand_coef = 1.4;
- if (l.random != 1.0) rand_coef = l.random;
- printf("Resizing, random_coef = %.2f \n", rand_coef);
- float random_val = rand_scale(rand_coef); // *x or /x
- int dim_w = roundl(random_val*init_w / net.resize_step + 1) * net.resize_step;
- int dim_h = roundl(random_val*init_h / net.resize_step + 1) * net.resize_step;
- if (random_val < 1 && (dim_w > init_w || dim_h > init_h)) dim_w = init_w, dim_h = init_h;
-
- int max_dim_w = roundl(rand_coef*init_w / net.resize_step + 1) * net.resize_step;
- int max_dim_h = roundl(rand_coef*init_h / net.resize_step + 1) * net.resize_step;
-
- // at the beginning (check if enough memory) and at the end (calc rolling mean/variance)
- if (avg_loss < 0 || get_current_iteration(net) > net.max_batches - 100) {
- dim_w = max_dim_w;
- dim_h = max_dim_h;
- }
-
- if (dim_w < net.resize_step) dim_w = net.resize_step;
- if (dim_h < net.resize_step) dim_h = net.resize_step;
- int dim_b = (init_b * max_dim_w * max_dim_h) / (dim_w * dim_h);
- int new_dim_b = (int)(dim_b * 0.8);
- if (new_dim_b > init_b) dim_b = new_dim_b;
-
- args.w = dim_w;
- args.h = dim_h;
-
- int k;
- if (net.dynamic_minibatch) {
- for (k = 0; k < ngpus; ++k) {
- (*nets[k].seen) = init_b * net.subdivisions * get_current_iteration(net); // remove this line, when you will save to weights-file both: seen & cur_iteration
- nets[k].batch = dim_b;
- int j;
- for (j = 0; j < nets[k].n; ++j)
- nets[k].layers[j].batch = dim_b;
- }
- net.batch = dim_b;
- imgs = net.batch * net.subdivisions * ngpus;
- args.n = imgs;
- printf("\n %d x %d (batch = %d) \n", dim_w, dim_h, net.batch);
- }
- else
- printf("\n %d x %d \n", dim_w, dim_h);
-
- pthread_join(load_thread, 0);
- train = buffer;
- free_data(train);
- load_thread = load_data(args);
-
- for (k = 0; k < ngpus; ++k) {
- resize_network(nets + k, dim_w, dim_h);
- }
- net = nets[0];
- }
- double time = what_time_is_it_now();
- pthread_join(load_thread, 0);
- train = buffer;
- if (net.track) {
- net.sequential_subdivisions = get_current_seq_subdivisions(net);
- args.threads = net.sequential_subdivisions * ngpus;
- printf(" sequential_subdivisions = %d, sequence = %d \n", net.sequential_subdivisions, get_sequence_value(net));
- }
- load_thread = load_data(args);
-
- /*
- int k;
- for(k = 0; k < l.max_boxes; ++k){
- box b = float_to_box(train.y.vals[10] + 1 + k*5);
- if(!b.x) break;
- printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
- }
- image im = float_to_image(448, 448, 3, train.X.vals[10]);
- int k;
- for(k = 0; k < l.max_boxes; ++k){
- box b = float_to_box(train.y.vals[10] + 1 + k*5);
- printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
- draw_bbox(im, b, 8, 1,0,0);
- }
- save_image(im, "truth11");
- */
-
- const double load_time = (what_time_is_it_now() - time);
- printf("Loaded: %lf seconds", load_time);
- if (load_time > 0.1 && avg_loss > 0) printf(" - performance bottleneck on CPU or Disk HDD/SSD");
- printf("\n");
-
- time = what_time_is_it_now();
- float loss = 0;
-#ifdef GPU
- if (ngpus == 1) {
- int wait_key = (dont_show) ? 0 : 1;
- loss = train_network_waitkey(net, train, wait_key);
- }
- else {
- loss = train_networks(nets, ngpus, train, 4);
- }
-#else
- loss = train_network(net, train);
-#endif
- if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan)
- avg_loss = avg_loss*.9 + loss*.1;
-
- const int iteration = get_current_iteration(net);
- //i = get_current_batch(net);
-
- int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs
- calc_map_for_each = fmax(calc_map_for_each, 100);
- int next_map_calc = iter_map + calc_map_for_each;
- next_map_calc = fmax(next_map_calc, net.burn_in);
- //next_map_calc = fmax(next_map_calc, 400);
- if (calc_map) {
- printf("\n (next mAP calculation at %d iterations) ", next_map_calc);
- if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %%, best = %2.2f %% ", mean_average_precision * 100, best_map * 100);
- }
-
- if (net.cudnn_half) {
- if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
- else fprintf(stderr, "\n Tensor Cores are used.\n");
- fflush(stderr);
- }
- printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images, %f hours left\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs, avg_time);
- fflush(stdout);
-
- int draw_precision = 0;
- if (calc_map && (iteration >= next_map_calc || iteration == net.max_batches)) {
- if (l.random) {
- printf("Resizing to initial size: %d x %d ", init_w, init_h);
- args.w = init_w;
- args.h = init_h;
- int k;
- if (net.dynamic_minibatch) {
- for (k = 0; k < ngpus; ++k) {
- for (k = 0; k < ngpus; ++k) {
- nets[k].batch = init_b;
- int j;
- for (j = 0; j < nets[k].n; ++j)
- nets[k].layers[j].batch = init_b;
- }
- }
- net.batch = init_b;
- imgs = init_b * net.subdivisions * ngpus;
- args.n = imgs;
- printf("\n %d x %d (batch = %d) \n", init_w, init_h, init_b);
- }
- pthread_join(load_thread, 0);
- free_data(train);
- train = buffer;
- load_thread = load_data(args);
- for (k = 0; k < ngpus; ++k) {
- resize_network(nets + k, init_w, init_h);
- }
- net = nets[0];
- }
-
- copy_weights_net(net, &net_map);
-
- // combine Training and Validation networks
- //network net_combined = combine_train_valid_networks(net, net_map);
-
- iter_map = iteration;
- mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined);
- printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision);
- if (mean_average_precision > best_map) {
- best_map = mean_average_precision;
- printf("New best mAP!\n");
- char buff[256];
- sprintf(buff, "%s/%s_best.weights", backup_directory, base);
- save_weights(net, buff);
- }
-
- draw_precision = 1;
- }
- time_remaining = ((net.max_batches - iteration) / ngpus)*(what_time_is_it_now() - time + load_time) / 60 / 60;
- // set initial value, even if resume training from 10000 iteration
- if (avg_time < 0) avg_time = time_remaining;
- else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time;
-#ifdef OPENCV
- draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port, avg_time);
-#endif // OPENCV
-
- //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
- //if (i % 100 == 0) {
- if (iteration >= (iter_save + 1000) || iteration % 1000 == 0) {
- iter_save = iteration;
-#ifdef GPU
- if (ngpus != 1) sync_nets(nets, ngpus, 0);
-#endif
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights", backup_directory, base, iteration);
- save_weights(net, buff);
- }
-
- if (iteration >= (iter_save_last + 100) || (iteration % 100 == 0 && iteration > 1)) {
- iter_save_last = iteration;
-#ifdef GPU
- if (ngpus != 1) sync_nets(nets, ngpus, 0);
-#endif
- char buff[256];
- sprintf(buff, "%s/%s_last.weights", backup_directory, base);
- save_weights(net, buff);
- }
- free_data(train);
- }
-#ifdef GPU
- if (ngpus != 1) sync_nets(nets, ngpus, 0);
-#endif
- char buff[256];
- sprintf(buff, "%s/%s_final.weights", backup_directory, base);
- save_weights(net, buff);
-
-#ifdef OPENCV
- release_mat(&img);
- destroy_all_windows_cv();
-#endif
-
- // free memory
- pthread_join(load_thread, 0);
- free_data(buffer);
-
- free_load_threads(&args);
-
- free(base);
- free(paths);
- free_list_contents(plist);
- free_list(plist);
-
- free_list_contents_kvp(options);
- free_list(options);
-
- for (k = 0; k < ngpus; ++k) free_network(nets[k]);
- free(nets);
- //free_network(net);
-
- if (calc_map) {
- net_map.n = 0;
- free_network(net_map);
- }
-}
-
-
-static int get_coco_image_id(char *filename)
-{
- char *p = strrchr(filename, '/');
- char *c = strrchr(filename, '_');
- if (c) p = c;
- return atoi(p + 1);
-}
-
-static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
-{
- int i, j;
- //int image_id = get_coco_image_id(image_path);
- char *p = basecfg(image_path);
- int image_id = atoi(p);
- for (i = 0; i < num_boxes; ++i) {
- float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
- float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
- float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
- float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
-
- if (xmin < 0) xmin = 0;
- if (ymin < 0) ymin = 0;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
-
- float bx = xmin;
- float by = ymin;
- float bw = xmax - xmin;
- float bh = ymax - ymin;
-
- for (j = 0; j < classes; ++j) {
- if (dets[i].prob[j] > 0) {
- char buff[1024];
- sprintf(buff, "{\"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]);
- fprintf(fp, buff);
- //printf("%s", buff);
- }
- }
- }
-}
-
-void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
-{
- int i, j;
- for (i = 0; i < total; ++i) {
- float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1;
- float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1;
- float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1;
- float ymax = dets[i].bbox.y + dets[i].bbox.h / 2. + 1;
-
- if (xmin < 1) xmin = 1;
- if (ymin < 1) ymin = 1;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
-
- for (j = 0; j < classes; ++j) {
- if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
- xmin, ymin, xmax, ymax);
- }
- }
-}
-
-void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
-{
- int i, j;
- for (i = 0; i < total; ++i) {
- float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
- float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
- float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
- float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
-
- if (xmin < 0) xmin = 0;
- if (ymin < 0) ymin = 0;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
-
- for (j = 0; j < classes; ++j) {
- int myclass = j;
- if (dets[i].prob[myclass] > 0) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[myclass],
- xmin, ymin, xmax, ymax);
- }
- }
-}
-
-static void print_kitti_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h, char *outfile, char *prefix)
-{
- char *kitti_ids[] = { "car", "pedestrian", "cyclist" };
- FILE *fpd = 0;
- char buffd[1024];
- snprintf(buffd, 1024, "%s/%s/data/%s.txt", prefix, outfile, id);
-
- fpd = fopen(buffd, "w");
- int i, j;
- for (i = 0; i < total; ++i)
- {
- float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
- float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
- float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
- float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
-
- if (xmin < 0) xmin = 0;
- if (ymin < 0) ymin = 0;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
-
- for (j = 0; j < classes; ++j)
- {
- //if (dets[i].prob[j]) fprintf(fpd, "%s 0 0 0 %f %f %f %f -1 -1 -1 -1 0 0 0 %f\n", kitti_ids[j], xmin, ymin, xmax, ymax, dets[i].prob[j]);
- if (dets[i].prob[j]) fprintf(fpd, "%s -1 -1 -10 %f %f %f %f -1 -1 -1 -1000 -1000 -1000 -10 %f\n", kitti_ids[j], xmin, ymin, xmax, ymax, dets[i].prob[j]);
- }
- }
- fclose(fpd);
-}
-
-static void eliminate_bdd(char *buf, char *a)
-{
- int n = 0;
- int i, k;
- for (i = 0; buf[i] != '\0'; i++)
- {
- if (buf[i] == a[n])
- {
- k = i;
- while (buf[i] == a[n])
- {
- if (a[++n] == '\0')
- {
- for (k; buf[k + n] != '\0'; k++)
- {
- buf[k] = buf[k + n];
- }
- buf[k] = '\0';
- break;
- }
- i++;
- }
- n = 0; i--;
- }
- }
-}
-
-static void get_bdd_image_id(char *filename)
-{
- char *p = strrchr(filename, '/');
- eliminate_bdd(p, ".jpg");
- eliminate_bdd(p, "/");
- strcpy(filename, p);
-}
-
-static void print_bdd_detections(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
-{
- char *bdd_ids[] = { "bike" , "bus" , "car" , "motor" ,"person", "rider", "traffic light", "traffic sign", "train", "truck" };
- get_bdd_image_id(image_path);
- int i, j;
-
- for (i = 0; i < num_boxes; ++i)
- {
- float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
- float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
- float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
- float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
-
- if (xmin < 0) xmin = 0;
- if (ymin < 0) ymin = 0;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
-
- float bx1 = xmin;
- float by1 = ymin;
- float bx2 = xmax;
- float by2 = ymax;
-
- for (j = 0; j < classes; ++j)
- {
- if (dets[i].prob[j])
- {
- fprintf(fp, "\t{\n\t\t\"name\":\"%s\",\n\t\t\"category\":\"%s\",\n\t\t\"bbox\":[%f, %f, %f, %f],\n\t\t\"score\":%f\n\t},\n", image_path, bdd_ids[j], bx1, by1, bx2, by2, dets[i].prob[j]);
- }
- }
- }
-}
-
-void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
-{
- int j;
- list *options = read_data_cfg(datacfg);
- char *valid_images = option_find_str(options, "valid", "data/train.list");
- char *name_list = option_find_str(options, "names", "data/names.list");
- char *prefix = option_find_str(options, "results", "results");
- 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, 1); // set batch=1
- if (weightfile) {
- load_weights(&net, weightfile);
- }
- //set_batch_network(&net, 1);
- fuse_conv_batchnorm(net);
- calculate_binary_weights(net);
- 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);
-
- layer l = net.layers[net.n - 1];
- int classes = l.classes;
-
- char buff[1024];
- char *type = option_find_str(options, "eval", "voc");
- FILE *fp = 0;
- FILE **fps = 0;
- int coco = 0;
- int imagenet = 0;
- int bdd = 0;
- int kitti = 0;
-
- if (0 == strcmp(type, "coco")) {
- if (!outfile) outfile = "coco_results";
- snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
- fp = fopen(buff, "w");
- fprintf(fp, "[\n");
- coco = 1;
- }
- else if (0 == strcmp(type, "bdd")) {
- if (!outfile) outfile = "bdd_results";
- snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
- fp = fopen(buff, "w");
- fprintf(fp, "[\n");
- bdd = 1;
- }
- else if (0 == strcmp(type, "kitti")) {
- char buff2[1024];
- if (!outfile) outfile = "kitti_results";
- printf("%s\n", outfile);
- snprintf(buff, 1024, "%s/%s", prefix, outfile);
- int mkd = make_directory(buff, 0777);
- snprintf(buff2, 1024, "%s/%s/data", prefix, outfile);
- int mkd2 = make_directory(buff2, 0777);
- kitti = 1;
- }
- else if (0 == strcmp(type, "imagenet")) {
- if (!outfile) outfile = "imagenet-detection";
- snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
- fp = fopen(buff, "w");
- imagenet = 1;
- classes = 200;
- }
- else {
- if (!outfile) outfile = "comp4_det_test_";
- fps = (FILE**) xcalloc(classes, sizeof(FILE *));
- for (j = 0; j < classes; ++j) {
- snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
- fps[j] = fopen(buff, "w");
- }
- }
-
-
- int m = plist->size;
- int i = 0;
- int t;
-
- float thresh = .001;
- float nms = .45;
-
- int nthreads = 4;
- if (m < 4) nthreads = m;
- image* val = (image*)xcalloc(nthreads, sizeof(image));
- image* val_resized = (image*)xcalloc(nthreads, sizeof(image));
- image* buf = (image*)xcalloc(nthreads, sizeof(image));
- image* buf_resized = (image*)xcalloc(nthreads, sizeof(image));
- pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t));
-
- load_args args = { 0 };
- args.w = net.w;
- args.h = net.h;
- args.c = net.c;
- args.type = IMAGE_DATA;
- const int letter_box = net.letter_box;
- if (letter_box) args.type = LETTERBOX_DATA;
-
- 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) {
- char *path = paths[i + t - nthreads];
- char *id = basecfg(path);
- float *X = val_resized[t].data;
- network_predict(net, X);
- int w = val[t].w;
- int h = val[t].h;
- int nboxes = 0;
- detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letter_box);
- if (nms) {
- if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
- else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
- }
-
- if (coco) {
- print_cocos(fp, path, dets, nboxes, classes, w, h);
- }
- else if (imagenet) {
- print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h);
- }
- else if (bdd) {
- print_bdd_detections(fp, path, dets, nboxes, classes, w, h);
- }
- else if (kitti) {
- print_kitti_detections(fps, id, dets, nboxes, classes, w, h, outfile, prefix);
- }
- else {
- print_detector_detections(fps, id, dets, nboxes, classes, w, h);
- }
-
- free_detections(dets, nboxes);
- free(id);
- free_image(val[t]);
- free_image(val_resized[t]);
- }
- }
- if (fps) {
- for (j = 0; j < classes; ++j) {
- fclose(fps[j]);
- }
- free(fps);
- }
- if (coco) {
-#ifdef WIN32
- fseek(fp, -3, SEEK_CUR);
-#else
- fseek(fp, -2, SEEK_CUR);
-#endif
- fprintf(fp, "\n]\n");
- }
-
- if (bdd) {
-#ifdef WIN32
- fseek(fp, -3, SEEK_CUR);
-#else
- fseek(fp, -2, SEEK_CUR);
-#endif
- fprintf(fp, "\n]\n");
- fclose(fp);
- }
-
- if (fp) fclose(fp);
-
- if (val) free(val);
- if (val_resized) free(val_resized);
- if (thr) free(thr);
- if (buf) free(buf);
- if (buf_resized) free(buf_resized);
-
- fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start);
-}
-
-void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
-{
- network net = parse_network_cfg_custom(cfgfile, 1, 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("data/coco_val_5k.list");
- list *options = read_data_cfg(datacfg);
- char *valid_images = option_find_str(options, "valid", "data/train.txt");
- list *plist = get_paths(valid_images);
- char **paths = (char **)list_to_array(plist);
-
- //layer l = net.layers[net.n - 1];
-
- int j, k;
-
- int m = plist->size;
- int i = 0;
-
- float thresh = .001;
- float iou_thresh = .5;
- float nms = .4;
-
- int total = 0;
- int correct = 0;
- int proposals = 0;
- float avg_iou = 0;
-
- for (i = 0; i < m; ++i) {
- char *path = paths[i];
- image orig = load_image(path, 0, 0, net.c);
- image sized = resize_image(orig, net.w, net.h);
- char *id = basecfg(path);
- network_predict(net, sized.data);
- int nboxes = 0;
- int letterbox = 0;
- detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox);
- if (nms) do_nms_obj(dets, nboxes, 1, nms);
-
- char labelpath[4096];
- replace_image_to_label(path, labelpath);
-
- int num_labels = 0;
- box_label *truth = read_boxes(labelpath, &num_labels);
- for (k = 0; k < nboxes; ++k) {
- if (dets[k].objectness > thresh) {
- ++proposals;
- }
- }
- for (j = 0; j < num_labels; ++j) {
- ++total;
- box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
- float best_iou = 0;
- for (k = 0; k < nboxes; ++k) {
- float iou = box_iou(dets[k].bbox, t);
- if (dets[k].objectness > thresh && iou > best_iou) {
- best_iou = iou;
- }
- }
- avg_iou += best_iou;
- if (best_iou > iou_thresh) {
- ++correct;
- }
- }
- //fprintf(stderr, " %s - %s - ", paths[i], labelpath);
- 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);
- }
-}
-
-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 = *(const box_prob *)pa;
- box_prob b = *(const box_prob *)pb;
- float diff = a.p - b.p;
- if (diff < 0) return 1;
- else if (diff > 0) return -1;
- return 0;
-}
-
-float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net)
-{
- 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");
- int names_size = 0;
- char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
- //char *mapf = option_find_str(options, "map", 0);
- //int *map = 0;
- //if (mapf) map = read_map(mapf);
- FILE* reinforcement_fd = NULL;
-
- network net;
- //int initial_batch;
- if (existing_net) {
- char *train_images = option_find_str(options, "train", "data/train.txt");
- valid_images = option_find_str(options, "valid", train_images);
- net = *existing_net;
- remember_network_recurrent_state(*existing_net);
- free_network_recurrent_state(*existing_net);
- }
- else {
- net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
- if (weightfile) {
- load_weights(&net, weightfile);
- }
- //set_batch_network(&net, 1);
- fuse_conv_batchnorm(net);
- calculate_binary_weights(net);
- }
- if (net.layers[net.n - 1].classes != names_size) {
- printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
- name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
- getchar();
- }
- srand(time(0));
- printf("\n calculation mAP (mean average precision)...\n");
-
- 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;
- if (m < 4) nthreads = m;
- image* val = (image*)xcalloc(nthreads, sizeof(image));
- image* val_resized = (image*)xcalloc(nthreads, sizeof(image));
- image* buf = (image*)xcalloc(nthreads, sizeof(image));
- image* buf_resized = (image*)xcalloc(nthreads, sizeof(image));
- pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t));
-
- load_args args = { 0 };
- args.w = net.w;
- args.h = net.h;
- args.c = net.c;
- if (letter_box) args.type = LETTERBOX_DATA;
- else 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 = (box_prob*)xcalloc(1, sizeof(box_prob));
- int detections_count = 0;
- int unique_truth_count = 0;
-
- int* truth_classes_count = (int*)xcalloc(classes, sizeof(int));
-
- // For multi-class precision and recall computation
- float *avg_iou_per_class = (float*)xcalloc(classes, sizeof(float));
- int *tp_for_thresh_per_class = (int*)xcalloc(classes, sizeof(int));
- int *fp_for_thresh_per_class = (int*)xcalloc(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, "\r%d", 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;
- float hier_thresh = 0;
- detection *dets;
- if (args.type == LETTERBOX_DATA) {
- dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box);
- }
- else {
- dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letter_box);
- }
- //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); // for letter_box=1
- if (nms) {
- if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
- else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
- }
- //if (nms) do_nms_obj(dets, nboxes, l.classes, nms);
-
- char labelpath[4096];
- replace_image_to_label(path, labelpath);
- int num_labels = 0;
- box_label *truth = read_boxes(labelpath, &num_labels);
- int 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];
- replace_image_to_label(path_dif, labelpath_dif);
-
- truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
- }
-
- const int checkpoint_detections_count = detections_count;
-
- int i;
- 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 = (box_prob*)xrealloc(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;
- avg_iou_per_class[class_id] += max_iou;
- tp_for_thresh_per_class[class_id]++;
- }
- else{
- fp_for_thresh++;
- fp_for_thresh_per_class[class_id]++;
- }
- }
- }
- }
- }
-
- unique_truth_count += num_labels;
-
- //static int previous_errors = 0;
- //int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh);
- //int errors_in_this_image = total_errors - previous_errors;
- //previous_errors = total_errors;
- //if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb");
- //char buff[1000];
- //sprintf(buff, "%s\n", path);
- //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd);
-
- free_detections(dets, nboxes);
- free(id);
- free_image(val[t]);
- free_image(val_resized[t]);
- }
- }
-
- //for (t = 0; t < nthreads; ++t) {
- // pthread_join(thr[t], 0);
- //}
-
- if ((tp_for_thresh + fp_for_thresh) > 0)
- avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
-
- int class_id;
- for(class_id = 0; class_id < classes; class_id++){
- if ((tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]) > 0)
- avg_iou_per_class[class_id] = avg_iou_per_class[class_id] / (tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]);
- }
-
- // 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 = (pr_t**)xcalloc(classes, sizeof(pr_t*));
- for (i = 0; i < classes; ++i) {
- pr[i] = (pr_t*)xcalloc(detections_count, sizeof(pr_t));
- }
- printf("\n detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count);
-
-
- int* detection_per_class_count = (int*)xcalloc(classes, sizeof(int));
- for (j = 0; j < detections_count; ++j) {
- detection_per_class_count[detections[j].class_id]++;
- }
-
- int* truth_flags = (int*)xcalloc(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++;
- }
- 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;
-
- if (rank == (detections_count - 1) && detection_per_class_count[i] != (tp + fp)) { // check for last rank
- printf(" class_id: %d - detections = %d, tp+fp = %d, tp = %d, fp = %d \n", i, detection_per_class_count[i], tp+fp, tp, fp);
- }
- }
- }
-
- free(truth_flags);
-
-
- double mean_average_precision = 0;
-
- for (i = 0; i < classes; ++i) {
- double avg_precision = 0;
-
- // MS COCO - uses 101-Recall-points on PR-chart.
- // PascalVOC2007 - uses 11-Recall-points on PR-chart.
- // PascalVOC2010-2012 - uses Area-Under-Curve on PR-chart.
- // ImageNet - uses Area-Under-Curve on PR-chart.
-
- // correct mAP calculation: ImageNet, PascalVOC 2010-2012
- if (map_points == 0)
- {
- double last_recall = pr[i][detections_count - 1].recall;
- double last_precision = pr[i][detections_count - 1].precision;
- for (rank = detections_count - 2; rank >= 0; --rank)
- {
- double delta_recall = last_recall - pr[i][rank].recall;
- last_recall = pr[i][rank].recall;
-
- if (pr[i][rank].precision > last_precision) {
- last_precision = pr[i][rank].precision;
- }
-
- avg_precision += delta_recall * last_precision;
- }
- }
- // MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points
- else
- {
- int point;
- for (point = 0; point < map_points; ++point) {
- double cur_recall = point * 1.0 / (map_points-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 / map_points;
- }
-
- printf("class_id = %d, name = %s, ap = %2.2f%% \t (TP = %d, FP = %d) \n",
- i, names[i], avg_precision * 100, tp_for_thresh_per_class[i], fp_for_thresh_per_class[i]);
-
- float class_precision = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);
- float class_recall = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));
- //printf("Precision = %1.2f, Recall = %1.2f, avg IOU = %2.2f%% \n\n", class_precision, class_recall, avg_iou_per_class[i]);
-
- 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("\n for conf_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 conf_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 IoU threshold = %2.0f %%, ", iou_thresh * 100);
- if (map_points) printf("used %d Recall-points \n", map_points);
- else printf("used Area-Under-Curve for each unique Recall \n");
-
- printf(" mean average precision (mAP@%0.2f) = %f, or %2.2f %% \n", iou_thresh, mean_average_precision, mean_average_precision * 100);
-
- for (i = 0; i < classes; ++i) {
- free(pr[i]);
- }
- free(pr);
- free(detections);
- free(truth_classes_count);
- free(detection_per_class_count);
-
- free(avg_iou_per_class);
- free(tp_for_thresh_per_class);
- free(fp_for_thresh_per_class);
-
- fprintf(stderr, "Total Detection Time: %d Seconds\n", (int)(time(0) - start));
- printf("\nSet -points flag:\n");
- printf(" `-points 101` for MS COCO \n");
- printf(" `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n");
- printf(" `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n");
- if (reinforcement_fd != NULL) fclose(reinforcement_fd);
-
- // free memory
- free_ptrs((void**)names, net.layers[net.n - 1].classes);
- free_list_contents_kvp(options);
- free_list(options);
-
- if (existing_net) {
- //set_batch_network(&net, initial_batch);
- //free_network_recurrent_state(*existing_net);
- restore_network_recurrent_state(*existing_net);
- //randomize_network_recurrent_state(*existing_net);
- }
- else {
- free_network(net);
- }
- if (val) free(val);
- if (val_resized) free(val_resized);
- if (thr) free(thr);
- if (buf) free(buf);
- if (buf_resized) free(buf_resized);
-
- return mean_average_precision;
-}
-
-typedef struct {
- float w, h;
-} anchors_t;
-
-int anchors_comparator(const void *pa, const void *pb)
-{
- anchors_t a = *(const anchors_t *)pa;
- anchors_t b = *(const 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;
-}
-
-int anchors_data_comparator(const float **pa, const float **pb)
-{
- float *a = (float *)*pa;
- float *b = (float *)*pb;
- float diff = b[0] * b[1] - a[0] * a[1];
- 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 = (float*)xcalloc(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 classes = option_find_int(options, "classes", 1);
- int* counter_per_class = (int*)xcalloc(classes, sizeof(int));
-
- srand(time(0));
- 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];
- replace_image_to_label(path, labelpath);
-
- int num_labels = 0;
- box_label *truth = read_boxes(labelpath, &num_labels);
- //printf(" new path: %s \n", labelpath);
- char *buff = (char*)xcalloc(6144, sizeof(char));
- for (j = 0; j < num_labels; ++j)
- {
- if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 ||
- truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0)
- {
- printf("\n\nWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f \n",
- labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
- sprintf(buff, "echo \"Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f\" >> bad_label.list",
- labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
- system(buff);
- if (check_mistakes) getchar();
- }
- if (truth[j].id >= classes) {
- classes = truth[j].id + 1;
- counter_per_class = (int*)xrealloc(counter_per_class, classes * sizeof(int));
- }
- counter_per_class[truth[j].id]++;
-
- number_of_boxes++;
- rel_width_height_array = (float*)xrealloc(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);
- }
- free(buff);
- }
- printf("\n all loaded. \n");
- printf("\n calculating k-means++ ...");
-
- matrix boxes_data;
- model anchors_data;
- boxes_data = make_matrix(number_of_boxes, 2);
-
- printf("\n");
- for (i = 0; i < number_of_boxes; ++i) {
- boxes_data.vals[i][0] = rel_width_height_array[i * 2];
- boxes_data.vals[i][1] = rel_width_height_array[i * 2 + 1];
- //if (w > 410 || h > 410) printf("i:%d, w = %f, h = %f \n", i, w, h);
- }
-
- // Is used: distance(box, centroid) = 1 - IoU(box, centroid)
-
- // K-means
- anchors_data = do_kmeans(boxes_data, num_of_clusters);
-
- qsort((void*)anchors_data.centers.vals, num_of_clusters, 2 * sizeof(float), (__compar_fn_t)anchors_data_comparator);
-
- //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 };
-
- printf("\n");
- float avg_iou = 0;
- for (i = 0; i < number_of_boxes; ++i) {
- float box_w = rel_width_height_array[i * 2]; //points->data.fl[i * 2];
- float box_h = rel_width_height_array[i * 2 + 1]; //points->data.fl[i * 2 + 1];
- //int cluster_idx = labels->data.i[i];
- int cluster_idx = 0;
- float min_dist = FLT_MAX;
- float best_iou = 0;
- for (j = 0; j < num_of_clusters; ++j) {
- float anchor_w = anchors_data.centers.vals[j][0]; // centers->data.fl[j * 2];
- float anchor_h = anchors_data.centers.vals[j][1]; // centers->data.fl[j * 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;
- float distance = 1 - iou;
- if (distance < min_dist) {
- min_dist = distance;
- cluster_idx = j;
- best_iou = iou;
- }
- }
-
- float anchor_w = anchors_data.centers.vals[cluster_idx][0]; //centers->data.fl[cluster_idx * 2];
- float anchor_h = anchors_data.centers.vals[cluster_idx][1]; //centers->data.fl[cluster_idx * 2 + 1];
- if (best_iou > 1 || best_iou < 0) { // || box_w > width || box_h > height) {
- printf(" Wrong label: i = %d, box_w = %f, box_h = %f, anchor_w = %f, anchor_h = %f, iou = %f \n",
- i, box_w, box_h, anchor_w, anchor_h, best_iou);
- }
- else avg_iou += best_iou;
- }
-
- char buff[1024];
- FILE* fwc = fopen("counters_per_class.txt", "wb");
- if (fwc) {
- sprintf(buff, "counters_per_class = ");
- printf("\n%s", buff);
- fwrite(buff, sizeof(char), strlen(buff), fwc);
- for (i = 0; i < classes; ++i) {
- sprintf(buff, "%d", counter_per_class[i]);
- printf("%s", buff);
- fwrite(buff, sizeof(char), strlen(buff), fwc);
- if (i < classes - 1) {
- fwrite(", ", sizeof(char), 2, fwc);
- printf(", ");
- }
- }
- printf("\n");
- fclose(fwc);
- }
- else {
- printf(" Error: file counters_per_class.txt can't be open \n");
- }
-
- avg_iou = 100 * avg_iou / number_of_boxes;
- printf("\n avg IoU = %2.2f %% \n", avg_iou);
-
-
- FILE* fw = fopen("anchors.txt", "wb");
- if (fw) {
- printf("\nSaving anchors to the file: anchors.txt \n");
- printf("anchors = ");
- for (i = 0; i < num_of_clusters; ++i) {
- float anchor_w = anchors_data.centers.vals[i][0]; //centers->data.fl[i * 2];
- float anchor_h = anchors_data.centers.vals[i][1]; //centers->data.fl[i * 2 + 1];
- if (width > 32) sprintf(buff, "%3.0f,%3.0f", anchor_w, anchor_h);
- else sprintf(buff, "%2.4f,%2.4f", anchor_w, anchor_h);
- 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);
- }
- else {
- printf(" Error: file anchors.txt can't be open \n");
- }
-
- if (show) {
-#ifdef OPENCV
- show_acnhors(number_of_boxes, num_of_clusters, rel_width_height_array, anchors_data, width, height);
-#endif // OPENCV
- }
- free(rel_width_height_array);
- free(counter_per_class);
-
- getchar();
-}
-
-
-void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
- float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers)
-{
- list *options = read_data_cfg(datacfg);
- char *name_list = option_find_str(options, "names", "data/names.list");
- int names_size = 0;
- char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
-
- image **alphabet = load_alphabet();
- network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
- if (weightfile) {
- load_weights(&net, weightfile);
- }
- net.benchmark_layers = benchmark_layers;
- fuse_conv_batchnorm(net);
- calculate_binary_weights(net);
- if (net.layers[net.n - 1].classes != names_size) {
- printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
- name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
- if (net.layers[net.n - 1].classes > names_size) getchar();
- }
- srand(2222222);
- char buff[256];
- char *input = buff;
- char *json_buf = NULL;
- int json_image_id = 0;
- FILE* json_file = NULL;
- if (outfile) {
- json_file = fopen(outfile, "wb");
- if(!json_file) {
- error("fopen failed");
- }
- char *tmp = "[\n";
- fwrite(tmp, sizeof(char), strlen(tmp), json_file);
- }
- 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) break;
- strtok(input, "\n");
- }
- //image im;
- //image sized = load_image_resize(input, net.w, net.h, net.c, &im);
- image im = load_image(input, 0, 0, net.c);
- image sized;
- if(letter_box) sized = letterbox_image(im, net.w, net.h);
- else sized = resize_image(im, net.w, net.h);
- 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] = (float*)xcalloc(l.classes, sizeof(float));
-
- float *X = sized.data;
-
- //time= what_time_is_it_now();
- double time = get_time_point();
- network_predict(net, X);
- //network_predict_image(&net, im); letterbox = 1;
- printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);
- //printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time));
-
- int nboxes = 0;
- detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter_box);
- if (nms) {
- if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
- else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
- }
- draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
- save_image(im, "predictions");
- if (!dont_show) {
- show_image(im, "predictions");
- }
-
- if (json_file) {
- if (json_buf) {
- char *tmp = ", \n";
- fwrite(tmp, sizeof(char), strlen(tmp), json_file);
- }
- ++json_image_id;
- json_buf = detection_to_json(dets, nboxes, l.classes, names, json_image_id, input);
-
- fwrite(json_buf, sizeof(char), strlen(json_buf), json_file);
- free(json_buf);
- }
-
- // pseudo labeling concept - fast.ai
- if (save_labels)
- {
- char labelpath[4096];
- replace_image_to_label(input, labelpath);
-
- FILE* fw = fopen(labelpath, "wb");
- int i;
- for (i = 0; i < nboxes; ++i) {
- char buff[1024];
- int class_id = -1;
- float prob = 0;
- for (j = 0; j < l.classes; ++j) {
- if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) {
- prob = dets[i].prob[j];
- class_id = j;
- }
- }
- if (class_id >= 0) {
- sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h);
- fwrite(buff, sizeof(char), strlen(buff), fw);
- }
- }
- fclose(fw);
- }
-
- free_detections(dets, nboxes);
- free_image(im);
- free_image(sized);
-
- if (!dont_show) {
- wait_until_press_key_cv();
- destroy_all_windows_cv();
- }
-
- if (filename) break;
- }
-
- if (json_file) {
- char *tmp = "\n]";
- fwrite(tmp, sizeof(char), strlen(tmp), json_file);
- fclose(json_file);
- }
-
- // free memory
- free_ptrs((void**)names, net.layers[net.n - 1].classes);
- free_list_contents_kvp(options);
- 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);
-}
-
-#if defined(OPENCV) && defined(GPU)
-
-// adversarial attack dnn
-void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num,
- int letter_box, int benchmark_layers)
-{
- list *options = read_data_cfg(datacfg);
- char *name_list = option_find_str(options, "names", "data/names.list");
- int names_size = 0;
- char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
-
- image **alphabet = load_alphabet();
- network net = parse_network_cfg(cfgfile);// parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
- net.adversarial = 1;
- set_batch_network(&net, 1);
- if (weightfile) {
- load_weights(&net, weightfile);
- }
- net.benchmark_layers = benchmark_layers;
- //fuse_conv_batchnorm(net);
- //calculate_binary_weights(net);
- if (net.layers[net.n - 1].classes != names_size) {
- printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
- name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
- if (net.layers[net.n - 1].classes > names_size) getchar();
- }
-
- srand(2222222);
- 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) break;
- strtok(input, "\n");
- }
- //image im;
- //image sized = load_image_resize(input, net.w, net.h, net.c, &im);
- image im = load_image(input, 0, 0, net.c);
- image sized;
- if (letter_box) sized = letterbox_image(im, net.w, net.h);
- else sized = resize_image(im, net.w, net.h);
-
- image src_sized = copy_image(sized);
-
- layer l = net.layers[net.n - 1];
- net.num_boxes = l.max_boxes;
- int num_truth = l.truths;
- float *truth_cpu = (float *)xcalloc(num_truth, sizeof(float));
-
- int *it_num_set = (int *)xcalloc(1, sizeof(int));
- float *lr_set = (float *)xcalloc(1, sizeof(float));
- int *boxonly = (int *)xcalloc(1, sizeof(int));
-
- cv_draw_object(sized, truth_cpu, net.num_boxes, num_truth, it_num_set, lr_set, boxonly, l.classes, names);
-
- net.learning_rate = *lr_set;
- it_num = *it_num_set;
-
- float *X = sized.data;
-
- mat_cv* img = NULL;
- float max_img_loss = 5;
- int number_of_lines = 100;
- int img_size = 1000;
- char windows_name[100];
- char *base = basecfg(cfgfile);
- sprintf(windows_name, "chart_%s.png", base);
- img = draw_train_chart(windows_name, max_img_loss, it_num, number_of_lines, img_size, dont_show, NULL);
-
- int iteration;
- for (iteration = 0; iteration < it_num; ++iteration)
- {
- forward_backward_network_gpu(net, X, truth_cpu);
-
- float avg_loss = get_network_cost(net);
- draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", dont_show, 0, 0);
-
- float inv_loss = 1.0 / max_val_cmp(0.01, avg_loss);
- //net.learning_rate = *lr_set * inv_loss;
-
- if (*boxonly) {
- int dw = truth_cpu[2] * sized.w, dh = truth_cpu[3] * sized.h;
- int dx = truth_cpu[0] * sized.w - dw / 2, dy = truth_cpu[1] * sized.h - dh / 2;
- image crop = crop_image(sized, dx, dy, dw, dh);
- copy_image_inplace(src_sized, sized);
- embed_image(crop, sized, dx, dy);
- }
-
- show_image_cv(sized, "image_optimization");
- wait_key_cv(20);
- }
-
- net.train = 0;
- quantize_image(sized);
- network_predict(net, X);
-
- save_image_png(sized, "drawn");
- //sized = load_image("drawn.png", 0, 0, net.c);
-
- int nboxes = 0;
- detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, 0, 0, 1, &nboxes, letter_box);
- if (nms) {
- if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
- else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
- }
- draw_detections_v3(sized, dets, nboxes, thresh, names, alphabet, l.classes, 1);
- save_image(sized, "pre_predictions");
- if (!dont_show) {
- show_image(sized, "pre_predictions");
- }
-
- free_detections(dets, nboxes);
- free_image(im);
- free_image(sized);
- free_image(src_sized);
-
- if (!dont_show) {
- wait_until_press_key_cv();
- destroy_all_windows_cv();
- }
-
- free(lr_set);
- free(it_num_set);
-
- if (filename) break;
- }
-
- // free memory
- free_ptrs((void**)names, net.layers[net.n - 1].classes);
- free_list_contents_kvp(options);
- 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);
-}
-#else // defined(OPENCV) && defined(GPU)
-void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num,
- int letter_box, int benchmark_layers)
-{
- printf(" ./darknet detector draw ... can't be used without OpenCV and CUDA! \n");
- getchar();
-}
-#endif // defined(OPENCV) && defined(GPU)
-
-void run_detector(int argc, char **argv)
-{
- int dont_show = find_arg(argc, argv, "-dont_show");
- int benchmark = find_arg(argc, argv, "-benchmark");
- int benchmark_layers = find_arg(argc, argv, "-benchmark_layers");
- //if (benchmark_layers) benchmark = 1;
- if (benchmark) dont_show = 1;
- int show = find_arg(argc, argv, "-show");
- int letter_box = find_arg(argc, argv, "-letter_box");
- int calc_map = find_arg(argc, argv, "-map");
- int map_points = find_int_arg(argc, argv, "-points", 0);
- check_mistakes = find_arg(argc, argv, "-check_mistakes");
- int show_imgs = find_arg(argc, argv, "-show_imgs");
- int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
- int dontdraw_bbox = find_arg(argc, argv, "-dontdraw_bbox");
- int json_port = find_int_arg(argc, argv, "-json_port", -1);
- char *http_post_host = find_char_arg(argc, argv, "-http_post_host", 0);
- int time_limit_sec = find_int_arg(argc, argv, "-time_limit_sec", 0);
- 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 iou_thresh = find_float_arg(argc, argv, "-iou_thresh", .5); // 0.5 for mAP
- 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);
- // extended output in test mode (output of rect bound coords)
- // and for recall mode (extended output table-like format with results for best_class fit)
- int ext_output = find_arg(argc, argv, "-ext_output");
- int save_labels = find_arg(argc, argv, "-save_labels");
- char* chart_path = find_char_arg(argc, argv, "-chart", 0);
- if (argc < 4) {
- fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [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 = (int)strlen(gpu_list);
- ngpus = 1;
- int i;
- for (i = 0; i < len; ++i) {
- if (gpu_list[i] == ',') ++ngpus;
- }
- gpus = (int*)xcalloc(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, ext_output, save_labels, outfile, letter_box, benchmark_layers);
- else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs, benchmark_layers, chart_path);
- 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, iou_thresh, map_points, letter_box, NULL);
- else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
- else if (0 == strcmp(argv[2], "draw")) {
- int it_num = 100;
- draw_object(datacfg, cfg, weights, filename, thresh, dont_show, it_num, letter_box, benchmark_layers);
- }
- 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,
- mjpeg_port, dontdraw_bbox, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host, benchmark, benchmark_layers);
-
- free_list_contents_kvp(options);
- free_list(options);
- }
- else printf(" There isn't such command: %s", argv[2]);
-
- if (gpus && gpu_list && ngpus > 1) free(gpus);
-}
+#include <stdlib.h>
+#include "darknet.h"
+#include "network.h"
+#include "region_layer.h"
+#include "cost_layer.h"
+#include "utils.h"
+#include "parser.h"
+#include "box.h"
+#include "demo.h"
+#include "option_list.h"
+
+#ifndef __COMPAR_FN_T
+#define __COMPAR_FN_T
+typedef int (*__compar_fn_t)(const void*, const void*);
+#ifdef __USE_GNU
+typedef __compar_fn_t comparison_fn_t;
+#endif
+#endif
+
+#include "http_stream.h"
+
+int check_mistakes = 0;
+
+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 };
+
+void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path)
+{
+ list *options = read_data_cfg(datacfg);
+ char *train_images = option_find_str(options, "train", "data/train.txt");
+ char *valid_images = option_find_str(options, "valid", train_images);
+ char *backup_directory = option_find_str(options, "backup", "/backup/");
+
+ network net_map;
+ if (calc_map) {
+ FILE* valid_file = fopen(valid_images, "r");
+ if (!valid_file) {
+ printf("\n Error: There is no %s file for mAP calculation!\n Don't use -map flag.\n Or set valid=%s in your %s file. \n", valid_images, train_images, datacfg);
+ getchar();
+ exit(-1);
+ }
+ else fclose(valid_file);
+
+ cuda_set_device(gpus[0]);
+ printf(" Prepare additional network for mAP calculation...\n");
+ net_map = parse_network_cfg_custom(cfgfile, 1, 1);
+ net_map.benchmark_layers = benchmark_layers;
+ const int net_classes = net_map.layers[net_map.n - 1].classes;
+
+ int k; // free memory unnecessary arrays
+ for (k = 0; k < net_map.n - 1; ++k) free_layer_custom(net_map.layers[k], 1);
+
+ char *name_list = option_find_str(options, "names", "data/names.list");
+ int names_size = 0;
+ char **names = get_labels_custom(name_list, &names_size);
+ if (net_classes != names_size) {
+ printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
+ name_list, names_size, net_classes, cfgfile);
+ if (net_classes > names_size) getchar();
+ }
+ free_ptrs((void**)names, net_map.layers[net_map.n - 1].classes);
+ }
+
+ srand(time(0));
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ float avg_loss = -1;
+ float avg_contrastive_acc = 0;
+ network* nets = (network*)xcalloc(ngpus, sizeof(network));
+
+ srand(time(0));
+ int seed = rand();
+ int k;
+ for (k = 0; k < ngpus; ++k) {
+ srand(seed);
+#ifdef GPU
+ cuda_set_device(gpus[k]);
+#endif
+ nets[k] = parse_network_cfg(cfgfile);
+ nets[k].benchmark_layers = benchmark_layers;
+ if (weightfile) {
+ load_weights(&nets[k], weightfile);
+ }
+ if (clear) {
+ *nets[k].seen = 0;
+ *nets[k].cur_iteration = 0;
+ }
+ nets[k].learning_rate *= ngpus;
+ }
+ srand(time(0));
+ network net = nets[0];
+
+ const int actual_batch_size = net.batch * net.subdivisions;
+ if (actual_batch_size == 1) {
+ printf("\n Error: You set incorrect value batch=1 for Training! You should set batch=64 subdivision=64 \n");
+ getchar();
+ }
+ else if (actual_batch_size < 8) {
+ printf("\n Warning: You set batch=%d lower than 64! It is recommended to set batch=64 subdivision=64 \n", actual_batch_size);
+ }
+
+ int imgs = net.batch * net.subdivisions * ngpus;
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ data train, buffer;
+
+ layer l = net.layers[net.n - 1];
+ for (k = 0; k < net.n; ++k) {
+ layer lk = net.layers[k];
+ if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) {
+ l = lk;
+ printf(" Detection layer: %d - type = %d \n", k, l.type);
+ }
+ }
+
+ int classes = l.classes;
+
+ list *plist = get_paths(train_images);
+ int train_images_num = plist->size;
+ char **paths = (char **)list_to_array(plist);
+
+ const int init_w = net.w;
+ const int init_h = net.h;
+ const int init_b = net.batch;
+ int iter_save, iter_save_last, iter_map;
+ iter_save = get_current_iteration(net);
+ iter_save_last = get_current_iteration(net);
+ iter_map = get_current_iteration(net);
+ float mean_average_precision = -1;
+ float best_map = mean_average_precision;
+
+ load_args args = { 0 };
+ args.w = net.w;
+ args.h = net.h;
+ args.c = net.c;
+ args.paths = paths;
+ args.n = imgs;
+ args.m = plist->size;
+ args.classes = classes;
+ args.flip = net.flip;
+ args.jitter = l.jitter;
+ args.resize = l.resize;
+ args.num_boxes = l.max_boxes;
+ args.truth_size = l.truth_size;
+ net.num_boxes = args.num_boxes;
+ net.train_images_num = train_images_num;
+ args.d = &buffer;
+ args.type = DETECTION_DATA;
+ args.threads = 64; // 16 or 64
+
+ args.angle = net.angle;
+ args.gaussian_noise = net.gaussian_noise;
+ args.blur = net.blur;
+ args.mixup = net.mixup;
+ args.exposure = net.exposure;
+ args.saturation = net.saturation;
+ args.hue = net.hue;
+ args.letter_box = net.letter_box;
+ args.mosaic_bound = net.mosaic_bound;
+ args.contrastive = net.contrastive;
+ args.contrastive_jit_flip = net.contrastive_jit_flip;
+ args.contrastive_color = net.contrastive_color;
+ if (dont_show && show_imgs) show_imgs = 2;
+ args.show_imgs = show_imgs;
+
+#ifdef OPENCV
+ //int num_threads = get_num_threads();
+ //if(num_threads > 2) args.threads = get_num_threads() - 2;
+ args.threads = 6 * ngpus; // 3 for - Amazon EC2 Tesla V100: p3.2xlarge (8 logical cores) - p3.16xlarge
+ //args.threads = 12 * ngpus; // Ryzen 7 2700X (16 logical cores)
+ mat_cv* img = NULL;
+ float max_img_loss = net.max_chart_loss;
+ int number_of_lines = 100;
+ int img_size = 1000;
+ char windows_name[100];
+ sprintf(windows_name, "chart_%s.png", base);
+ img = draw_train_chart(windows_name, max_img_loss, net.max_batches, number_of_lines, img_size, dont_show, chart_path);
+#endif //OPENCV
+ if (net.contrastive && args.threads > net.batch/2) args.threads = net.batch / 2;
+ if (net.track) {
+ args.track = net.track;
+ args.augment_speed = net.augment_speed;
+ if (net.sequential_subdivisions) args.threads = net.sequential_subdivisions * ngpus;
+ else args.threads = net.subdivisions * ngpus;
+ args.mini_batch = net.batch / net.time_steps;
+ printf("\n Tracking! batch = %d, subdiv = %d, time_steps = %d, mini_batch = %d \n", net.batch, net.subdivisions, net.time_steps, args.mini_batch);
+ }
+ //printf(" imgs = %d \n", imgs);
+
+ pthread_t load_thread = load_data(args);
+
+ int count = 0;
+ double time_remaining, avg_time = -1, alpha_time = 0.01;
+
+ //while(i*imgs < N*120){
+ while (get_current_iteration(net) < net.max_batches) {
+ if (l.random && count++ % 10 == 0) {
+ float rand_coef = 1.4;
+ if (l.random != 1.0) rand_coef = l.random;
+ printf("Resizing, random_coef = %.2f \n", rand_coef);
+ float random_val = rand_scale(rand_coef); // *x or /x
+ int dim_w = roundl(random_val*init_w / net.resize_step + 1) * net.resize_step;
+ int dim_h = roundl(random_val*init_h / net.resize_step + 1) * net.resize_step;
+ if (random_val < 1 && (dim_w > init_w || dim_h > init_h)) dim_w = init_w, dim_h = init_h;
+
+ int max_dim_w = roundl(rand_coef*init_w / net.resize_step + 1) * net.resize_step;
+ int max_dim_h = roundl(rand_coef*init_h / net.resize_step + 1) * net.resize_step;
+
+ // at the beginning (check if enough memory) and at the end (calc rolling mean/variance)
+ if (avg_loss < 0 || get_current_iteration(net) > net.max_batches - 100) {
+ dim_w = max_dim_w;
+ dim_h = max_dim_h;
+ }
+
+ if (dim_w < net.resize_step) dim_w = net.resize_step;
+ if (dim_h < net.resize_step) dim_h = net.resize_step;
+ int dim_b = (init_b * max_dim_w * max_dim_h) / (dim_w * dim_h);
+ int new_dim_b = (int)(dim_b * 0.8);
+ if (new_dim_b > init_b) dim_b = new_dim_b;
+
+ args.w = dim_w;
+ args.h = dim_h;
+
+ int k;
+ if (net.dynamic_minibatch) {
+ for (k = 0; k < ngpus; ++k) {
+ (*nets[k].seen) = init_b * net.subdivisions * get_current_iteration(net); // remove this line, when you will save to weights-file both: seen & cur_iteration
+ nets[k].batch = dim_b;
+ int j;
+ for (j = 0; j < nets[k].n; ++j)
+ nets[k].layers[j].batch = dim_b;
+ }
+ net.batch = dim_b;
+ imgs = net.batch * net.subdivisions * ngpus;
+ args.n = imgs;
+ printf("\n %d x %d (batch = %d) \n", dim_w, dim_h, net.batch);
+ }
+ else
+ printf("\n %d x %d \n", dim_w, dim_h);
+
+ pthread_join(load_thread, 0);
+ train = buffer;
+ free_data(train);
+ load_thread = load_data(args);
+
+ for (k = 0; k < ngpus; ++k) {
+ resize_network(nets + k, dim_w, dim_h);
+ }
+ net = nets[0];
+ }
+ double time = what_time_is_it_now();
+ pthread_join(load_thread, 0);
+ train = buffer;
+ if (net.track) {
+ net.sequential_subdivisions = get_current_seq_subdivisions(net);
+ args.threads = net.sequential_subdivisions * ngpus;
+ printf(" sequential_subdivisions = %d, sequence = %d \n", net.sequential_subdivisions, get_sequence_value(net));
+ }
+ load_thread = load_data(args);
+ //wait_key_cv(500);
+
+ /*
+ int k;
+ for(k = 0; k < l.max_boxes; ++k){
+ box b = float_to_box(train.y.vals[10] + 1 + k*5);
+ if(!b.x) break;
+ printf("loaded: %f %f %f %f\n", b.x, b.y, b.w, b.h);
+ }
+ image im = float_to_image(448, 448, 3, train.X.vals[10]);
+ int k;
+ for(k = 0; k < l.max_boxes; ++k){
+ box b = float_to_box(train.y.vals[10] + 1 + k*5);
+ printf("%d %d %d %d\n", truth.x, truth.y, truth.w, truth.h);
+ draw_bbox(im, b, 8, 1,0,0);
+ }
+ save_image(im, "truth11");
+ */
+
+ const double load_time = (what_time_is_it_now() - time);
+ printf("Loaded: %lf seconds", load_time);
+ if (load_time > 0.1 && avg_loss > 0) printf(" - performance bottleneck on CPU or Disk HDD/SSD");
+ printf("\n");
+
+ time = what_time_is_it_now();
+ float loss = 0;
+#ifdef GPU
+ if (ngpus == 1) {
+ int wait_key = (dont_show) ? 0 : 1;
+ loss = train_network_waitkey(net, train, wait_key);
+ }
+ else {
+ loss = train_networks(nets, ngpus, train, 4);
+ }
+#else
+ loss = train_network(net, train);
+#endif
+ if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss; // if(-inf or nan)
+ avg_loss = avg_loss*.9 + loss*.1;
+
+ const int iteration = get_current_iteration(net);
+ //i = get_current_batch(net);
+
+ int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions); // calculate mAP for each 4 Epochs
+ calc_map_for_each = fmax(calc_map_for_each, 100);
+ int next_map_calc = iter_map + calc_map_for_each;
+ next_map_calc = fmax(next_map_calc, net.burn_in);
+ //next_map_calc = fmax(next_map_calc, 400);
+ if (calc_map) {
+ printf("\n (next mAP calculation at %d iterations) ", next_map_calc);
+ if (mean_average_precision > 0) printf("\n Last accuracy mAP@0.5 = %2.2f %%, best = %2.2f %% ", mean_average_precision * 100, best_map * 100);
+ }
+
+ if (net.cudnn_half) {
+ if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
+ else fprintf(stderr, "\n Tensor Cores are used.\n");
+ fflush(stderr);
+ }
+ printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images, %f hours left\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs, avg_time);
+ fflush(stdout);
+
+ int draw_precision = 0;
+ if (calc_map && (iteration >= next_map_calc || iteration == net.max_batches)) {
+ if (l.random) {
+ printf("Resizing to initial size: %d x %d ", init_w, init_h);
+ args.w = init_w;
+ args.h = init_h;
+ int k;
+ if (net.dynamic_minibatch) {
+ for (k = 0; k < ngpus; ++k) {
+ for (k = 0; k < ngpus; ++k) {
+ nets[k].batch = init_b;
+ int j;
+ for (j = 0; j < nets[k].n; ++j)
+ nets[k].layers[j].batch = init_b;
+ }
+ }
+ net.batch = init_b;
+ imgs = init_b * net.subdivisions * ngpus;
+ args.n = imgs;
+ printf("\n %d x %d (batch = %d) \n", init_w, init_h, init_b);
+ }
+ pthread_join(load_thread, 0);
+ free_data(train);
+ train = buffer;
+ load_thread = load_data(args);
+ for (k = 0; k < ngpus; ++k) {
+ resize_network(nets + k, init_w, init_h);
+ }
+ net = nets[0];
+ }
+
+ copy_weights_net(net, &net_map);
+
+ // combine Training and Validation networks
+ //network net_combined = combine_train_valid_networks(net, net_map);
+
+ iter_map = iteration;
+ mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined);
+ printf("\n mean_average_precision (mAP@0.5) = %f \n", mean_average_precision);
+ if (mean_average_precision > best_map) {
+ best_map = mean_average_precision;
+ printf("New best mAP!\n");
+ char buff[256];
+ sprintf(buff, "%s/%s_best.weights", backup_directory, base);
+ save_weights(net, buff);
+ }
+
+ draw_precision = 1;
+ }
+ time_remaining = ((net.max_batches - iteration) / ngpus)*(what_time_is_it_now() - time + load_time) / 60 / 60;
+ // set initial value, even if resume training from 10000 iteration
+ if (avg_time < 0) avg_time = time_remaining;
+ else avg_time = alpha_time * time_remaining + (1 - alpha_time) * avg_time;
+#ifdef OPENCV
+ if (net.contrastive) {
+ float cur_con_acc = -1;
+ for (k = 0; k < net.n; ++k)
+ if (net.layers[k].type == CONTRASTIVE) cur_con_acc = *net.layers[k].loss;
+ if (cur_con_acc >= 0) avg_contrastive_acc = avg_contrastive_acc*0.99 + cur_con_acc * 0.01;
+ printf(" avg_contrastive_acc = %f \n", avg_contrastive_acc);
+ }
+ draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", avg_contrastive_acc / 100, dont_show, mjpeg_port, avg_time);
+#endif // OPENCV
+
+ //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
+ //if (i % 100 == 0) {
+ if ((iteration >= (iter_save + 10000) || iteration % 10000 == 0) ||
+ (iteration >= (iter_save + 1000) || iteration % 1000 == 0) && net.max_batches < 10000)
+ {
+ iter_save = iteration;
+#ifdef GPU
+ if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights", backup_directory, base, iteration);
+ save_weights(net, buff);
+ }
+
+ if (iteration >= (iter_save_last + 100) || (iteration % 100 == 0 && iteration > 1)) {
+ iter_save_last = iteration;
+#ifdef GPU
+ if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+ char buff[256];
+ sprintf(buff, "%s/%s_last.weights", backup_directory, base);
+ save_weights(net, buff);
+
+ if (net.ema_alpha && is_ema_initialized(net)) {
+ sprintf(buff, "%s/%s_ema.weights", backup_directory, base);
+ save_weights_upto(net, buff, net.n, 1);
+ printf(" EMA weights are saved to the file: %s \n", buff);
+ }
+ }
+ free_data(train);
+ }
+#ifdef GPU
+ if (ngpus != 1) sync_nets(nets, ngpus, 0);
+#endif
+ char buff[256];
+ sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+ save_weights(net, buff);
+ printf("If you want to train from the beginning, then use flag in the end of training command: -clear \n");
+
+#ifdef OPENCV
+ release_mat(&img);
+ destroy_all_windows_cv();
+#endif
+
+ // free memory
+ pthread_join(load_thread, 0);
+ free_data(buffer);
+
+ free_load_threads(&args);
+
+ free(base);
+ free(paths);
+ free_list_contents(plist);
+ free_list(plist);
+
+ free_list_contents_kvp(options);
+ free_list(options);
+
+ for (k = 0; k < ngpus; ++k) free_network(nets[k]);
+ free(nets);
+ //free_network(net);
+
+ if (calc_map) {
+ net_map.n = 0;
+ free_network(net_map);
+ }
+}
+
+
+static int get_coco_image_id(char *filename)
+{
+ char *p = strrchr(filename, '/');
+ char *c = strrchr(filename, '_');
+ if (c) p = c;
+ return atoi(p + 1);
+}
+
+static void print_cocos(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
+{
+ int i, j;
+ //int image_id = get_coco_image_id(image_path);
+ char *p = basecfg(image_path);
+ int image_id = atoi(p);
+ for (i = 0; i < num_boxes; ++i) {
+ float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
+ float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
+ float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
+ float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
+
+ if (xmin < 0) xmin = 0;
+ if (ymin < 0) ymin = 0;
+ if (xmax > w) xmax = w;
+ if (ymax > h) ymax = h;
+
+ float bx = xmin;
+ float by = ymin;
+ float bw = xmax - xmin;
+ float bh = ymax - ymin;
+
+ for (j = 0; j < classes; ++j) {
+ if (dets[i].prob[j] > 0) {
+ char buff[1024];
+ sprintf(buff, "{\"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]);
+ fprintf(fp, buff);
+ //printf("%s", buff);
+ }
+ }
+ }
+}
+
+void print_detector_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h)
+{
+ int i, j;
+ for (i = 0; i < total; ++i) {
+ float xmin = dets[i].bbox.x - dets[i].bbox.w / 2. + 1;
+ float xmax = dets[i].bbox.x + dets[i].bbox.w / 2. + 1;
+ float ymin = dets[i].bbox.y - dets[i].bbox.h / 2. + 1;
+ float ymax = dets[i].bbox.y + dets[i].bbox.h / 2. + 1;
+
+ if (xmin < 1) xmin = 1;
+ if (ymin < 1) ymin = 1;
+ if (xmax > w) xmax = w;
+ if (ymax > h) ymax = h;
+
+ for (j = 0; j < classes; ++j) {
+ if (dets[i].prob[j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, dets[i].prob[j],
+ xmin, ymin, xmax, ymax);
+ }
+ }
+}
+
+void print_imagenet_detections(FILE *fp, int id, detection *dets, int total, int classes, int w, int h)
+{
+ int i, j;
+ for (i = 0; i < total; ++i) {
+ float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
+ float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
+ float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
+ float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
+
+ if (xmin < 0) xmin = 0;
+ if (ymin < 0) ymin = 0;
+ if (xmax > w) xmax = w;
+ if (ymax > h) ymax = h;
+
+ for (j = 0; j < classes; ++j) {
+ int myclass = j;
+ if (dets[i].prob[myclass] > 0) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[myclass],
+ xmin, ymin, xmax, ymax);
+ }
+ }
+}
+
+static void print_kitti_detections(FILE **fps, char *id, detection *dets, int total, int classes, int w, int h, char *outfile, char *prefix)
+{
+ char *kitti_ids[] = { "car", "pedestrian", "cyclist" };
+ FILE *fpd = 0;
+ char buffd[1024];
+ snprintf(buffd, 1024, "%s/%s/data/%s.txt", prefix, outfile, id);
+
+ fpd = fopen(buffd, "w");
+ int i, j;
+ for (i = 0; i < total; ++i)
+ {
+ float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
+ float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
+ float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
+ float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
+
+ if (xmin < 0) xmin = 0;
+ if (ymin < 0) ymin = 0;
+ if (xmax > w) xmax = w;
+ if (ymax > h) ymax = h;
+
+ for (j = 0; j < classes; ++j)
+ {
+ //if (dets[i].prob[j]) fprintf(fpd, "%s 0 0 0 %f %f %f %f -1 -1 -1 -1 0 0 0 %f\n", kitti_ids[j], xmin, ymin, xmax, ymax, dets[i].prob[j]);
+ if (dets[i].prob[j]) fprintf(fpd, "%s -1 -1 -10 %f %f %f %f -1 -1 -1 -1000 -1000 -1000 -10 %f\n", kitti_ids[j], xmin, ymin, xmax, ymax, dets[i].prob[j]);
+ }
+ }
+ fclose(fpd);
+}
+
+static void eliminate_bdd(char *buf, char *a)
+{
+ int n = 0;
+ int i, k;
+ for (i = 0; buf[i] != '\0'; i++)
+ {
+ if (buf[i] == a[n])
+ {
+ k = i;
+ while (buf[i] == a[n])
+ {
+ if (a[++n] == '\0')
+ {
+ for (k; buf[k + n] != '\0'; k++)
+ {
+ buf[k] = buf[k + n];
+ }
+ buf[k] = '\0';
+ break;
+ }
+ i++;
+ }
+ n = 0; i--;
+ }
+ }
+}
+
+static void get_bdd_image_id(char *filename)
+{
+ char *p = strrchr(filename, '/');
+ eliminate_bdd(p, ".jpg");
+ eliminate_bdd(p, "/");
+ strcpy(filename, p);
+}
+
+static void print_bdd_detections(FILE *fp, char *image_path, detection *dets, int num_boxes, int classes, int w, int h)
+{
+ char *bdd_ids[] = { "bike" , "bus" , "car" , "motor" ,"person", "rider", "traffic light", "traffic sign", "train", "truck" };
+ get_bdd_image_id(image_path);
+ int i, j;
+
+ for (i = 0; i < num_boxes; ++i)
+ {
+ float xmin = dets[i].bbox.x - dets[i].bbox.w / 2.;
+ float xmax = dets[i].bbox.x + dets[i].bbox.w / 2.;
+ float ymin = dets[i].bbox.y - dets[i].bbox.h / 2.;
+ float ymax = dets[i].bbox.y + dets[i].bbox.h / 2.;
+
+ if (xmin < 0) xmin = 0;
+ if (ymin < 0) ymin = 0;
+ if (xmax > w) xmax = w;
+ if (ymax > h) ymax = h;
+
+ float bx1 = xmin;
+ float by1 = ymin;
+ float bx2 = xmax;
+ float by2 = ymax;
+
+ for (j = 0; j < classes; ++j)
+ {
+ if (dets[i].prob[j])
+ {
+ fprintf(fp, "\t{\n\t\t\"name\":\"%s\",\n\t\t\"category\":\"%s\",\n\t\t\"bbox\":[%f, %f, %f, %f],\n\t\t\"score\":%f\n\t},\n", image_path, bdd_ids[j], bx1, by1, bx2, by2, dets[i].prob[j]);
+ }
+ }
+ }
+}
+
+void validate_detector(char *datacfg, char *cfgfile, char *weightfile, char *outfile)
+{
+ int j;
+ list *options = read_data_cfg(datacfg);
+ char *valid_images = option_find_str(options, "valid", "data/train.list");
+ char *name_list = option_find_str(options, "names", "data/names.list");
+ char *prefix = option_find_str(options, "results", "results");
+ 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, 1); // set batch=1
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ //set_batch_network(&net, 1);
+ fuse_conv_batchnorm(net);
+ calculate_binary_weights(net);
+ 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);
+
+ layer l = net.layers[net.n - 1];
+ int k;
+ for (k = 0; k < net.n; ++k) {
+ layer lk = net.layers[k];
+ if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) {
+ l = lk;
+ printf(" Detection layer: %d - type = %d \n", k, l.type);
+ }
+ }
+ int classes = l.classes;
+
+ char buff[1024];
+ char *type = option_find_str(options, "eval", "voc");
+ FILE *fp = 0;
+ FILE **fps = 0;
+ int coco = 0;
+ int imagenet = 0;
+ int bdd = 0;
+ int kitti = 0;
+
+ if (0 == strcmp(type, "coco")) {
+ if (!outfile) outfile = "coco_results";
+ snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
+ fp = fopen(buff, "w");
+ fprintf(fp, "[\n");
+ coco = 1;
+ }
+ else if (0 == strcmp(type, "bdd")) {
+ if (!outfile) outfile = "bdd_results";
+ snprintf(buff, 1024, "%s/%s.json", prefix, outfile);
+ fp = fopen(buff, "w");
+ fprintf(fp, "[\n");
+ bdd = 1;
+ }
+ else if (0 == strcmp(type, "kitti")) {
+ char buff2[1024];
+ if (!outfile) outfile = "kitti_results";
+ printf("%s\n", outfile);
+ snprintf(buff, 1024, "%s/%s", prefix, outfile);
+ int mkd = make_directory(buff, 0777);
+ snprintf(buff2, 1024, "%s/%s/data", prefix, outfile);
+ int mkd2 = make_directory(buff2, 0777);
+ kitti = 1;
+ }
+ else if (0 == strcmp(type, "imagenet")) {
+ if (!outfile) outfile = "imagenet-detection";
+ snprintf(buff, 1024, "%s/%s.txt", prefix, outfile);
+ fp = fopen(buff, "w");
+ imagenet = 1;
+ classes = 200;
+ }
+ else {
+ if (!outfile) outfile = "comp4_det_test_";
+ fps = (FILE**) xcalloc(classes, sizeof(FILE *));
+ for (j = 0; j < classes; ++j) {
+ snprintf(buff, 1024, "%s/%s%s.txt", prefix, outfile, names[j]);
+ fps[j] = fopen(buff, "w");
+ }
+ }
+
+
+ int m = plist->size;
+ int i = 0;
+ int t;
+
+ float thresh = .001;
+ float nms = .6;
+
+ int nthreads = 4;
+ if (m < 4) nthreads = m;
+ image* val = (image*)xcalloc(nthreads, sizeof(image));
+ image* val_resized = (image*)xcalloc(nthreads, sizeof(image));
+ image* buf = (image*)xcalloc(nthreads, sizeof(image));
+ image* buf_resized = (image*)xcalloc(nthreads, sizeof(image));
+ pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t));
+
+ load_args args = { 0 };
+ args.w = net.w;
+ args.h = net.h;
+ args.c = net.c;
+ args.type = IMAGE_DATA;
+ const int letter_box = net.letter_box;
+ if (letter_box) args.type = LETTERBOX_DATA;
+
+ 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) {
+ char *path = paths[i + t - nthreads];
+ char *id = basecfg(path);
+ float *X = val_resized[t].data;
+ network_predict(net, X);
+ int w = val[t].w;
+ int h = val[t].h;
+ int nboxes = 0;
+ detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letter_box);
+ if (nms) {
+ if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
+ else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
+ }
+
+ if (coco) {
+ print_cocos(fp, path, dets, nboxes, classes, w, h);
+ }
+ else if (imagenet) {
+ print_imagenet_detections(fp, i + t - nthreads + 1, dets, nboxes, classes, w, h);
+ }
+ else if (bdd) {
+ print_bdd_detections(fp, path, dets, nboxes, classes, w, h);
+ }
+ else if (kitti) {
+ print_kitti_detections(fps, id, dets, nboxes, classes, w, h, outfile, prefix);
+ }
+ else {
+ print_detector_detections(fps, id, dets, nboxes, classes, w, h);
+ }
+
+ free_detections(dets, nboxes);
+ free(id);
+ free_image(val[t]);
+ free_image(val_resized[t]);
+ }
+ }
+ if (fps) {
+ for (j = 0; j < classes; ++j) {
+ fclose(fps[j]);
+ }
+ free(fps);
+ }
+ if (coco) {
+#ifdef WIN32
+ fseek(fp, -3, SEEK_CUR);
+#else
+ fseek(fp, -2, SEEK_CUR);
+#endif
+ fprintf(fp, "\n]\n");
+ }
+
+ if (bdd) {
+#ifdef WIN32
+ fseek(fp, -3, SEEK_CUR);
+#else
+ fseek(fp, -2, SEEK_CUR);
+#endif
+ fprintf(fp, "\n]\n");
+ fclose(fp);
+ }
+
+ if (fp) fclose(fp);
+
+ if (val) free(val);
+ if (val_resized) free(val_resized);
+ if (thr) free(thr);
+ if (buf) free(buf);
+ if (buf_resized) free(buf_resized);
+
+ fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)time(0) - start);
+}
+
+void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
+{
+ network net = parse_network_cfg_custom(cfgfile, 1, 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("data/coco_val_5k.list");
+ list *options = read_data_cfg(datacfg);
+ char *valid_images = option_find_str(options, "valid", "data/train.txt");
+ list *plist = get_paths(valid_images);
+ char **paths = (char **)list_to_array(plist);
+
+ //layer l = net.layers[net.n - 1];
+
+ int j, k;
+
+ int m = plist->size;
+ int i = 0;
+
+ float thresh = .001;
+ float iou_thresh = .5;
+ float nms = .4;
+
+ int total = 0;
+ int correct = 0;
+ int proposals = 0;
+ float avg_iou = 0;
+
+ for (i = 0; i < m; ++i) {
+ char *path = paths[i];
+ image orig = load_image(path, 0, 0, net.c);
+ image sized = resize_image(orig, net.w, net.h);
+ char *id = basecfg(path);
+ network_predict(net, sized.data);
+ int nboxes = 0;
+ int letterbox = 0;
+ detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, .5, 0, 1, &nboxes, letterbox);
+ if (nms) do_nms_obj(dets, nboxes, 1, nms);
+
+ char labelpath[4096];
+ replace_image_to_label(path, labelpath);
+
+ int num_labels = 0;
+ box_label *truth = read_boxes(labelpath, &num_labels);
+ for (k = 0; k < nboxes; ++k) {
+ if (dets[k].objectness > thresh) {
+ ++proposals;
+ }
+ }
+ for (j = 0; j < num_labels; ++j) {
+ ++total;
+ box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
+ float best_iou = 0;
+ for (k = 0; k < nboxes; ++k) {
+ float iou = box_iou(dets[k].bbox, t);
+ if (dets[k].objectness > thresh && iou > best_iou) {
+ best_iou = iou;
+ }
+ }
+ avg_iou += best_iou;
+ if (best_iou > iou_thresh) {
+ ++correct;
+ }
+ }
+ //fprintf(stderr, " %s - %s - ", paths[i], labelpath);
+ 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);
+ }
+}
+
+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 = *(const box_prob *)pa;
+ box_prob b = *(const box_prob *)pb;
+ float diff = a.p - b.p;
+ if (diff < 0) return 1;
+ else if (diff > 0) return -1;
+ return 0;
+}
+
+float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net)
+{
+ 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");
+ int names_size = 0;
+ char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
+ //char *mapf = option_find_str(options, "map", 0);
+ //int *map = 0;
+ //if (mapf) map = read_map(mapf);
+ FILE* reinforcement_fd = NULL;
+
+ network net;
+ //int initial_batch;
+ if (existing_net) {
+ char *train_images = option_find_str(options, "train", "data/train.txt");
+ valid_images = option_find_str(options, "valid", train_images);
+ net = *existing_net;
+ remember_network_recurrent_state(*existing_net);
+ free_network_recurrent_state(*existing_net);
+ }
+ else {
+ net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ //set_batch_network(&net, 1);
+ fuse_conv_batchnorm(net);
+ calculate_binary_weights(net);
+ }
+ if (net.layers[net.n - 1].classes != names_size) {
+ printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
+ name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
+ getchar();
+ }
+ srand(time(0));
+ printf("\n calculation mAP (mean average precision)...\n");
+
+ 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 k;
+ for (k = 0; k < net.n; ++k) {
+ layer lk = net.layers[k];
+ if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) {
+ l = lk;
+ printf(" Detection layer: %d - type = %d \n", k, l.type);
+ }
+ }
+ 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;
+ if (m < 4) nthreads = m;
+ image* val = (image*)xcalloc(nthreads, sizeof(image));
+ image* val_resized = (image*)xcalloc(nthreads, sizeof(image));
+ image* buf = (image*)xcalloc(nthreads, sizeof(image));
+ image* buf_resized = (image*)xcalloc(nthreads, sizeof(image));
+ pthread_t* thr = (pthread_t*)xcalloc(nthreads, sizeof(pthread_t));
+
+ load_args args = { 0 };
+ args.w = net.w;
+ args.h = net.h;
+ args.c = net.c;
+ letter_box = net.letter_box;
+ if (letter_box) args.type = LETTERBOX_DATA;
+ else 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 = (box_prob*)xcalloc(1, sizeof(box_prob));
+ int detections_count = 0;
+ int unique_truth_count = 0;
+
+ int* truth_classes_count = (int*)xcalloc(classes, sizeof(int));
+
+ // For multi-class precision and recall computation
+ float *avg_iou_per_class = (float*)xcalloc(classes, sizeof(float));
+ int *tp_for_thresh_per_class = (int*)xcalloc(classes, sizeof(int));
+ int *fp_for_thresh_per_class = (int*)xcalloc(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, "\r%d", 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;
+ float hier_thresh = 0;
+ detection *dets;
+ if (args.type == LETTERBOX_DATA) {
+ dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box);
+ }
+ else {
+ dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 0, &nboxes, letter_box);
+ }
+ //detection *dets = get_network_boxes(&net, val[t].w, val[t].h, thresh, hier_thresh, 0, 1, &nboxes, letter_box); // for letter_box=1
+ if (nms) {
+ if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
+ else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
+ }
+
+ //if (l.embedding_size) set_track_id(dets, nboxes, thresh, l.sim_thresh, l.track_ciou_norm, l.track_history_size, l.dets_for_track, l.dets_for_show);
+
+ char labelpath[4096];
+ replace_image_to_label(path, labelpath);
+ int num_labels = 0;
+ box_label *truth = read_boxes(labelpath, &num_labels);
+ int 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];
+ replace_image_to_label(path_dif, labelpath_dif);
+
+ truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
+ }
+
+ const int checkpoint_detections_count = detections_count;
+
+ int i;
+ 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 = (box_prob*)xrealloc(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;
+ avg_iou_per_class[class_id] += max_iou;
+ tp_for_thresh_per_class[class_id]++;
+ }
+ else{
+ fp_for_thresh++;
+ fp_for_thresh_per_class[class_id]++;
+ }
+ }
+ }
+ }
+ }
+
+ unique_truth_count += num_labels;
+
+ //static int previous_errors = 0;
+ //int total_errors = fp_for_thresh + (unique_truth_count - tp_for_thresh);
+ //int errors_in_this_image = total_errors - previous_errors;
+ //previous_errors = total_errors;
+ //if(reinforcement_fd == NULL) reinforcement_fd = fopen("reinforcement.txt", "wb");
+ //char buff[1000];
+ //sprintf(buff, "%s\n", path);
+ //if(errors_in_this_image > 0) fwrite(buff, sizeof(char), strlen(buff), reinforcement_fd);
+
+ free_detections(dets, nboxes);
+ free(id);
+ free_image(val[t]);
+ free_image(val_resized[t]);
+ }
+ }
+
+ //for (t = 0; t < nthreads; ++t) {
+ // pthread_join(thr[t], 0);
+ //}
+
+ if ((tp_for_thresh + fp_for_thresh) > 0)
+ avg_iou = avg_iou / (tp_for_thresh + fp_for_thresh);
+
+ int class_id;
+ for(class_id = 0; class_id < classes; class_id++){
+ if ((tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]) > 0)
+ avg_iou_per_class[class_id] = avg_iou_per_class[class_id] / (tp_for_thresh_per_class[class_id] + fp_for_thresh_per_class[class_id]);
+ }
+
+ // 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 = (pr_t**)xcalloc(classes, sizeof(pr_t*));
+ for (i = 0; i < classes; ++i) {
+ pr[i] = (pr_t*)xcalloc(detections_count, sizeof(pr_t));
+ }
+ printf("\n detections_count = %d, unique_truth_count = %d \n", detections_count, unique_truth_count);
+
+
+ int* detection_per_class_count = (int*)xcalloc(classes, sizeof(int));
+ for (j = 0; j < detections_count; ++j) {
+ detection_per_class_count[detections[j].class_id]++;
+ }
+
+ int* truth_flags = (int*)xcalloc(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++;
+ }
+ 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;
+
+ if (rank == (detections_count - 1) && detection_per_class_count[i] != (tp + fp)) { // check for last rank
+ printf(" class_id: %d - detections = %d, tp+fp = %d, tp = %d, fp = %d \n", i, detection_per_class_count[i], tp+fp, tp, fp);
+ }
+ }
+ }
+
+ free(truth_flags);
+
+
+ double mean_average_precision = 0;
+
+ for (i = 0; i < classes; ++i) {
+ double avg_precision = 0;
+
+ // MS COCO - uses 101-Recall-points on PR-chart.
+ // PascalVOC2007 - uses 11-Recall-points on PR-chart.
+ // PascalVOC2010-2012 - uses Area-Under-Curve on PR-chart.
+ // ImageNet - uses Area-Under-Curve on PR-chart.
+
+ // correct mAP calculation: ImageNet, PascalVOC 2010-2012
+ if (map_points == 0)
+ {
+ double last_recall = pr[i][detections_count - 1].recall;
+ double last_precision = pr[i][detections_count - 1].precision;
+ for (rank = detections_count - 2; rank >= 0; --rank)
+ {
+ double delta_recall = last_recall - pr[i][rank].recall;
+ last_recall = pr[i][rank].recall;
+
+ if (pr[i][rank].precision > last_precision) {
+ last_precision = pr[i][rank].precision;
+ }
+
+ avg_precision += delta_recall * last_precision;
+ }
+ //add remaining area of PR curve when recall isn't 0 at rank-1
+ double delta_recall = last_recall - 0;
+ avg_precision += delta_recall * last_precision;
+ }
+ // MSCOCO - 101 Recall-points, PascalVOC - 11 Recall-points
+ else
+ {
+ int point;
+ for (point = 0; point < map_points; ++point) {
+ double cur_recall = point * 1.0 / (map_points-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 / map_points;
+ }
+
+ printf("class_id = %d, name = %s, ap = %2.2f%% \t (TP = %d, FP = %d) \n",
+ i, names[i], avg_precision * 100, tp_for_thresh_per_class[i], fp_for_thresh_per_class[i]);
+
+ float class_precision = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);
+ float class_recall = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));
+ //printf("Precision = %1.2f, Recall = %1.2f, avg IOU = %2.2f%% \n\n", class_precision, class_recall, avg_iou_per_class[i]);
+
+ 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("\n for conf_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 conf_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 IoU threshold = %2.0f %%, ", iou_thresh * 100);
+ if (map_points) printf("used %d Recall-points \n", map_points);
+ else printf("used Area-Under-Curve for each unique Recall \n");
+
+ printf(" mean average precision (mAP@%0.2f) = %f, or %2.2f %% \n", iou_thresh, mean_average_precision, mean_average_precision * 100);
+
+ for (i = 0; i < classes; ++i) {
+ free(pr[i]);
+ }
+ free(pr);
+ free(detections);
+ free(truth_classes_count);
+ free(detection_per_class_count);
+
+ free(avg_iou_per_class);
+ free(tp_for_thresh_per_class);
+ free(fp_for_thresh_per_class);
+
+ fprintf(stderr, "Total Detection Time: %d Seconds\n", (int)(time(0) - start));
+ printf("\nSet -points flag:\n");
+ printf(" `-points 101` for MS COCO \n");
+ printf(" `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n");
+ printf(" `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n");
+ if (reinforcement_fd != NULL) fclose(reinforcement_fd);
+
+ // free memory
+ free_ptrs((void**)names, net.layers[net.n - 1].classes);
+ free_list_contents_kvp(options);
+ free_list(options);
+
+ if (existing_net) {
+ //set_batch_network(&net, initial_batch);
+ //free_network_recurrent_state(*existing_net);
+ restore_network_recurrent_state(*existing_net);
+ //randomize_network_recurrent_state(*existing_net);
+ }
+ else {
+ free_network(net);
+ }
+ if (val) free(val);
+ if (val_resized) free(val_resized);
+ if (thr) free(thr);
+ if (buf) free(buf);
+ if (buf_resized) free(buf_resized);
+
+ return mean_average_precision;
+}
+
+typedef struct {
+ float w, h;
+} anchors_t;
+
+int anchors_comparator(const void *pa, const void *pb)
+{
+ anchors_t a = *(const anchors_t *)pa;
+ anchors_t b = *(const 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;
+}
+
+int anchors_data_comparator(const float **pa, const float **pb)
+{
+ float *a = (float *)*pa;
+ float *b = (float *)*pb;
+ float diff = b[0] * b[1] - a[0] * a[1];
+ 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 = (float*)xcalloc(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 classes = option_find_int(options, "classes", 1);
+ int* counter_per_class = (int*)xcalloc(classes, sizeof(int));
+
+ srand(time(0));
+ 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];
+ replace_image_to_label(path, labelpath);
+
+ int num_labels = 0;
+ box_label *truth = read_boxes(labelpath, &num_labels);
+ //printf(" new path: %s \n", labelpath);
+ char *buff = (char*)xcalloc(6144, sizeof(char));
+ for (j = 0; j < num_labels; ++j)
+ {
+ if (truth[j].x > 1 || truth[j].x <= 0 || truth[j].y > 1 || truth[j].y <= 0 ||
+ truth[j].w > 1 || truth[j].w <= 0 || truth[j].h > 1 || truth[j].h <= 0)
+ {
+ printf("\n\nWrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f \n",
+ labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
+ sprintf(buff, "echo \"Wrong label: %s - j = %d, x = %f, y = %f, width = %f, height = %f\" >> bad_label.list",
+ labelpath, j, truth[j].x, truth[j].y, truth[j].w, truth[j].h);
+ system(buff);
+ if (check_mistakes) getchar();
+ }
+ if (truth[j].id >= classes) {
+ classes = truth[j].id + 1;
+ counter_per_class = (int*)xrealloc(counter_per_class, classes * sizeof(int));
+ }
+ counter_per_class[truth[j].id]++;
+
+ number_of_boxes++;
+ rel_width_height_array = (float*)xrealloc(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);
+ }
+ free(buff);
+ }
+ printf("\n all loaded. \n");
+ printf("\n calculating k-means++ ...");
+
+ matrix boxes_data;
+ model anchors_data;
+ boxes_data = make_matrix(number_of_boxes, 2);
+
+ printf("\n");
+ for (i = 0; i < number_of_boxes; ++i) {
+ boxes_data.vals[i][0] = rel_width_height_array[i * 2];
+ boxes_data.vals[i][1] = rel_width_height_array[i * 2 + 1];
+ //if (w > 410 || h > 410) printf("i:%d, w = %f, h = %f \n", i, w, h);
+ }
+
+ // Is used: distance(box, centroid) = 1 - IoU(box, centroid)
+
+ // K-means
+ anchors_data = do_kmeans(boxes_data, num_of_clusters);
+
+ qsort((void*)anchors_data.centers.vals, num_of_clusters, 2 * sizeof(float), (__compar_fn_t)anchors_data_comparator);
+
+ //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 };
+
+ printf("\n");
+ float avg_iou = 0;
+ for (i = 0; i < number_of_boxes; ++i) {
+ float box_w = rel_width_height_array[i * 2]; //points->data.fl[i * 2];
+ float box_h = rel_width_height_array[i * 2 + 1]; //points->data.fl[i * 2 + 1];
+ //int cluster_idx = labels->data.i[i];
+ int cluster_idx = 0;
+ float min_dist = FLT_MAX;
+ float best_iou = 0;
+ for (j = 0; j < num_of_clusters; ++j) {
+ float anchor_w = anchors_data.centers.vals[j][0]; // centers->data.fl[j * 2];
+ float anchor_h = anchors_data.centers.vals[j][1]; // centers->data.fl[j * 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;
+ float distance = 1 - iou;
+ if (distance < min_dist) {
+ min_dist = distance;
+ cluster_idx = j;
+ best_iou = iou;
+ }
+ }
+
+ float anchor_w = anchors_data.centers.vals[cluster_idx][0]; //centers->data.fl[cluster_idx * 2];
+ float anchor_h = anchors_data.centers.vals[cluster_idx][1]; //centers->data.fl[cluster_idx * 2 + 1];
+ if (best_iou > 1 || best_iou < 0) { // || box_w > width || box_h > height) {
+ printf(" Wrong label: i = %d, box_w = %f, box_h = %f, anchor_w = %f, anchor_h = %f, iou = %f \n",
+ i, box_w, box_h, anchor_w, anchor_h, best_iou);
+ }
+ else avg_iou += best_iou;
+ }
+
+ char buff[1024];
+ FILE* fwc = fopen("counters_per_class.txt", "wb");
+ if (fwc) {
+ sprintf(buff, "counters_per_class = ");
+ printf("\n%s", buff);
+ fwrite(buff, sizeof(char), strlen(buff), fwc);
+ for (i = 0; i < classes; ++i) {
+ sprintf(buff, "%d", counter_per_class[i]);
+ printf("%s", buff);
+ fwrite(buff, sizeof(char), strlen(buff), fwc);
+ if (i < classes - 1) {
+ fwrite(", ", sizeof(char), 2, fwc);
+ printf(", ");
+ }
+ }
+ printf("\n");
+ fclose(fwc);
+ }
+ else {
+ printf(" Error: file counters_per_class.txt can't be open \n");
+ }
+
+ avg_iou = 100 * avg_iou / number_of_boxes;
+ printf("\n avg IoU = %2.2f %% \n", avg_iou);
+
+
+ FILE* fw = fopen("anchors.txt", "wb");
+ if (fw) {
+ printf("\nSaving anchors to the file: anchors.txt \n");
+ printf("anchors = ");
+ for (i = 0; i < num_of_clusters; ++i) {
+ float anchor_w = anchors_data.centers.vals[i][0]; //centers->data.fl[i * 2];
+ float anchor_h = anchors_data.centers.vals[i][1]; //centers->data.fl[i * 2 + 1];
+ if (width > 32) sprintf(buff, "%3.0f,%3.0f", anchor_w, anchor_h);
+ else sprintf(buff, "%2.4f,%2.4f", anchor_w, anchor_h);
+ 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);
+ }
+ else {
+ printf(" Error: file anchors.txt can't be open \n");
+ }
+
+ if (show) {
+#ifdef OPENCV
+ show_acnhors(number_of_boxes, num_of_clusters, rel_width_height_array, anchors_data, width, height);
+#endif // OPENCV
+ }
+ free(rel_width_height_array);
+ free(counter_per_class);
+
+ getchar();
+}
+
+
+void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
+ float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers)
+{
+ list *options = read_data_cfg(datacfg);
+ char *name_list = option_find_str(options, "names", "data/names.list");
+ int names_size = 0;
+ char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
+
+ image **alphabet = load_alphabet();
+ network net = parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ if (net.letter_box) letter_box = 1;
+ net.benchmark_layers = benchmark_layers;
+ fuse_conv_batchnorm(net);
+ calculate_binary_weights(net);
+ if (net.layers[net.n - 1].classes != names_size) {
+ printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
+ name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
+ if (net.layers[net.n - 1].classes > names_size) getchar();
+ }
+ srand(2222222);
+ char buff[256];
+ char *input = buff;
+ char *json_buf = NULL;
+ int json_image_id = 0;
+ FILE* json_file = NULL;
+ if (outfile) {
+ json_file = fopen(outfile, "wb");
+ if(!json_file) {
+ error("fopen failed");
+ }
+ char *tmp = "[\n";
+ fwrite(tmp, sizeof(char), strlen(tmp), json_file);
+ }
+ 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) break;
+ strtok(input, "\n");
+ }
+ //image im;
+ //image sized = load_image_resize(input, net.w, net.h, net.c, &im);
+ image im = load_image(input, 0, 0, net.c);
+ image sized;
+ if(letter_box) sized = letterbox_image(im, net.w, net.h);
+ else sized = resize_image(im, net.w, net.h);
+
+ layer l = net.layers[net.n - 1];
+ int k;
+ for (k = 0; k < net.n; ++k) {
+ layer lk = net.layers[k];
+ if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) {
+ l = lk;
+ printf(" Detection layer: %d - type = %d \n", k, l.type);
+ }
+ }
+
+ //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] = (float*)xcalloc(l.classes, sizeof(float));
+
+ float *X = sized.data;
+
+ //time= what_time_is_it_now();
+ double time = get_time_point();
+ network_predict(net, X);
+ //network_predict_image(&net, im); letterbox = 1;
+ printf("%s: Predicted in %lf milli-seconds.\n", input, ((double)get_time_point() - time) / 1000);
+ //printf("%s: Predicted in %f seconds.\n", input, (what_time_is_it_now()-time));
+
+ int nboxes = 0;
+ detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter_box);
+ if (nms) {
+ if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
+ else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
+ }
+ draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes, ext_output);
+ save_image(im, "predictions");
+ if (!dont_show) {
+ show_image(im, "predictions");
+ }
+
+ if (json_file) {
+ if (json_buf) {
+ char *tmp = ", \n";
+ fwrite(tmp, sizeof(char), strlen(tmp), json_file);
+ }
+ ++json_image_id;
+ json_buf = detection_to_json(dets, nboxes, l.classes, names, json_image_id, input);
+
+ fwrite(json_buf, sizeof(char), strlen(json_buf), json_file);
+ free(json_buf);
+ }
+
+ // pseudo labeling concept - fast.ai
+ if (save_labels)
+ {
+ char labelpath[4096];
+ replace_image_to_label(input, labelpath);
+
+ FILE* fw = fopen(labelpath, "wb");
+ int i;
+ for (i = 0; i < nboxes; ++i) {
+ char buff[1024];
+ int class_id = -1;
+ float prob = 0;
+ for (j = 0; j < l.classes; ++j) {
+ if (dets[i].prob[j] > thresh && dets[i].prob[j] > prob) {
+ prob = dets[i].prob[j];
+ class_id = j;
+ }
+ }
+ if (class_id >= 0) {
+ sprintf(buff, "%d %2.4f %2.4f %2.4f %2.4f\n", class_id, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h);
+ fwrite(buff, sizeof(char), strlen(buff), fw);
+ }
+ }
+ fclose(fw);
+ }
+
+ free_detections(dets, nboxes);
+ free_image(im);
+ free_image(sized);
+
+ if (!dont_show) {
+ wait_until_press_key_cv();
+ destroy_all_windows_cv();
+ }
+
+ if (filename) break;
+ }
+
+ if (json_file) {
+ char *tmp = "\n]";
+ fwrite(tmp, sizeof(char), strlen(tmp), json_file);
+ fclose(json_file);
+ }
+
+ // free memory
+ free_ptrs((void**)names, net.layers[net.n - 1].classes);
+ free_list_contents_kvp(options);
+ 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);
+}
+
+#if defined(OPENCV) && defined(GPU)
+
+// adversarial attack dnn
+void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num,
+ int letter_box, int benchmark_layers)
+{
+ list *options = read_data_cfg(datacfg);
+ char *name_list = option_find_str(options, "names", "data/names.list");
+ int names_size = 0;
+ char **names = get_labels_custom(name_list, &names_size); //get_labels(name_list);
+
+ image **alphabet = load_alphabet();
+ network net = parse_network_cfg(cfgfile);// parse_network_cfg_custom(cfgfile, 1, 1); // set batch=1
+ net.adversarial = 1;
+ set_batch_network(&net, 1);
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ net.benchmark_layers = benchmark_layers;
+ //fuse_conv_batchnorm(net);
+ //calculate_binary_weights(net);
+ if (net.layers[net.n - 1].classes != names_size) {
+ printf("\n Error: in the file %s number of names %d that isn't equal to classes=%d in the file %s \n",
+ name_list, names_size, net.layers[net.n - 1].classes, cfgfile);
+ if (net.layers[net.n - 1].classes > names_size) getchar();
+ }
+
+ srand(2222222);
+ 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) break;
+ strtok(input, "\n");
+ }
+ //image im;
+ //image sized = load_image_resize(input, net.w, net.h, net.c, &im);
+ image im = load_image(input, 0, 0, net.c);
+ image sized;
+ if (letter_box) sized = letterbox_image(im, net.w, net.h);
+ else sized = resize_image(im, net.w, net.h);
+
+ image src_sized = copy_image(sized);
+
+ layer l = net.layers[net.n - 1];
+ int k;
+ for (k = 0; k < net.n; ++k) {
+ layer lk = net.layers[k];
+ if (lk.type == YOLO || lk.type == GAUSSIAN_YOLO || lk.type == REGION) {
+ l = lk;
+ printf(" Detection layer: %d - type = %d \n", k, l.type);
+ }
+ }
+
+ net.num_boxes = l.max_boxes;
+ int num_truth = l.truths;
+ float *truth_cpu = (float *)xcalloc(num_truth, sizeof(float));
+
+ int *it_num_set = (int *)xcalloc(1, sizeof(int));
+ float *lr_set = (float *)xcalloc(1, sizeof(float));
+ int *boxonly = (int *)xcalloc(1, sizeof(int));
+
+ cv_draw_object(sized, truth_cpu, net.num_boxes, num_truth, it_num_set, lr_set, boxonly, l.classes, names);
+
+ net.learning_rate = *lr_set;
+ it_num = *it_num_set;
+
+ float *X = sized.data;
+
+ mat_cv* img = NULL;
+ float max_img_loss = 5;
+ int number_of_lines = 100;
+ int img_size = 1000;
+ char windows_name[100];
+ char *base = basecfg(cfgfile);
+ sprintf(windows_name, "chart_%s.png", base);
+ img = draw_train_chart(windows_name, max_img_loss, it_num, number_of_lines, img_size, dont_show, NULL);
+
+ int iteration;
+ for (iteration = 0; iteration < it_num; ++iteration)
+ {
+ forward_backward_network_gpu(net, X, truth_cpu);
+
+ float avg_loss = get_network_cost(net);
+ draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, it_num, 0, 0, "mAP%", 0, dont_show, 0, 0);
+
+ float inv_loss = 1.0 / max_val_cmp(0.01, avg_loss);
+ //net.learning_rate = *lr_set * inv_loss;
+
+ if (*boxonly) {
+ int dw = truth_cpu[2] * sized.w, dh = truth_cpu[3] * sized.h;
+ int dx = truth_cpu[0] * sized.w - dw / 2, dy = truth_cpu[1] * sized.h - dh / 2;
+ image crop = crop_image(sized, dx, dy, dw, dh);
+ copy_image_inplace(src_sized, sized);
+ embed_image(crop, sized, dx, dy);
+ }
+
+ show_image_cv(sized, "image_optimization");
+ wait_key_cv(20);
+ }
+
+ net.train = 0;
+ quantize_image(sized);
+ network_predict(net, X);
+
+ save_image_png(sized, "drawn");
+ //sized = load_image("drawn.png", 0, 0, net.c);
+
+ int nboxes = 0;
+ detection *dets = get_network_boxes(&net, sized.w, sized.h, thresh, 0, 0, 1, &nboxes, letter_box);
+ if (nms) {
+ if (l.nms_kind == DEFAULT_NMS) do_nms_sort(dets, nboxes, l.classes, nms);
+ else diounms_sort(dets, nboxes, l.classes, nms, l.nms_kind, l.beta_nms);
+ }
+ draw_detections_v3(sized, dets, nboxes, thresh, names, alphabet, l.classes, 1);
+ save_image(sized, "pre_predictions");
+ if (!dont_show) {
+ show_image(sized, "pre_predictions");
+ }
+
+ free_detections(dets, nboxes);
+ free_image(im);
+ free_image(sized);
+ free_image(src_sized);
+
+ if (!dont_show) {
+ wait_until_press_key_cv();
+ destroy_all_windows_cv();
+ }
+
+ free(lr_set);
+ free(it_num_set);
+
+ if (filename) break;
+ }
+
+ // free memory
+ free_ptrs((void**)names, net.layers[net.n - 1].classes);
+ free_list_contents_kvp(options);
+ 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);
+}
+#else // defined(OPENCV) && defined(GPU)
+void draw_object(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh, int dont_show, int it_num,
+ int letter_box, int benchmark_layers)
+{
+ printf(" ./darknet detector draw ... can't be used without OpenCV and CUDA! \n");
+ getchar();
+}
+#endif // defined(OPENCV) && defined(GPU)
+
+void run_detector(int argc, char **argv)
+{
+ int dont_show = find_arg(argc, argv, "-dont_show");
+ int benchmark = find_arg(argc, argv, "-benchmark");
+ int benchmark_layers = find_arg(argc, argv, "-benchmark_layers");
+ //if (benchmark_layers) benchmark = 1;
+ if (benchmark) dont_show = 1;
+ int show = find_arg(argc, argv, "-show");
+ int letter_box = find_arg(argc, argv, "-letter_box");
+ int calc_map = find_arg(argc, argv, "-map");
+ int map_points = find_int_arg(argc, argv, "-points", 0);
+ check_mistakes = find_arg(argc, argv, "-check_mistakes");
+ int show_imgs = find_arg(argc, argv, "-show_imgs");
+ int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
+ int avgframes = find_int_arg(argc, argv, "-avgframes", 3);
+ int dontdraw_bbox = find_arg(argc, argv, "-dontdraw_bbox");
+ int json_port = find_int_arg(argc, argv, "-json_port", -1);
+ char *http_post_host = find_char_arg(argc, argv, "-http_post_host", 0);
+ int time_limit_sec = find_int_arg(argc, argv, "-time_limit_sec", 0);
+ 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 iou_thresh = find_float_arg(argc, argv, "-iou_thresh", .5); // 0.5 for mAP
+ 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);
+ // extended output in test mode (output of rect bound coords)
+ // and for recall mode (extended output table-like format with results for best_class fit)
+ int ext_output = find_arg(argc, argv, "-ext_output");
+ int save_labels = find_arg(argc, argv, "-save_labels");
+ char* chart_path = find_char_arg(argc, argv, "-chart", 0);
+ if (argc < 4) {
+ fprintf(stderr, "usage: %s %s [train/test/valid/demo/map] [data] [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 = (int)strlen(gpu_list);
+ ngpus = 1;
+ int i;
+ for (i = 0; i < len; ++i) {
+ if (gpu_list[i] == ',') ++ngpus;
+ }
+ gpus = (int*)xcalloc(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, ext_output, save_labels, outfile, letter_box, benchmark_layers);
+ else if (0 == strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show, calc_map, mjpeg_port, show_imgs, benchmark_layers, chart_path);
+ 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, iou_thresh, map_points, letter_box, NULL);
+ else if (0 == strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, height, show);
+ else if (0 == strcmp(argv[2], "draw")) {
+ int it_num = 100;
+ draw_object(datacfg, cfg, weights, filename, thresh, dont_show, it_num, letter_box, benchmark_layers);
+ }
+ 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, avgframes, frame_skip, prefix, out_filename,
+ mjpeg_port, dontdraw_bbox, json_port, dont_show, ext_output, letter_box, time_limit_sec, http_post_host, benchmark, benchmark_layers);
+
+ free_list_contents_kvp(options);
+ free_list(options);
+ }
+ else printf(" There isn't such command: %s", argv[2]);
+
+ if (gpus && gpu_list && ngpus > 1) free(gpus);
+}
--
Gitblit v1.8.0