From 3fc3fd0f1f1924acb7195fe98fe68648e4056a09 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Wed, 28 Mar 2018 23:38:35 +0000
Subject: [PATCH] Fixed training, calculation mAP and anchors for Yolo v3
---
build/darknet/x64/darknet_yolo_v3_video.cmd | 5
src/network.c | 10
build/darknet/x64/data/voc.data | 4
build/darknet/x64/calc_anchors.cmd | 4
build/darknet/x64/yolov3.cfg | 8
src/data.c | 3
src/detector.c | 533 ++++++++++++++++++++++++++---------------------------
src/yolo_layer.c | 21 +
src/data.h | 2
build/darknet/x64/calc_mAP.cmd | 4
10 files changed, 308 insertions(+), 286 deletions(-)
diff --git a/build/darknet/x64/calc_anchors.cmd b/build/darknet/x64/calc_anchors.cmd
index f8a77ad..497b30b 100644
--- a/build/darknet/x64/calc_anchors.cmd
+++ b/build/darknet/x64/calc_anchors.cmd
@@ -1,10 +1,10 @@
rem # How to calculate Yolo v2 anchors using K-means++
-darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -final_width 13 -final_heigh 13
+darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -width 416 -heigh 416
-rem darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -final_width 13 -final_heigh 13 -show
+rem darknet.exe detector calc_anchors data/voc.data -num_of_clusters 5 -width 416 -heigh 416 -show
diff --git a/build/darknet/x64/calc_mAP.cmd b/build/darknet/x64/calc_mAP.cmd
index 37cad1b..614a422 100644
--- a/build/darknet/x64/calc_mAP.cmd
+++ b/build/darknet/x64/calc_mAP.cmd
@@ -1,10 +1,10 @@
rem # How to calculate mAP (mean average precision)
-rem darknet.exe detector map data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights
+rem darknet.exe detector map data/voc.data cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights
-darknet.exe detector map data/voc.data yolo-voc.cfg yolo-voc.weights
+darknet.exe detector map data/voc.data cfg/yolov2-voc.cfg yolo-voc.weights
diff --git a/build/darknet/x64/darknet_yolo_v3_video.cmd b/build/darknet/x64/darknet_yolo_v3_video.cmd
new file mode 100644
index 0000000..729ec08
--- /dev/null
+++ b/build/darknet/x64/darknet_yolo_v3_video.cmd
@@ -0,0 +1,5 @@
+
+darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights -i 0 -thresh 0.25 test.mp4
+
+
+pause
\ No newline at end of file
diff --git a/build/darknet/x64/data/voc.data b/build/darknet/x64/data/voc.data
index 046019f..d677587 100644
--- a/build/darknet/x64/data/voc.data
+++ b/build/darknet/x64/data/voc.data
@@ -1,7 +1,7 @@
classes= 20
train = data/train_voc.txt
-valid = data/voc/2007_test.txt
-#difficult = data/voc/difficult_2007_test.txt
+valid = data/2007_test.txt
+#difficult = data/difficult_2007_test.txt
names = data/voc.names
backup = backup/
diff --git a/build/darknet/x64/yolov3.cfg b/build/darknet/x64/yolov3.cfg
index 5f3ab62..33d9421 100644
--- a/build/darknet/x64/yolov3.cfg
+++ b/build/darknet/x64/yolov3.cfg
@@ -1,10 +1,10 @@
[net]
# Testing
-batch=1
-subdivisions=1
+#batch=1
+#subdivisions=1
# Training
-# batch=64
-# subdivisions=16
+batch=64
+subdivisions=16
width=416
height=416
channels=3
diff --git a/src/data.c b/src/data.c
index d9cedf5..88d5720 100644
--- a/src/data.c
+++ b/src/data.c
@@ -827,6 +827,9 @@
} else if (a.type == IMAGE_DATA){
*(a.im) = load_image_color(a.path, 0, 0);
*(a.resized) = resize_image(*(a.im), a.w, a.h);
+ }else if (a.type == LETTERBOX_DATA) {
+ *(a.im) = load_image_color(a.path, 0, 0);
+ *(a.resized) = letterbox_image(*(a.im), a.w, a.h);
} else if (a.type == TAG_DATA){
*a.d = load_data_tag(a.paths, a.n, a.m, a.classes, a.min, a.max, a.size, a.angle, a.aspect, a.hue, a.saturation, a.exposure);
}
diff --git a/src/data.h b/src/data.h
index e80beae..e50ad00 100644
--- a/src/data.h
+++ b/src/data.h
@@ -32,7 +32,7 @@
} data;
typedef enum {
- CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA
+ CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, LETTERBOX_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA
} data_type;
typedef struct load_args{
diff --git a/src/detector.c b/src/detector.c
index 77175b4..ac84c59 100644
--- a/src/detector.c
+++ b/src/detector.c
@@ -170,7 +170,7 @@
avg_loss = avg_loss*.9 + loss*.1;
i = get_current_batch(net);
- printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+ printf("\n %d: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
#ifdef OPENCV
if(!dont_show)
@@ -204,301 +204,290 @@
static int get_coco_image_id(char *filename)
{
- char *p = strrchr(filename, '_');
- return atoi(p+1);
+ 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, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+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 = boxes[i].x - boxes[i].w/2.;
- float xmax = boxes[i].x + boxes[i].w/2.;
- float ymin = boxes[i].y - boxes[i].h/2.;
- float ymax = boxes[i].y + boxes[i].h/2.;
+ 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;
+ 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;
+ float bx = xmin;
+ float by = ymin;
+ float bw = xmax - xmin;
+ float bh = ymax - ymin;
- for(j = 0; j < classes; ++j){
- if (probs[i][j]) fprintf(fp, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f},\n", image_id, coco_ids[j], bx, by, bw, bh, probs[i][j]);
- }
- }
+ 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, box *boxes, float **probs, int total, int classes, int w, int h)
+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 = boxes[i].x - boxes[i].w/2.;
- float xmax = boxes[i].x + boxes[i].w/2.;
- float ymin = boxes[i].y - boxes[i].h/2.;
- float ymax = boxes[i].y + boxes[i].h/2.;
+ 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 < 0) xmin = 0;
- if (ymin < 0) ymin = 0;
- if (xmax > w) xmax = w;
- if (ymax > h) ymax = h;
+ 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 (probs[i][j]) fprintf(fps[j], "%s %f %f %f %f %f\n", id, probs[i][j],
- xmin, ymin, xmax, ymax);
- }
- }
+ 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, box *boxes, float **probs, int total, int classes, int w, int h)
+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 = boxes[i].x - boxes[i].w/2.;
- float xmax = boxes[i].x + boxes[i].w/2.;
- float ymin = boxes[i].y - boxes[i].h/2.;
- float ymax = boxes[i].y + boxes[i].h/2.;
+ 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;
+ 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 class_id = j;
- if (probs[i][class_id]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j+1, probs[i][class_id],
- xmin, ymin, xmax, ymax);
- }
- }
+ for (j = 0; j < classes; ++j) {
+ int class = j;
+ if (dets[i].prob[class]) fprintf(fp, "%d %d %f %f %f %f %f\n", id, j + 1, dets[i].prob[class],
+ xmin, ymin, xmax, ymax);
+ }
+ }
}
-void validate_detector(char *datacfg, char *cfgfile, char *weightfile)
+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);
+ 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));
+ 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);
+ list *plist = get_paths(valid_images);
+ char **paths = (char **)list_to_array(plist);
- layer l = net.layers[net.n-1];
- int classes = l.classes;
+ 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")){
- snprintf(buff, 1024, "%s/coco_results.json", prefix);
- fp = fopen(buff, "w");
- fprintf(fp, "[\n");
- coco = 1;
- } else if(0==strcmp(type, "imagenet")){
- snprintf(buff, 1024, "%s/imagenet-detection.txt", prefix);
- fp = fopen(buff, "w");
- imagenet = 1;
- classes = 200;
- } else {
- fps = calloc(classes, sizeof(FILE *));
- for(j = 0; j < classes; ++j){
- snprintf(buff, 1024, "%s/%s%s.txt", prefix, base, names[j]);
- fps[j] = fopen(buff, "w");
- }
- }
+ 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 = 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");
+ }
+ }
- 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;
- int m = plist->size;
- int i=0;
- int t;
+ float thresh = .005;
+ float nms = .45;
- float thresh = .005;
- float nms = .45;
+ 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));
- int detection_count = 0;
+ load_args args = { 0 };
+ args.w = net.w;
+ args.h = net.h;
+ args.type = IMAGE_DATA;
+ //args.type = LETTERBOX_DATA;
- 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;
-
- 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;
- 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;
- }
+ 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_v3(dets, nboxes, classes, nms);
+ if (coco) {
+ print_cocos(fp, path, dets, nboxes, classes, w, h);
}
-
- if (coco){
- print_cocos(fp, path, boxes, probs, l.w*l.h*l.n, classes, w, h);
- } else if (imagenet){
- print_imagenet_detections(fp, i+t-nthreads+1, boxes, probs, l.w*l.h*l.n, classes, w, h);
- } else {
- print_detector_detections(fps, id, boxes, probs, l.w*l.h*l.n, classes, w, h);
- }
- free(id);
- free_image(val[t]);
- free_image(val_resized[t]);
- }
- }
- for(j = 0; j < classes; ++j){
- if(fps) fclose(fps[j]);
- }
- if(coco){
- fseek(fp, -2, SEEK_CUR);
- 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));
+ 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) {
+ fseek(fp, -2, SEEK_CUR);
+ fprintf(fp, "\n]\n");
+ fclose(fp);
+ }
+ fprintf(stderr, "Total Detection Time: %f Seconds\n", time(0) - start);
}
void validate_detector_recall(char *datacfg, char *cfgfile, char *weightfile)
{
- 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));
+ network net = parse_network_cfg_custom(cfgfile, 1);
+ if (weightfile) {
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ 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);
+ list *plist = get_paths(valid_images);
+ char **paths = (char **)list_to_array(plist);
- layer l = net.layers[net.n-1];
- int classes = l.classes;
+ layer l = net.layers[net.n - 1];
- int j, k;
- 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 j, k;
- int m = plist->size;
- int i=0;
+ int m = plist->size;
+ int i = 0;
- float thresh = .001;// .001; // .2;
- float iou_thresh = .5;
- float nms = .4;
+ float thresh = .001;
+ float iou_thresh = .5;
+ float nms = .4;
- int detection_count = 0, truth_count = 0;
+ int total = 0;
+ int correct = 0;
+ int proposals = 0;
+ float avg_iou = 0;
- 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_color(path, 0, 0);
+ 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_v3(dets, nboxes, 1, nms);
- for(i = 0; i < m; ++i){
- char *path = paths[i];
- image orig = load_image_color(path, 0, 0);
- image sized = resize_image(orig, net.w, net.h);
- char *id = basecfg(path);
- network_predict(net, sized.data);
- get_region_boxes(l, 1, 1, thresh, probs, boxes, 1, 0);
- if (nms) do_nms(boxes, probs, l.w*l.h*l.n, 1, 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);
- 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);
- truth_count += num_labels;
- for(k = 0; k < l.w*l.h*l.n; ++k){
- if(probs[k][0] > thresh){
- ++proposals;
- }
- }
+ 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 < l.w*l.h*l.n; ++k) {
- float iou = box_iou(boxes[k], t);
- if (probs[k][0] > thresh && iou > best_iou) {
+ 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;
- }
- }
+ avg_iou += best_iou;
+ if (best_iou > iou_thresh) {
+ ++correct;
+ }
+ }
- fprintf(stderr, "%5d %5d %5d\tRPs/Img: %.2f\tIOU: %.2f%%\tRecall:%.2f%%\n", i, correct, total, (float)proposals/(i+1), avg_iou*100/total, 100.*correct/total);
- free(id);
- free_image(orig);
- free_image(sized);
- }
- printf("\n truth_count = %d \n", truth_count);
+ 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 {
@@ -537,7 +526,6 @@
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);
@@ -553,10 +541,6 @@
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;
@@ -576,6 +560,7 @@
args.w = net.w;
args.h = net.h;
args.type = IMAGE_DATA;
+ //args.type = LETTERBOX_DATA;
//const float thresh_calc_avg_iou = 0.24;
float avg_iou = 0;
@@ -614,8 +599,12 @@
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);
+
+ int nboxes = 0;
+ int letterbox = (args.type == LETTERBOX_DATA);
+ float hier_thresh = 0;
+ detection *dets = get_network_boxes(&net, 1, 1, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
+ if (nms) do_nms_sort_v3(dets, nboxes, l.classes, nms);
char labelpath[4096];
find_replace(path, "images", "labels", labelpath);
@@ -646,15 +635,15 @@
truth_dif = read_boxes(labelpath_dif, &num_labels_dif);
}
- for (i = 0; i < (l.w*l.h*l.n); ++i) {
+ for (i = 0; i < nboxes; ++i) {
int class_id;
for (class_id = 0; class_id < classes; ++class_id) {
- float prob = probs[i][class_id];
+ float prob = dets[i].prob[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].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;
@@ -667,8 +656,8 @@
{
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);
+ // 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;
@@ -686,7 +675,7 @@
// if object is difficult then remove detection
for (j = 0; j < num_labels_dif; ++j) {
box t = { truth_dif[j].x, truth_dif[j].y, truth_dif[j].w, truth_dif[j].h };
- float current_iou = box_iou(boxes[i], t);
+ float current_iou = box_iou(dets[i].bbox, t);
if (current_iou > iou_thresh && class_id == truth_dif[j].id) {
--detections_count;
break;
@@ -709,6 +698,7 @@
unique_truth_count += num_labels;
+ free_detections(dets, nboxes);
free(id);
free_image(val[t]);
free_image(val_resized[t]);
@@ -830,9 +820,9 @@
}
#ifdef OPENCV
-void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show)
+void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show)
{
- printf("\n num_of_clusters = %d, final_width = %d, final_height = %d \n", num_of_clusters, final_width, final_height);
+ printf("\n num_of_clusters = %d, width = %d, height = %d \n", num_of_clusters, width, height);
//float pointsdata[] = { 1,1, 2,2, 6,6, 5,5, 10,10 };
float *rel_width_height_array = calloc(1000, sizeof(float));
@@ -862,8 +852,8 @@
{
number_of_boxes++;
rel_width_height_array = realloc(rel_width_height_array, 2 * number_of_boxes * sizeof(float));
- rel_width_height_array[number_of_boxes * 2 - 2] = truth[j].w * final_width;
- rel_width_height_array[number_of_boxes * 2 - 1] = truth[j].h * final_height;
+ 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);
}
}
@@ -967,15 +957,15 @@
for (j = 0; j < num_of_clusters; ++j) {
CvPoint pt1, pt2;
pt1.x = pt1.y = 0;
- pt2.x = centers->data.fl[j * 2] * img_size / final_width;
- pt2.y = centers->data.fl[j * 2 + 1] * img_size / final_height;
+ pt2.x = centers->data.fl[j * 2] * img_size / width;
+ pt2.y = centers->data.fl[j * 2 + 1] * img_size / height;
cvRectangle(img, pt1, pt2, CV_RGB(255, 255, 255), 1, 8, 0);
}
for (i = 0; i < number_of_boxes; ++i) {
CvPoint pt;
- pt.x = points->data.fl[i * 2] * img_size / final_width;
- pt.y = points->data.fl[i * 2 + 1] * img_size / final_height;
+ pt.x = points->data.fl[i * 2] * img_size / width;
+ pt.y = points->data.fl[i * 2 + 1] * img_size / height;
int cluster_idx = labels->data.i[i];
int red_id = (cluster_idx * (uint64_t)123 + 55) % 255;
int green_id = (cluster_idx * (uint64_t)321 + 33) % 255;
@@ -995,7 +985,7 @@
cvReleaseMat(&labels);
}
#else
-void calc_anchors(char *datacfg, int num_of_clusters, int final_width, int final_height, int show) {
+void calc_anchors(char *datacfg, int num_of_clusters, int width, int height, int show) {
printf(" k-means++ can't be used without OpenCV, because there is used cvKMeans2 implementation \n");
}
#endif // OPENCV
@@ -1030,9 +1020,9 @@
strtok(input, "\n");
}
image im = load_image_color(input,0,0);
- int letter = 0;
+ int letterbox = 0;
image sized = resize_image(im, net.w, net.h);
- //image sized = letterbox_image(im, net.w, net.h); letter = 1;
+ //image sized = letterbox_image(im, net.w, net.h); letterbox = 1;
layer l = net.layers[net.n-1];
//box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
@@ -1047,7 +1037,7 @@
// if (nms) do_nms_sort(boxes, probs, l.w*l.h*l.n, l.classes, nms);
//draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
int nboxes = 0;
- detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letter);
+ detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
if (nms) do_nms_sort_v3(dets, nboxes, l.classes, nms);
draw_detections_v3(im, dets, nboxes, thresh, names, alphabet, l.classes);
free_detections(dets, nboxes);
@@ -1076,14 +1066,15 @@
int show = find_arg(argc, argv, "-show");
int http_stream_port = find_int_arg(argc, argv, "-http_port", -1);
char *out_filename = find_char_arg(argc, argv, "-out_filename", 0);
+ char *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 hier_thresh = find_float_arg(argc, argv, "-hier", .5);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);
int num_of_clusters = find_int_arg(argc, argv, "-num_of_clusters", 5);
- int final_width = find_int_arg(argc, argv, "-final_width", 13);
- int final_heigh = find_int_arg(argc, argv, "-final_heigh", 13);
+ int width = find_int_arg(argc, argv, "-width", 13);
+ int heigh = find_int_arg(argc, argv, "-heigh", 13);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
@@ -1121,10 +1112,10 @@
char *filename = (argc > 6) ? argv[6]: 0;
if(0==strcmp(argv[2], "test")) test_detector(datacfg, cfg, weights, filename, thresh, hier_thresh, dont_show);
else if(0==strcmp(argv[2], "train")) train_detector(datacfg, cfg, weights, gpus, ngpus, clear, dont_show);
- else if(0==strcmp(argv[2], "valid")) validate_detector(datacfg, cfg, weights);
+ else if(0==strcmp(argv[2], "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);
- else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, final_width, final_heigh, show);
+ else if(0==strcmp(argv[2], "calc_anchors")) calc_anchors(datacfg, num_of_clusters, width, heigh, show);
else if(0==strcmp(argv[2], "demo")) {
list *options = read_data_cfg(datacfg);
int classes = option_find_int(options, "classes", 20);
diff --git a/src/network.c b/src/network.c
index 8619158..51d290c 100644
--- a/src/network.c
+++ b/src/network.c
@@ -374,10 +374,14 @@
resize_maxpool_layer(&l, w, h);
}else if(l.type == REGION){
resize_region_layer(&l, w, h);
+ }else if (l.type == YOLO) {
+ resize_yolo_layer(&l, w, h);
}else if(l.type == ROUTE){
resize_route_layer(&l, net);
}else if (l.type == SHORTCUT) {
resize_shortcut_layer(&l, w, h);
+ }else if (l.type == UPSAMPLE) {
+ resize_upsample_layer(&l, w, h);
}else if(l.type == REORG){
resize_reorg_layer(&l, w, h);
}else if(l.type == AVGPOOL){
@@ -539,12 +543,14 @@
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
int i, j;
for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
- get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
+ get_region_boxes(l, w, h, thresh, probs, boxes, 0, map);
for (j = 0; j < l.w*l.h*l.n; ++j) {
dets[j].classes = l.classes;
dets[j].bbox = boxes[j];
dets[j].objectness = 1;
- for (i = 0; i < l.classes; ++i) dets[j].prob[i] = probs[j][i];
+ for (i = 0; i < l.classes; ++i) {
+ dets[j].prob[i] = probs[j][i];
+ }
}
free(boxes);
diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index c8e2ff5..2925b26 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -378,9 +378,26 @@
return;
}
- cuda_pull_array(l.output_gpu, state.input, l.batch*l.inputs);
- forward_yolo_layer(l, state);
+ //cuda_pull_array(l.output_gpu, state.input, l.batch*l.inputs);
+ float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
+ cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
+ float *truth_cpu = 0;
+ if (state.truth) {
+ int num_truth = l.batch*l.truths;
+ truth_cpu = calloc(num_truth, sizeof(float));
+ cuda_pull_array(state.truth, truth_cpu, num_truth);
+ }
+ network_state cpu_state = state;
+ cpu_state.net = state.net;
+ cpu_state.index = state.index;
+ cpu_state.train = state.train;
+ cpu_state.truth = truth_cpu;
+ cpu_state.input = in_cpu;
+ forward_yolo_layer(l, cpu_state);
+ //forward_yolo_layer(l, state);
cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
+ free(in_cpu);
+ if (cpu_state.truth) free(cpu_state.truth);
}
void backward_yolo_layer_gpu(const layer l, network_state state)
--
Gitblit v1.10.0