From feabcc31de9dfb93b59d5a598a03b617dabe86da Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 21 Apr 2015 19:58:03 +0000
Subject: [PATCH] gonna fuck shit up
---
src/detection.c | 127 ++++++++++++++++++++++++------------------
1 files changed, 72 insertions(+), 55 deletions(-)
diff --git a/src/detection.c b/src/detection.c
index eea6136..c61c799 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -81,7 +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");
}
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);
@@ -94,13 +96,11 @@
load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, net.w, net.h, side, side, background, &buffer);
/*
- image im = float_to_image(net.w, net.h, 3, train.X.vals[114]);
- image copy = copy_image(im);
- translate_image(copy, 1);
- scale_image(copy, .5);
- draw_detection(copy, train.y.vals[114], 7);
- free_image(copy);
- */
+ image im = float_to_image(net.w, net.h, 3, train.X.vals[114]);
+ image copy = copy_image(im);
+ draw_detection(copy, train.y.vals[114], 7);
+ free_image(copy);
+ */
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -118,49 +118,9 @@
}
}
-void validate_detection(char *cfgfile, char *weightfile)
+void predict_detections(network net, data d, float threshold, int offset, int classes, int nuisance, int background, int num_boxes, int per_box)
{
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- detection_layer *layer = get_network_detection_layer(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("/home/pjreddie/data/voc/val.txt");
- //list *plist = get_paths("/home/pjreddie/data/voc/val.expanded.txt");
- //list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
- char **paths = (char **)list_to_array(plist);
-
- int classes = layer->classes;
- int nuisance = layer->nuisance;
- int background = (layer->background && !nuisance);
- int num_boxes = sqrt(get_detection_layer_locations(*layer));
-
- int per_box = 4+classes+background+nuisance;
- int num_output = num_boxes*num_boxes*per_box;
-
- 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);
- clock_t time;
- for(i = 1; i <= splits; ++i){
- 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);
-
- fprintf(stderr, "%d: Loaded: %lf seconds\n", i, sec(clock()-time));
- matrix pred = network_predict_data(net, val);
+ 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){
@@ -178,13 +138,72 @@
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);
+ 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);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ detection_layer *layer = get_network_detection_layer(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("/home/pjreddie/data/voc/val.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/test.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/val.expanded.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
+ char **paths = (char **)list_to_array(plist);
+
+ int classes = layer->classes;
+ int nuisance = layer->nuisance;
+ int background = (layer->background && !nuisance);
+ int num_boxes = sqrt(get_detection_layer_locations(*layer));
+
+ int per_box = 4+classes+background+nuisance;
+ int num_output = num_boxes*num_boxes*per_box;
+
+ int m = plist->size;
+ int i = 0;
+ int splits = 100;
+
+ 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 = nthreads; i <= splits; i += nthreads){
time=clock();
- free_data(val);
+ 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));
+ 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();
}
}
@@ -203,8 +222,6 @@
fgets(filename, 256, stdin);
strtok(filename, "\n");
image im = load_image_color(filename, im_size, im_size);
- translate_image(im, -128);
- scale_image(im, 1/128.);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = im.data;
time=clock();
--
Gitblit v1.10.0