From 0f645836f193e75c4c3b718369e6fab15b5d19c5 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 11 Feb 2015 03:41:03 +0000
Subject: [PATCH] Detection is back, baby\!
---
src/darknet.c | 121 +++++++++++++++++++++++++++------------
1 files changed, 83 insertions(+), 38 deletions(-)
diff --git a/src/darknet.c b/src/darknet.c
index 0b93aa6..92a9196 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -57,8 +57,8 @@
int d = im.w/side;
int y = r*d+box[j+1]*d;
int x = c*d+box[j+2]*d;
- int h = box[j+3]*256;
- int w = box[j+4]*256;
+ int h = box[j+3]*im.h;
+ int w = box[j+4]*im.w;
//printf("%f %f %f %f\n", box[j+1], box[j+2], box[j+3], box[j+4]);
//printf("%d %d %d %d\n", x, y, w, h);
//printf("%d %d %d %d\n", x-w/2, y-h/2, x+w/2, y+h/2);
@@ -70,54 +70,79 @@
cvWaitKey(0);
}
-
-void train_detection_net(char *cfgfile)
+char *basename(char *cfgfile)
{
+ char *c = cfgfile;
+ char *next;
+ while((next = strchr(c, '/')))
+ {
+ c = next+1;
+ }
+ c = copy_string(c);
+ next = strchr(c, '_');
+ if (next) *next = 0;
+ next = strchr(c, '.');
+ if (next) *next = 0;
+ return c;
+}
+
+void train_detection_net(char *cfgfile, char *weightfile)
+{
+ char *base = basename(cfgfile);
+ printf("%s\n", base);
float avg_loss = 1;
- //network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1024;
+ int imgs = 128;
srand(time(0));
//srand(23410);
- int i = 0;
- list *plist = get_paths("/home/pjreddie/data/imagenet/horse.txt");
+ int i = net.seen/imgs;
+ list *plist = get_paths("/home/pjreddie/data/imagenet/horse_pos.txt");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
data train, buffer;
- pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, 256, 256, 7, 7, 256, &buffer);
+ int im_dim = 512;
+ int jitter = 64;
+ pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, im_dim, im_dim, 7, 7, jitter, &buffer);
clock_t time;
while(1){
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- load_thread = load_data_detection_thread(imgs, paths, plist->size, 256, 256, 7, 7, 256, &buffer);
- //data train = load_data_detection_random(imgs, paths, plist->size, 224, 224, 7, 7, 256);
+ load_thread = load_data_detection_thread(imgs, paths, plist->size, im_dim, im_dim, 7, 7, jitter, &buffer);
-/*
- image im = float_to_image(224, 224, 3, train.X.vals[923]);
+ /*
+ image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[923]);
draw_detection(im, train.y.vals[923], 7);
+ show_image(im, "truth");
+ cvWaitKey(0);
*/
- normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
+ net.seen += imgs;
avg_loss = avg_loss*.9 + loss*.1;
printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
if(i%100==0){
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/detnet_%d.cfg", i);
- save_network(net, buff);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);
+ save_weights(net, buff);
}
free_data(train);
}
}
-void validate_detection_net(char *cfgfile)
+void validate_detection_net(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
srand(time(0));
@@ -137,7 +162,6 @@
time=clock();
pthread_join(load_thread, 0);
val = buffer;
- normalize_data_rows(val);
num = (i+1)*m/splits - i*m/splits;
char **part = paths+(i*m/splits);
@@ -206,20 +230,13 @@
}
*/
-char *basename(char *cfgfile)
+void convert(char *cfgfile, char *outfile, char *weightfile)
{
- char *c = cfgfile;
- char *next;
- while((next = strchr(c, '/')))
- {
- c = next+1;
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
}
- c = copy_string(c);
- next = strchr(c, '_');
- if (next) *next = 0;
- next = strchr(c, '.');
- if (next) *next = 0;
- return c;
+ save_network(net, outfile);
}
void train_imagenet(char *cfgfile, char *weightfile)
@@ -232,8 +249,6 @@
if(weightfile){
load_weights(&net, weightfile);
}
- //test_learn_bias(*(convolutional_layer *)net.layers[1]);
- //set_learning_network(&net, net.learning_rate, 0, net.decay);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
int i = net.seen/imgs;
@@ -279,7 +294,7 @@
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
- list *plist = get_paths("/home/pjreddie/data/imagenet/cls.val.list");
+ list *plist = get_paths("/data/imagenet/cls.val.list");
char **paths = (char **)list_to_array(plist);
int m = plist->size;
free_list(plist);
@@ -312,9 +327,12 @@
}
}
-void test_detection(char *cfgfile)
+void test_detection(char *cfgfile, char *weightfile)
{
network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
set_batch_network(&net, 1);
srand(2222222);
clock_t time;
@@ -323,7 +341,8 @@
fgets(filename, 256, stdin);
strtok(filename, "\n");
image im = load_image_color(filename, 224, 224);
- z_normalize_image(im);
+ 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();
@@ -386,6 +405,30 @@
cvWaitKey(0);
}
+void test_voc_segment(char *cfgfile, char *weightfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ while(1){
+ char filename[256];
+ fgets(filename, 256, stdin);
+ strtok(filename, "\n");
+ image im = load_image_color(filename, 500, 500);
+ //resize_network(net, im.h, im.w, im.c);
+ translate_image(im, -128);
+ scale_image(im, 1/128.);
+ //float *predictions = network_predict(net, im.data);
+ network_predict(net, im.data);
+ free_image(im);
+ image output = get_network_image_layer(net, net.n-2);
+ show_image(output, "Segment Output");
+ cvWaitKey(0);
+ }
+}
+
void test_imagenet(char *cfgfile)
{
network net = parse_network_cfg(cfgfile);
@@ -764,25 +807,27 @@
fprintf(stderr, "usage: %s <function> <filename>\n", argv[0]);
return 0;
}
- else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2]);
+ else if(0==strcmp(argv[1], "detection")) train_detection_net(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "test")) test_imagenet(argv[2]);
else if(0==strcmp(argv[1], "dog")) test_dog(argv[2]);
else if(0==strcmp(argv[1], "ctrain")) train_cifar10(argv[2]);
else if(0==strcmp(argv[1], "nist")) train_nist(argv[2]);
else if(0==strcmp(argv[1], "ctest")) test_cifar10(argv[2]);
else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2], (argc > 3)? argv[3] : 0);
+ else if(0==strcmp(argv[1], "testseg")) test_voc_segment(argv[2], (argc > 3)? argv[3] : 0);
//else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
- else if(0==strcmp(argv[1], "detect")) test_detection(argv[2]);
+ else if(0==strcmp(argv[1], "detect")) test_detection(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "init")) test_init(argv[2]);
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2], (argc > 3)? argv[3] : 0);
else if(0==strcmp(argv[1], "testnist")) test_nist(argv[2]);
- else if(0==strcmp(argv[1], "validetect")) validate_detection_net(argv[2]);
+ else if(0==strcmp(argv[1], "validetect")) validate_detection_net(argv[2], (argc > 3)? argv[3] : 0);
else if(argc < 4){
fprintf(stderr, "usage: %s <function> <filename> <filename>\n", argv[0]);
return 0;
}
else if(0==strcmp(argv[1], "compare")) compare_nist(argv[2], argv[3]);
+ else if(0==strcmp(argv[1], "convert")) convert(argv[2], argv[3], (argc > 4)? argv[4] : 0);
else if(0==strcmp(argv[1], "scale")) scale_rate(argv[2], atof(argv[3]));
fprintf(stderr, "Success!\n");
return 0;
--
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