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Added C implementation of calculation mAP (mean average precision) using Darknet

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This commit is contained in:
AlexeyAB
2018-02-15 15:43:25 +03:00
parent 6af2cf692c
commit a1af57d8d6
3 changed files with 298 additions and 2 deletions
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12
build/darknet/x64/calc_mAP.cmd Normal file
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@ -0,0 +1,12 @@
rem # How to calculate mAP (mean average precision)
darknet.exe detector map data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights
rem darknet.exe detector map data/voc.data yolo-voc.cfg yolo-voc.weights
pause

4
build/darknet/x64/compute_mAP.cmd → build/darknet/x64/calc_mAP_voc_py.cmd
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@ -3,9 +3,9 @@ rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install cPi
rem C:\Users\Alex\AppData\Local\Programs\Python\Python36\Scripts\pip install _pickle
rem darknet.exe detector valid data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights
darknet.exe detector valid data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights
darknet.exe detector valid data/voc.data yolo-voc.cfg yolo-voc.weights
rem darknet.exe detector valid data/voc.data yolo-voc.cfg yolo-voc.weights
reval_voc_py3.py --year 2007 --classes data\voc.names --image_set test --voc_dir E:\VOC2007_2012\VOCtrainval_11-May-2012\VOCdevkit results

284
src/detector.c
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@ -315,6 +315,8 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
float thresh = .005;
float nms = .45;
int detection_count = 0;
int nthreads = 4;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
@ -356,6 +358,15 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
int h = val[t].h;
get_region_boxes(l, w, h, thresh, probs, boxes, 0, map);
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
int x, y;
for (x = 0; x < (l.w*l.h*l.n); ++x) {
for (y = 0; y < classes; ++y)
{
if (probs[x][y]) ++detection_count;
}
}
if (coco){
print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
} else if (imagenet){
@ -376,6 +387,7 @@ void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
fprintf(fp, "\n]\n");
fclose(fp);
}
printf("\n detection_count = %d \n", detection_count);
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
@ -409,6 +421,8 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
float iou_thresh = .5;
float nms = .4;
int detection_count = 0, truth_count = 0;
int total = 0;
int correct = 0;
int proposals = 0;
@ -432,6 +446,7 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
truth_count += num_labels;
for(k = 0; k < l.w*l.h*l.n; ++k){
if(probs[k][0] > thresh){
++proposals;
@ -458,6 +473,274 @@ void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
free_image(orig);
free_image(sized);
}
printf("\n truth_count = %d \n", truth_count);
}
typedef struct {
box b;
float p;
int class_id;
int image_index;
int truth_flag;
int unique_truth_index;
} box_prob;
int detections_comparator(const void *pa, const void *pb)
{
box_prob a = *(box_prob *)pa;
box_prob b = *(box_prob *)pb;
float diff = a.p - b.p;
if (diff < 0) return 1;
else if (diff > 0) return -1;
return 0;
}
void validate_detector_map(char *datacfg, char *cfgfile, char *weightfile)
{
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);
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));
char *base = "comp4_det_test_";
list *plist = get_paths(valid_images);
char **paths = (char **)list_to_array(plist);
layer l = net.layers[net.n - 1];
int classes = l.classes;
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
int m = plist->size;
int i = 0;
int t;
const float thresh = .005;
const float nms = .45;
const float iou_thresh = 0.5;
int nthreads = 4;
image *val = calloc(nthreads, sizeof(image));
image *val_resized = calloc(nthreads, sizeof(image));
image *buf = calloc(nthreads, sizeof(image));
image *buf_resized = calloc(nthreads, sizeof(image));
pthread_t *thr = calloc(nthreads, sizeof(pthread_t));
load_args args = { 0 };
args.w = net.w;
args.h = net.h;
args.type = IMAGE_DATA;
box_prob *detections = calloc(1, sizeof(box_prob));
int detections_count = 0;
int unique_truth_index = 0;
int *truth_classes_count = calloc(classes, sizeof(int));
for (t = 0; t < nthreads; ++t) {
args.path = paths[i + t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
time_t start = time(0);
for (i = nthreads; i < m + nthreads; i += nthreads) {
fprintf(stderr, "%d\n", i);
for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
pthread_join(thr[t], 0);
val[t] = buf[t];
val_resized[t] = buf_resized[t];
}
for (t = 0; t < nthreads && i + t < m; ++t) {
args.path = paths[i + t];
args.im = &buf[t];
args.resized = &buf_resized[t];
thr[t] = load_data_in_thread(args);
}
for (t = 0; t < nthreads && i + t - nthreads < m; ++t) {
const int image_index = i + t - nthreads;
char *path = paths[i + t - nthreads];
char *id = basecfg(path);
float *X = val_resized[t].data;
network_predict(net, X);
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, classes, nms);
char labelpath[4096];
find_replace(path, "images", "labels", labelpath);
find_replace(labelpath, "JPEGImages", "labels", labelpath);
find_replace(labelpath, ".jpg", ".txt", labelpath);
find_replace(labelpath, ".JPEG", ".txt", labelpath);
find_replace(labelpath, ".png", ".txt", labelpath);
int num_labels = 0;
box_label *truth = read_boxes(labelpath, &num_labels);
int i, j;
for (j = 0; j < num_labels; ++j) {
truth_classes_count[truth[j].id]++;
}
for (i = 0; i < (l.w*l.h*l.n); ++i) {
int class_id;
for (class_id = 0; class_id < classes; ++class_id) {
float prob = probs[i][class_id];
if (prob > 0) {
detections_count++;
detections = realloc(detections, detections_count * sizeof(box_prob));
detections[detections_count - 1].b = boxes[i];
detections[detections_count - 1].p = prob;
detections[detections_count - 1].image_index = image_index;
detections[detections_count - 1].class_id = class_id;
int truth_index = -1;
float max_iou = 0;
for (j = 0; j < num_labels; ++j)
{
box t = { truth[j].x, truth[j].y, truth[j].w, truth[j].h };
//printf(" IoU = %f, prob = %f, class_id = %d, truth[j].id = %d \n",
// box_iou(boxes[i], t), prob, class_id, truth[j].id);
float current_iou = box_iou(boxes[i], t);
if (current_iou > iou_thresh && class_id == truth[j].id) {
if (current_iou > max_iou) {
current_iou = max_iou;
truth_index = unique_truth_index + j;
}
}
}
// best IoU
if (truth_index > -1) {
detections[detections_count - 1].truth_flag = 1;
detections[detections_count - 1].unique_truth_index = truth_index;
}
}
}
}
unique_truth_index += num_labels;
free(id);
free_image(val[t]);
free_image(val_resized[t]);
}
}
// SORT(detections)
qsort(detections, detections_count, sizeof(box_prob), detections_comparator);
typedef struct {
double precision;
double recall;
int tp, fp, fn;
} pr_t;
// for PR-curve
pr_t **pr = calloc(classes, sizeof(pr_t*));
for (i = 0; i < classes; ++i) {
pr[i] = calloc(detections_count, sizeof(pr_t));
}
printf("detections_count = %d, unique_truth_index = %d \n", detections_count, unique_truth_index);
int *truth_flags = calloc(unique_truth_index, sizeof(int));
int rank;
for (rank = 0; rank < detections_count; ++rank) {
if(rank % 100 == 0)
printf(" rank = %d of ranks = %d \r", rank, detections_count);
if (rank > 0) {
int class_id;
for (class_id = 0; class_id < classes; ++class_id) {
pr[class_id][rank].tp = pr[class_id][rank - 1].tp;
pr[class_id][rank].fp = pr[class_id][rank - 1].fp;
}
}
box_prob d = detections[rank];
// if (detected && isn't detected before)
if (d.truth_flag == 1) {
if (truth_flags[d.unique_truth_index] == 0)
{
truth_flags[d.unique_truth_index] = 1;
pr[d.class_id][rank].tp++; // true-positive
}
}
else {
pr[d.class_id][rank].fp++; // false-positive
}
for (i = 0; i < classes; ++i)
{
const int tp = pr[i][rank].tp;
const int fp = pr[i][rank].fp;
const int fn = truth_classes_count[i] - tp; // false-negative = objects - true-positive
pr[i][rank].fn = fn;
if ((tp + fp) > 0) pr[i][rank].precision = (double)tp / (double)(tp + fp);
else pr[i][rank].precision = 0;
if ((tp + fn) > 0) pr[i][rank].recall = (double)tp / (double)(tp + fn);
else pr[i][rank].recall = 0;
}
}
free(truth_flags);
double mean_average_precision = 0;
for (i = 0; i < classes; ++i) {
double avg_precision = 0;
int point;
for (point = 0; point < 11; ++point) {
double cur_recall = point * 0.1;
double cur_precision = 0;
for (rank = 0; rank < detections_count; ++rank)
{
if (pr[i][rank].recall >= cur_recall) { // > or >=
if (pr[i][rank].precision > cur_precision) {
cur_precision = pr[i][rank].precision;
}
}
}
//printf("point = %d, cur_recall = %.4f, cur_precision = %.4f \n", point, cur_recall, cur_precision);
avg_precision += cur_precision;
}
avg_precision = avg_precision / 11;
printf("class = %d, name = %s, \t ap = %2.2f %% \n", i, names[i], avg_precision*100);
mean_average_precision += avg_precision;
}
mean_average_precision = mean_average_precision / classes;
printf("\n mean average precision (mAP) = %f, or %2.2f %% \n", mean_average_precision, mean_average_precision*100);
for (i = 0; i < classes; ++i) {
free(pr[i]);
}
free(pr);
free(detections);
free(truth_classes_count);
fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
}
void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh)
@ -565,6 +848,7 @@ void run_detector(int argc, char **argv)
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear);
else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_detector_recall(datacfg, cfg, weights);
else if(0==strcmp(argv[2], "map")) validate_detector_map(datacfg, cfg, weights);
else if(0==strcmp(argv[2], "demo")) {
list *options = read_data_cfg(datacfg);
int classes = option_find_int(options, "classes", 20);