From 451ef0a0a6b595bb8e4a945633659b4d31f0a372 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 20 Apr 2015 15:46:12 +0000
Subject: [PATCH] It's time, to du-du-du-du-DU-DU-DUEL!! https://www.youtube.com/watch?v=IVmtUK_1jh4
---
src/image.c | 2
src/detection.c | 90 +++++++++++++++++++++++++++------------------
src/parser.c | 1
src/data.c | 25 ++++++++++++
src/data.h | 1
5 files changed, 82 insertions(+), 37 deletions(-)
diff --git a/src/data.c b/src/data.c
index 012d7cf..2b74386 100644
--- a/src/data.c
+++ b/src/data.c
@@ -408,6 +408,31 @@
return thread;
}
+matrix concat_matrix(matrix m1, matrix m2)
+{
+ int i, count = 0;
+ matrix m;
+ m.cols = m1.cols;
+ m.rows = m1.rows+m2.rows;
+ m.vals = calloc(m1.rows + m2.rows, sizeof(float*));
+ for(i = 0; i < m1.rows; ++i){
+ m.vals[count++] = m1.vals[i];
+ }
+ for(i = 0; i < m2.rows; ++i){
+ m.vals[count++] = m2.vals[i];
+ }
+ return m;
+}
+
+data concat_data(data d1, data d2)
+{
+ data d;
+ d.shallow = 1;
+ d.X = concat_matrix(d1.X, d2.X);
+ d.y = concat_matrix(d1.y, d2.y);
+ return d;
+}
+
data load_categorical_data_csv(char *filename, int target, int k)
{
data d;
diff --git a/src/data.h b/src/data.h
index 8e3e1d9..22fd248 100644
--- a/src/data.h
+++ b/src/data.h
@@ -50,5 +50,6 @@
void translate_data_rows(data d, float s);
void randomize_data(data d);
data *split_data(data d, int part, int total);
+data concat_data(data d1, data d2);
#endif
diff --git a/src/detection.c b/src/detection.c
index cba3d18..2610b48 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -81,9 +81,9 @@
if (imgnet){
plist = get_paths("/home/pjreddie/data/imagenet/det.train.list");
}else{
- plist = get_paths("/home/pjreddie/data/voc/trainall.txt");
+ //plist = get_paths("/home/pjreddie/data/voc/trainall.txt");
//plist = get_paths("/home/pjreddie/data/coco/trainval.txt");
- //plist = get_paths("/home/pjreddie/data/voc/all2007-2012.txt");
+ plist = get_paths("/home/pjreddie/data/voc/all2007-2012.txt");
}
paths = (char **)list_to_array(plist);
pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
@@ -118,6 +118,34 @@
}
}
+void predict_detections(network net, data d, float threshold, int offset, int classes, int nuisance, int background, int num_boxes, int per_box)
+{
+ matrix pred = network_predict_data(net, d);
+ int j, k, class;
+ for(j = 0; j < pred.rows; ++j){
+ for(k = 0; k < pred.cols; k += per_box){
+ float scale = 1.;
+ int index = k/per_box;
+ int row = index / num_boxes;
+ int col = index % num_boxes;
+ if (nuisance) scale = 1.-pred.vals[j][k];
+ for (class = 0; class < classes; ++class){
+ int ci = k+classes+background+nuisance;
+ float y = (pred.vals[j][ci + 0] + row)/num_boxes;
+ float x = (pred.vals[j][ci + 1] + col)/num_boxes;
+ float h = pred.vals[j][ci + 2]; //* distance_from_edge(row, num_boxes);
+ h = h*h;
+ float w = pred.vals[j][ci + 3]; //* distance_from_edge(col, num_boxes);
+ w = w*w;
+ float prob = scale*pred.vals[j][k+class+background+nuisance];
+ if(prob < threshold) continue;
+ printf("%d %d %f %f %f %f %f\n", offset + j, class, prob, y, x, h, w);
+ }
+ }
+ }
+ free_matrix(pred);
+}
+
void validate_detection(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
@@ -144,47 +172,37 @@
int m = plist->size;
int i = 0;
int splits = 100;
- int num = (i+1)*m/splits - i*m/splits;
- fprintf(stderr, "%d\n", m);
- data val, buffer;
- pthread_t load_thread = load_data_thread(paths, num, 0, 0, num_output, net.w, net.h, &buffer);
+ int nthreads = 4;
+ int t;
+ data *val = calloc(nthreads, sizeof(data));
+ data *buf = calloc(nthreads, sizeof(data));
+ pthread_t *thr = calloc(nthreads, sizeof(data));
+ for(t = 0; t < nthreads; ++t){
+ int num = (i+1+t)*m/splits - (i+t)*m/splits;
+ char **part = paths+((i+t)*m/splits);
+ thr[t] = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &(buf[t]));
+ }
+
clock_t time;
- for(i = 1; i <= splits; ++i){
+ for(i = nthreads; i <= splits; i += nthreads){
time=clock();
- pthread_join(load_thread, 0);
- val = buffer;
-
- num = (i+1)*m/splits - i*m/splits;
- char **part = paths+(i*m/splits);
- if(i != splits) load_thread = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &buffer);
+ for(t = 0; t < nthreads; ++t){
+ pthread_join(thr[t], 0);
+ val[t] = buf[t];
+ }
+ for(t = 0; t < nthreads && i < splits; ++t){
+ int num = (i+1+t)*m/splits - (i+t)*m/splits;
+ char **part = paths+((i+t)*m/splits);
+ thr[t] = load_data_thread(part, num, 0, 0, num_output, net.w, net.h, &(buf[t]));
+ }
fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
- matrix pred = network_predict_data(net, val);
- int j, k, class;
- for(j = 0; j < pred.rows; ++j){
- for(k = 0; k < pred.cols; k += per_box){
- float scale = 1.;
- int index = k/per_box;
- int row = index / num_boxes;
- int col = index % num_boxes;
- if (nuisance) scale = 1.-pred.vals[j][k];
- for (class = 0; class < classes; ++class){
- int ci = k+classes+background+nuisance;
- float y = (pred.vals[j][ci + 0] + row)/num_boxes;
- float x = (pred.vals[j][ci + 1] + col)/num_boxes;
- float h = pred.vals[j][ci + 2]; //* distance_from_edge(row, num_boxes);
- h = h*h;
- float w = pred.vals[j][ci + 3]; //* distance_from_edge(col, num_boxes);
- w = w*w;
- float prob = scale*pred.vals[j][k+class+background+nuisance];
- if(prob < .001) continue;
- printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, prob, y, x, h, w);
- }
- }
+ for(t = 0; t < nthreads; ++t){
+ predict_detections(net, val[t], .01, (i-nthreads+t)*m/splits, classes, nuisance, background, num_boxes, per_box);
+ free_data(val[t]);
}
time=clock();
- free_data(val);
}
}
diff --git a/src/image.c b/src/image.c
index 1daea27..7509ce5 100644
--- a/src/image.c
+++ b/src/image.c
@@ -603,12 +603,12 @@
exit(0);
}
image out = ipl_to_image(src);
+ cvReleaseImage(&src);
if((h && w) && (h != out.h || w != out.w)){
image resized = resize_image(out, w, h);
free_image(out);
out = resized;
}
- cvReleaseImage(&src);
return out;
}
diff --git a/src/parser.c b/src/parser.c
index ca60ef7..0f13d77 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -623,6 +623,7 @@
fread(&net->momentum, sizeof(float), 1, fp);
fread(&net->decay, sizeof(float), 1, fp);
fread(&net->seen, sizeof(int), 1, fp);
+ fprintf(stderr, "%f %f %f %d\n", net->learning_rate, net->momentum, net->decay, net->seen);
int i;
for(i = 0; i < net->n && i < cutoff; ++i){
--
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