From d407bffde934ea4c1ee392f24cdf26d9a987199b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 18 Nov 2014 21:51:04 +0000
Subject: [PATCH] checkpoint
---
src/cnn.c | 102 +++++++++++++++++++++++++++++----------------------
1 files changed, 58 insertions(+), 44 deletions(-)
diff --git a/src/cnn.c b/src/cnn.c
index de37bc3..5399679 100644
--- a/src/cnn.c
+++ b/src/cnn.c
@@ -265,10 +265,8 @@
void test_parser()
{
- network net = parse_network_cfg("cfg/test_parser.cfg");
- save_network(net, "cfg/test_parser_1.cfg");
- network net2 = parse_network_cfg("cfg/test_parser_1.cfg");
- save_network(net2, "cfg/test_parser_2.cfg");
+ network net = parse_network_cfg("cfg/trained_imagenet.cfg");
+ save_network(net, "cfg/trained_imagenet_smaller.cfg");
}
void test_data()
@@ -278,9 +276,9 @@
free_data(train);
}
-void train_assira()
+void train_asirra()
{
- network net = parse_network_cfg("cfg/assira.cfg");
+ network net = parse_network_cfg("cfg/imagenet.cfg");
int imgs = 1000/net.batch+1;
//imgs = 1;
srand(2222222);
@@ -288,18 +286,19 @@
char *labels[] = {"cat","dog"};
clock_t time;
while(1){
- i += 1000;
+ i += 1;
time=clock();
data train = load_data_image_pathfile_random("data/assira/train.list", imgs*net.batch, labels, 2, 256, 256);
normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
- float loss = train_network_sgd(net, train, imgs);
- printf("%d: %f, Time: %lf seconds\n", i, loss, sec(clock()-time));
+ //float loss = train_network_data(net, train, imgs);
+ float loss = 0;
+ printf("%d: %f, Time: %lf seconds\n", i*net.batch*imgs, loss, sec(clock()-time));
free_data(train);
- if(i%10000==0){
+ if(i%10==0){
char buff[256];
- sprintf(buff, "cfg/assira_backup_%d.cfg", i);
+ sprintf(buff, "cfg/asirra_backup_%d.cfg", i);
save_network(net, buff);
}
//lr *= .99;
@@ -308,10 +307,12 @@
void train_imagenet()
{
- network net = parse_network_cfg("cfg/imagenet_backup_slowest_2340.cfg");
+ float avg_loss = 1;
+ network net = parse_network_cfg("/home/pjreddie/imagenet_backup/imagenet_2280.cfg");
+ //network net = parse_network_cfg("cfg/imagenet2.cfg");
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1000/net.batch+1;
- srand(6472345);
+ srand(time(0));
int i = 0;
char **labels = get_labels("/home/pjreddie/data/imagenet/cls.labels.list");
list *plist = get_paths("/data/imagenet/cls.train.list");
@@ -322,22 +323,51 @@
i += 1;
time=clock();
data train = load_data_random(imgs*net.batch, paths, plist->size, labels, 1000, 256, 256);
- normalize_data_rows(train);
+ //translate_data_rows(train, -144);
+ normalize_data_rows(train);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
#ifdef GPU
float loss = train_network_data_gpu(net, train, imgs);
- printf("%d: %f, %lf seconds, %d images\n", i, loss, sec(clock()-time), i*imgs*net.batch);
+ 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*net.batch);
#endif
free_data(train);
if(i%10==0){
char buff[256];
- sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_small_%d.cfg", i);
+ sprintf(buff, "/home/pjreddie/imagenet_backup/imagenet_%d.cfg", i);
save_network(net, buff);
}
}
}
+void validate_imagenet(char *filename)
+{
+ int i;
+ network net = parse_network_cfg(filename);
+ srand(time(0));
+
+ char **labels = get_labels("/home/pjreddie/data/imagenet/cls.val.labels.list");
+ char *path = "/home/pjreddie/data/imagenet/cls.val.list";
+
+ clock_t time;
+ float avg_acc = 0;
+ int splits = 50;
+ for(i = 0; i < splits; ++i){
+ time=clock();
+ data val = load_data_image_pathfile_part(path, i, splits, labels, 1000, 256, 256);
+ normalize_data_rows(val);
+ printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
+ time=clock();
+ #ifdef GPU
+ float acc = network_accuracy_gpu(net, val);
+ avg_acc += acc;
+ printf("%d: %f, %f avg, %lf seconds, %d images\n", i, acc, avg_acc/(i+1), sec(clock()-time), val.X.rows);
+ #endif
+ free_data(val);
+ }
+}
+
void train_imagenet_small()
{
network net = parse_network_cfg("cfg/imagenet_small.cfg");
@@ -369,7 +399,7 @@
void test_imagenet()
{
- network net = parse_network_cfg("cfg/imagenet_test.cfg");
+ network net = parse_network_cfg("cfg/imagenet_test.cfg");
//imgs=1;
srand(2222222);
int i = 0;
@@ -378,9 +408,9 @@
char filename[256];
int indexes[10];
while(1){
- gets(filename);
+ fgets(filename, 256, stdin);
image im = load_image_color(filename, 256, 256);
- normalize_image(im);
+ z_normalize_image(im);
printf("%d %d %d\n", im.h, im.w, im.c);
float *X = im.data;
time=clock();
@@ -395,9 +425,9 @@
}
}
-void test_visualize()
+void test_visualize(char *filename)
{
- network net = parse_network_cfg("cfg/imagenet.cfg");
+ network net = parse_network_cfg(filename);
visualize_network(net);
cvWaitKey(0);
}
@@ -518,35 +548,16 @@
data train = load_categorical_data_csv("data/mnist/mnist_train.csv", 0, 10);
data test = load_categorical_data_csv("data/mnist/mnist_test.csv",0,10);
translate_data_rows(train, -144);
- //scale_data_rows(train, 1./128);
translate_data_rows(test, -144);
- //scale_data_rows(test, 1./128);
- //randomize_data(train);
int count = 0;
- //clock_t start = clock(), end;
- int iters = 10000/net.batch;
+ int iters = 50000/net.batch;
while(++count <= 2000){
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters);
end = clock();
float test_acc = network_accuracy(net, test);
- //float test_acc = 0;
- printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, net.learning_rate, net.momentum, net.decay);
- /*printf("%f %f %f %f %f\n", mean_array(get_network_output_layer(net,0), 100),
- mean_array(get_network_output_layer(net,1), 100),
- mean_array(get_network_output_layer(net,2), 100),
- mean_array(get_network_output_layer(net,3), 100),
- mean_array(get_network_output_layer(net,4), 100));
- */
- //save_network(net, "cfg/nist_final2.cfg");
-
- //printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
- //end = clock();
- //printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
- //start=end;
- //lr *= .5;
+ printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC);
}
- //save_network(net, "cfg/nist_basic_trained.cfg");
}
void test_ensemble()
@@ -1016,15 +1027,18 @@
int main(int argc, char *argv[])
{
- if(argc != 2){
+ if(argc < 2){
fprintf(stderr, "usage: %s <function>\n", argv[0]);
return 0;
}
if(0==strcmp(argv[1], "train")) train_imagenet();
+ else if(0==strcmp(argv[1], "asirra")) train_asirra();
+ else if(0==strcmp(argv[1], "nist")) train_nist();
else if(0==strcmp(argv[1], "train_small")) train_imagenet_small();
else if(0==strcmp(argv[1], "test_correct")) test_gpu_net();
else if(0==strcmp(argv[1], "test")) test_imagenet();
- else if(0==strcmp(argv[1], "visualize")) test_visualize();
+ else if(0==strcmp(argv[1], "visualize")) test_visualize(argv[2]);
+ else if(0==strcmp(argv[1], "valid")) validate_imagenet(argv[2]);
#ifdef GPU
else if(0==strcmp(argv[1], "test_gpu")) test_gpu_blas();
#endif
--
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