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detector.c
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detector.c
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#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)
{
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);
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(net_map.layers[k]);
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(" 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();
}
}
srand(time(0));
char *base = basecfg(cfgfile);
printf("%s\n", base);
float avg_loss = -1;
network* nets = (network*)calloc(ngpus, sizeof(network));
srand(time(0));
int seed = rand();
int i;
for (i = 0; i < ngpus; ++i) {
srand(seed);
#ifdef GPU
cuda_set_device(gpus[i]);
#endif
nets[i] = parse_network_cfg(cfgfile);
if (weightfile) {
load_weights(&nets[i], weightfile);
}
if (clear) *nets[i].seen = 0;
nets[i].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);
int init_w = net.w;
int init_h = net.h;
int iter_save, iter_save_last, iter_map;
iter_save = get_current_batch(net);
iter_save_last = get_current_batch(net);
iter_map = get_current_batch(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.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 = 3 * ngpus; // 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;
img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show);
#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);
double time;
int count = 0;
//while(i*imgs < N*120){
while (get_current_batch(net) < net.max_batches) {
if (l.random && count++ % 10 == 0) {
printf("Resizing\n");
float random_val = rand_scale(1.4); // *x or /x
int dim_w = roundl(random_val*init_w / 32 + 1) * 32;
int dim_h = roundl(random_val*init_h / 32 + 1) * 32;
// at the beginning
if (avg_loss < 0) {
dim_w = roundl(1.4*init_w / 32 + 1) * 32;
dim_h = roundl(1.4*init_h / 32 + 1) * 32;
}
if (dim_w < 32) dim_w = 32;
if (dim_h < 32) dim_h = 32;
printf("%d x %d \n", dim_w, dim_h);
args.w = dim_w;
args.h = dim_h;
pthread_join(load_thread, 0);
train = buffer;
free_data(train);
load_thread = load_data(args);
for (i = 0; i < ngpus; ++i) {
resize_network(nets + i, dim_w, dim_h);
}
net = nets[0];
}
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");
*/
printf("Loaded: %lf seconds\n", (what_time_is_it_now() - time));
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;
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 (i < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in);
else fprintf(stderr, "\n Tensor Cores are used.");
}
printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs);
int draw_precision = 0;
if (calc_map && (i >= next_map_calc || i == net.max_batches)) {
if (l.random) {
printf("Resizing to initial size: %d x %d \n", init_w, init_h);
args.w = init_w;
args.h = init_h;
pthread_join(load_thread, 0);
free_data(train);
train = buffer;
load_thread = load_data(args);
int k;
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 = i;
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;
}
#ifdef OPENCV
draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port);
#endif // OPENCV
//if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
//if (i % 100 == 0) {
if (i >= (iter_save + 1000) || i % 1000 == 0) {
iter_save = i;
#ifdef GPU
if (ngpus != 1) sync_nets(nets, ngpus, 0);
#endif
char buff[256];
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
save_weights(net, buff);
}
if (i >= (iter_save_last + 100) || i % 100 == 0) {
iter_save_last = i;
#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(base);
free(paths);
free_list_contents(plist);
free_list(plist);
free_list_contents_kvp(options);
free_list(options);
for (i = 0; i < ngpus; ++i) free_network(nets[i]);
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);
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]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, dets[i].prob[j]);
}
}
}
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]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[myclass],
xmin, ymin, xmax, ymax);
}
}
}
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);
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;
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, "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**)calloc(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 = .005;
float nms = .45;
int nthreads = 4;
if (m < 4) nthreads = m;
image* val = (image*)calloc(nthreads, sizeof(image));
image* val_resized = (image*)calloc(nthreads, sizeof(image));
image* buf = (image*)calloc(nthreads, sizeof(image));
image* buf_resized = (image*)calloc(nthreads, sizeof(image));
pthread_t* thr = (pthread_t*)calloc(nthreads, sizeof(pthread_t));
load_args args = { 0 };
args.w = net.w;
args.h = net.h;
args.c = net.c;
args.type = IMAGE_DATA;
//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;
int letterbox = (args.type == LETTERBOX_DATA);
detection *dets = get_network_boxes(&net, w, h, thresh, .5, map, 0, &nboxes, letterbox);
if (nms) do_nms_sort(dets, nboxes, classes, 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 {
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]);
}
}
for (j = 0; j < classes; ++j) {
if (fps) fclose(fps[j]);
}
if (coco) {
#ifdef WIN32
fseek(fp, -3, SEEK_CUR);
#else
fseek(fp, -2, SEEK_CUR);
#endif
fprintf(fp, "\n]\n");
fclose(fp);
}
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 = *(box_prob *)pa;
box_prob b = *(box_prob *)pb;
float diff = a.p - b.p;
if (diff < 0) return 1;
else if (diff > 0) return -1;
return 0;
}
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(" 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*)calloc(nthreads, sizeof(image));
image* val_resized = (image*)calloc(nthreads, sizeof(image));
image* buf = (image*)calloc(nthreads, sizeof(image));
image* buf_resized = (image*)calloc(nthreads, sizeof(image));
pthread_t* thr = (pthread_t*)calloc(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*)calloc(1, sizeof(box_prob));
int detections_count = 0;
int unique_truth_count = 0;
int* truth_classes_count = (int*)calloc(classes, sizeof(int));
// For multi-class precision and recall computation
float *avg_iou_per_class = (float*)calloc(classes, sizeof(float));
int *tp_for_thresh_per_class = (int*)calloc(classes, sizeof(int));
int *fp_for_thresh_per_class = (int*)calloc(classes, sizeof(int));
for (t = 0; t < nthreads; ++t) {
args.path = paths[i + t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
time_t start = time(0);
for (i = nthreads; i < m + nthreads; i += nthreads) {
fprintf(stderr, "\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) do_nms_sort(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 i, j;
for (j = 0; j < num_labels; ++j) {
truth_classes_count[truth[j].id]++;
}
// difficult
box_label *truth_dif = NULL;
int num_labels_dif = 0;
if (paths_dif)
{
char *path_dif = paths_dif[image_index];
char labelpath_dif[4096];
replace_image_to_label(path_dif, labelpath_dif);
truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
}
const int checkpoint_detections_count = detections_count;
for (i = 0; i < nboxes; ++i) {
int class_id;
for (class_id = 0; class_id < classes; ++class_id) {
float prob = dets[i].prob[class_id];
if (prob > 0) {
detections_count++;
detections = (box_prob*)realloc(detections, detections_count * sizeof(box_prob));
detections[detections_count - 1].b = dets[i].bbox;
detections[detections_count - 1].p = prob;
detections[detections_count - 1].image_index = image_index;
detections[detections_count - 1].class_id = class_id;
detections[detections_count - 1].truth_flag = 0;
detections[detections_count - 1].unique_truth_index = -1;
int truth_index = -1;
float max_iou = 0;
for (j = 0; j < num_labels; ++j)
{
box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
//printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d \n",
// box_iou(dets[i].bbox, t), prob, class_id, truth[j].id);
float current_iou = box_iou(dets[i].bbox, t);
if (current_iou > iou_thresh && class_id == truth[j].id) {
if (current_iou > max_iou) {
max_iou = current_iou;
truth_index = unique_truth_count + j;
}
}
}
// best IoU
if (truth_index > -1) {
detections[detections_count - 1].truth_flag = 1;
detections[detections_count - 1].unique_truth_index = truth_index;
}
else {
// if object is difficult then remove detection
for (j = 0; j < num_labels_dif; ++j) {
box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h };
float current_iou = box_iou(dets[i].bbox, t);
if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
--detections_count;
break;
}
}
}
// calc avg IoU, true-positives, false-positives for required Threshold
if (prob > thresh_calc_avg_iou) {
int z, found = 0;
for (z = checkpoint_detections_count; z < detections_count - 1; ++z) {
if (detections[z].unique_truth_index == truth_index) {
found = 1; break;
}
}
if (truth_index > -1 && found == 0) {
avg_iou += max_iou;
++tp_for_thresh;
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]);
}
}
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**)calloc(classes, sizeof(pr_t*));
for (i = 0; i < classes; ++i) {
pr[i] = (pr_t*)calloc(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*)calloc(classes, sizeof(int));
for (j = 0; j < detections_count; ++j) {
detection_per_class_count[detections[j].class_id]++;
}
int* truth_flags = (int*)calloc(unique_truth_count, sizeof(int));
int rank;
for (rank = 0; rank < detections_count; ++rank) {
if (rank % 100 == 0)
printf(" rank = %d of ranks = %d \r", rank, detections_count);
if (rank > 0) {
int class_id;
for (class_id = 0; class_id < classes; ++class_id) {
pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
}
}
box_prob d = detections[rank];
// if (detected && isn't detected before)
if (d.truth_flag == 1) {
if (truth_flags[d.unique_truth_index] == 0)
{
truth_flags[d.unique_truth_index] = 1;
pr[d.class_id][rank].tp++; // true-positive
} else
pr[d.class_id][rank].fp++;
}
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.