From aa5996d58e68edfbefe51061856aecd549dd09c4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 13 Jan 2015 01:27:08 +0000
Subject: [PATCH] Faster
---
src/cnn.c | 88 ++++++++++++++++++++++++++++++++++++-------
1 files changed, 73 insertions(+), 15 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index 1c74e5c..e587a1b 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -71,11 +71,11 @@
}
-void train_detection_net()
+void train_detection_net(char *cfgfile)
{
float avg_loss = 1;
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
- network net = parse_network_cfg("cfg/detnet.cfg");
+ network net = parse_network_cfg(cfgfile);
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
srand(time(0));
@@ -115,6 +115,57 @@
}
}
+void validate_detection_net(char *cfgfile)
+{
+ network net = parse_network_cfg(cfgfile);
+ 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/imagenet/detection.val");
+ char **paths = (char **)list_to_array(plist);
+
+ int m = plist->size;
+ int i = 0;
+ int splits = 50;
+ 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, 245, 224, 224, &buffer);
+ clock_t time;
+ for(i = 1; i <= splits; ++i){
+ 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);
+ if(i != splits) load_thread = load_data_thread(part, num, 0, 0, 245, 224, 224, &buffer);
+
+ fprintf(stderr, "Loaded: %lf seconds\n", sec(clock()-time));
+ matrix pred = network_predict_data(net, val);
+ int j, k;
+ for(j = 0; j < pred.rows; ++j){
+ for(k = 0; k < pred.cols; k += 5){
+ if (pred.vals[j][k] > .005){
+ int index = k/5;
+ int r = index/7;
+ int c = index%7;
+ float y = (32.*(r + pred.vals[j][k+1]))/224.;
+ float x = (32.*(c + pred.vals[j][k+2]))/224.;
+ float h = (256.*(pred.vals[j][k+3]))/224.;
+ float w = (256.*(pred.vals[j][k+4]))/224.;
+ printf("%d %f %f %f %f %f\n", (i-1)*m/splits + j + 1, pred.vals[j][k], y, x, h, w);
+ }
+ }
+ }
+
+ time=clock();
+ free_data(val);
+ }
+}
+
void train_imagenet_distributed(char *address)
{
float avg_loss = 1;
@@ -159,10 +210,10 @@
//network net = parse_network_cfg("/home/pjreddie/imagenet_backup/alexnet_1270.cfg");
srand(time(0));
network net = parse_network_cfg(cfgfile);
- //set_learning_network(&net, net.learning_rate, 0, .0005);
+ 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 = 47900;
+ int i = 77700;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
char **paths = (char **)list_to_array(plist);
@@ -177,7 +228,9 @@
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- normalize_data_rows(train);
+ //normalize_data_rows(train);
+ translate_data_rows(train, -128);
+ scale_data_rows(train, 1./128);
load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
@@ -265,8 +318,10 @@
int i = 0;
char *filename = "data/test.jpg";
- image im = load_image_color(filename, 224, 224);
- z_normalize_image(im);
+ image im = load_image_color(filename, 256, 256);
+ //z_normalize_image(im);
+ translate_image(im, -128);
+ scale_image(im, 1/128.);
float *X = im.data;
forward_network(net, X, 0, 1);
for(i = 0; i < net.n; ++i){
@@ -352,9 +407,9 @@
if(count%10 == 0){
float test_acc = network_accuracy(net, test);
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,sec(clock()-time));
- char buff[256];
- sprintf(buff, "unikitty/cifar10_%d.cfg", count);
- save_network(net, buff);
+ //char buff[256];
+ //sprintf(buff, "unikitty/cifar10_%d.cfg", count);
+ //save_network(net, buff);
}else{
printf("%d: Loss: %f, Time: %lf seconds\n", count, loss, sec(clock()-time));
}
@@ -482,7 +537,7 @@
cvWaitKey(0);
}
-void test_gpu_net()
+void test_correct_nist()
{
srand(222222);
network net = parse_network_cfg("cfg/nist.cfg");
@@ -523,11 +578,12 @@
clock_t time;
int count = 0;
network net;
+
+ srand(222222);
+ net = parse_network_cfg("cfg/net.cfg");
int imgs = net.batch;
count = 0;
- srand(222222);
- net = parse_network_cfg("cfg/net.cfg");
while(++count <= 5){
time=clock();
data train = load_data(paths, imgs, plist->size, labels, 1000, 256, 256);
@@ -624,9 +680,9 @@
}
#endif
- if(0==strcmp(argv[1], "detection")) train_detection_net();
- else if(0==strcmp(argv[1], "cifar")) train_cifar10();
+ if(0==strcmp(argv[1], "cifar")) train_cifar10();
else if(0==strcmp(argv[1], "test_correct")) test_correct_alexnet();
+ else if(0==strcmp(argv[1], "test_correct_nist")) test_correct_nist();
else if(0==strcmp(argv[1], "test")) test_imagenet();
else if(0==strcmp(argv[1], "server")) run_server();
@@ -638,6 +694,7 @@
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], "nist")) train_nist(argv[2]);
else if(0==strcmp(argv[1], "train")) train_imagenet(argv[2]);
else if(0==strcmp(argv[1], "client")) train_imagenet_distributed(argv[2]);
@@ -646,6 +703,7 @@
else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
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(argc < 4){
fprintf(stderr, "usage: %s <function> <filename> <filename>\n", argv[0]);
return 0;
--
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