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darknet/src/detector.c

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#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;
float jitter = l.jitter;
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 = jitter;
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
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);
}