From ae43c2bc32fbb838bfebeeaf2c2b058ccab5c83c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@burninator.cs.washington.edu>
Date: Thu, 23 Jun 2016 05:31:14 +0000
Subject: [PATCH] hi
---
src/imagenet.c | 43 +++++++++++++++++++++++--------------------
1 files changed, 23 insertions(+), 20 deletions(-)
diff --git a/src/imagenet.c b/src/imagenet.c
index 5d79483..1625526 100644
--- a/src/imagenet.c
+++ b/src/imagenet.c
@@ -19,10 +19,9 @@
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- //net.seen=0;
int imgs = 1024;
char **labels = get_labels("data/inet.labels.list");
- list *plist = get_paths("/data/imagenet/cls.train.list");
+ list *plist = get_paths("data/inet.train.list");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
int N = plist->size;
@@ -40,38 +39,39 @@
args.m = N;
args.labels = labels;
args.d = &buffer;
- args.type = CLASSIFICATION_DATA;
+ args.type = OLD_CLASSIFICATION_DATA;
load_thread = load_data_in_thread(args);
- int epoch = net.seen/N;
- while(1){
+ int epoch = (*net.seen)/N;
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- /*
- image im = float_to_image(256, 256, 3, train.X.vals[114]);
- show_image(im, "training");
- cvWaitKey(0);
- */
-
load_thread = load_data_in_thread(args);
printf("Loaded: %lf seconds\n", sec(clock()-time));
time=clock();
float loss = train_network(net, train);
- net.seen += imgs;
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%.3f: %f, %f avg, %lf seconds, %d images\n", (float)net.seen/N, loss, avg_loss, sec(clock()-time), net.seen);
+ printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
free_data(train);
- if(net.seen/N > epoch){
- epoch = net.seen/N;
+ if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
char buff[256];
sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
save_weights(net, buff);
- if(epoch%22 == 0) net.learning_rate *= .1;
+ }
+ if(*net.seen%1000 == 0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup",backup_directory,base);
+ save_weights(net, buff);
}
}
+ char buff[256];
+ sprintf(buff, "%s/%s.weights", backup_directory, base);
+ save_weights(net, buff);
+
pthread_join(load_thread, 0);
free_data(buffer);
free_network(net);
@@ -91,6 +91,7 @@
srand(time(0));
char **labels = get_labels("data/inet.labels.list");
+ //list *plist = get_paths("data/inet.suppress.list");
list *plist = get_paths("data/inet.val.list");
char **paths = (char **)list_to_array(plist);
@@ -114,7 +115,7 @@
args.m = 0;
args.labels = labels;
args.d = &buffer;
- args.type = CLASSIFICATION_DATA;
+ args.type = OLD_CLASSIFICATION_DATA;
pthread_t load_thread = load_data_in_thread(args);
for(i = 1; i <= splits; ++i){
@@ -132,7 +133,7 @@
printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock()-time));
time=clock();
- float *acc = network_accuracies(net, val);
+ float *acc = network_accuracies(net, val, 5);
avg_acc += acc[0];
avg_top5 += acc[1];
printf("%d: top1: %f, top5: %f, %lf seconds, %d images\n", i, avg_acc/i, avg_top5/i, sec(clock()-time), val.X.rows);
@@ -151,15 +152,17 @@
int i = 0;
char **names = get_labels("data/shortnames.txt");
clock_t time;
- char input[256];
int indexes[10];
+ char buff[256];
+ char *input = buff;
while(1){
if(filename){
strncpy(input, filename, 256);
}else{
printf("Enter Image Path: ");
fflush(stdout);
- fgets(input, 256, stdin);
+ input = fgets(input, 256, stdin);
+ if(!input) return;
strtok(input, "\n");
}
image im = load_image_color(input, 256, 256);
--
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