From 522e2a220cf2beffd6283374a7756bf7c85749ad Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 22 Sep 2015 23:36:57 +0000
Subject: [PATCH] hm
---
src/writing.c | 41 ++++++++++++++++++++++++++++++-----------
cfg/writing.cfg | 8 ++++----
src/data.c | 2 ++
src/data.h | 3 ++-
4 files changed, 38 insertions(+), 16 deletions(-)
diff --git a/cfg/writing.cfg b/cfg/writing.cfg
index 2c84b19..030deeb 100644
--- a/cfg/writing.cfg
+++ b/cfg/writing.cfg
@@ -1,10 +1,10 @@
[net]
-batch=64
-subdivisions=1
+batch=128
+subdivisions=2
height=256
width=256
channels=3
-learning_rate=0.00001
+learning_rate=0.0000001
momentum=0.9
decay=0.0005
seen=0
@@ -40,7 +40,7 @@
[convolutional]
filters=1
-size=5
+size=3
stride=1
pad=1
activation=logistic
diff --git a/src/data.c b/src/data.c
index 7ef1546..7574a44 100644
--- a/src/data.c
+++ b/src/data.c
@@ -553,6 +553,8 @@
*a.d = load_data(a.paths, a.n, a.m, a.labels, a.classes, a.w, a.h);
} else if (a.type == DETECTION_DATA){
*a.d = load_data_detection(a.n, a.paths, a.m, a.classes, a.w, a.h, a.num_boxes, a.background);
+ } else if (a.type == WRITING_DATA){
+ *a.d = load_data_writing(a.paths, a.n, a.m, a.w, a.h, a.downsample);
} else if (a.type == REGION_DATA){
*a.d = load_data_region(a.n, a.paths, a.m, a.w, a.h, a.num_boxes, a.classes);
} else if (a.type == COMPARE_DATA){
diff --git a/src/data.h b/src/data.h
index 1343460..c7809b5 100644
--- a/src/data.h
+++ b/src/data.h
@@ -26,7 +26,7 @@
} data;
typedef enum {
- CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA
+ CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA
} data_type;
typedef struct load_args{
@@ -37,6 +37,7 @@
char **labels;
int h;
int w;
+ int downsample;
int nh;
int nw;
int num_boxes;
diff --git a/src/writing.c b/src/writing.c
index e0c1d1a..ccfc844 100644
--- a/src/writing.c
+++ b/src/writing.c
@@ -19,17 +19,34 @@
load_weights(&net, weightfile);
}
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = 1024;
- int i = *net.seen/imgs;
+ int imgs = net.batch*net.subdivisions;
list *plist = get_paths("figures.list");
char **paths = (char **)list_to_array(plist);
- printf("%d\n", plist->size);
clock_t time;
- while(1){
- ++i;
+ int N = plist->size;
+ printf("N: %d\n", N);
+
+ data train, buffer;
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.paths = paths;
+ args.n = imgs;
+ args.m = N;
+ args.downsample = 1;
+ args.d = &buffer;
+ args.type = WRITING_DATA;
+
+ pthread_t load_thread = load_data_in_thread(args);
+ int epoch = (*net.seen)/N;
+ while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
time=clock();
- data train = load_data_writing(paths, imgs, plist->size, 256, 256, 1);
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data_in_thread(args);
printf("Loaded %lf seconds\n",sec(clock()-time));
+
time=clock();
float loss = train_network(net, train);
@@ -51,13 +68,15 @@
if(avg_loss == -1) avg_loss = loss;
avg_loss = avg_loss*.9 + loss*.1;
- printf("%d: %f, %f avg, %lf seconds, %d images\n", i, 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(i%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97;
- if(i%1000==0){
+ if(*net.seen/N > epoch){
+ epoch = *net.seen/N;
char buff[256];
- sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
- save_weights(net, buff);
+ sprintf(buff, "%s/%s_%d.weights",backup_directory,base, epoch);
+ //TODO
+ if(get_current_batch(net)%10 == 0)
+ save_weights(net, buff);
}
}
}
--
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