From 70d622ea54c55aa5489e71b769a92447a586c879 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 14 Jul 2014 05:07:51 +0000
Subject: [PATCH] Added batch to col2im, padding option
---
src/network.c | 112 +++++++++++++++++++++++++++++++++++++++++++-------------
1 files changed, 86 insertions(+), 26 deletions(-)
diff --git a/src/network.c b/src/network.c
index b75eddf..ef80110 100644
--- a/src/network.c
+++ b/src/network.c
@@ -113,10 +113,9 @@
fclose(fp);
}
+#ifdef GPU
void forward_network(network net, float *input, int train)
{
- int i;
- #ifdef GPU
cl_setup();
size_t size = get_network_input_size(net);
if(!net.input_cl){
@@ -126,16 +125,12 @@
}
cl_write_array(net.input_cl, input, size);
cl_mem input_cl = net.input_cl;
- #endif
+ int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- #ifdef GPU
forward_convolutional_layer_gpu(layer, input_cl);
input_cl = layer.output_cl;
- #else
- forward_convolutional_layer(layer, input);
- #endif
input = layer.output;
}
else if(net.types[i] == CONNECTED){
@@ -161,6 +156,41 @@
}
}
+#else
+
+void forward_network(network net, float *input, int train)
+{
+ int i;
+ for(i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ forward_convolutional_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ forward_connected_layer(layer, input, train);
+ input = layer.output;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ forward_softmax_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ forward_maxpool_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ forward_normalization_layer(layer, input);
+ input = layer.output;
+ }
+ }
+}
+#endif
+
void update_network(network net, float step, float momentum, float decay)
{
int i;
@@ -238,9 +268,10 @@
float sum = 0;
float *delta = get_network_delta(net);
float *out = get_network_output(net);
- int i, k = get_network_output_size(net);
- for(i = 0; i < k; ++i){
- //printf("%f, ", out[i]);
+ int i;
+ for(i = 0; i < get_network_output_size(net)*net.batch; ++i){
+ //if(i %get_network_output_size(net) == 0) printf("\n");
+ //printf("%5.2f %5.2f, ", out[i], truth[i]);
delta[i] = truth[i] - out[i];
sum += delta[i]*delta[i];
}
@@ -305,20 +336,38 @@
float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
{
- int i;
- float error = 0;
- int correct = 0;
- int pos = 0;
+ int batch = net.batch;
+ float *X = calloc(batch*d.X.cols, sizeof(float));
+ float *y = calloc(batch*d.y.cols, sizeof(float));
+
+ int i,j;
+ float sum = 0;
for(i = 0; i < n; ++i){
- int index = rand()%d.X.rows;
- float err = train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
+ for(j = 0; j < batch; ++j){
+ int index = rand()%d.X.rows;
+ memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
+ memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
+ }
+ float err = train_network_datum(net, X, y, step, momentum, decay);
+ sum += err;
+ //train_network_datum(net, X, y, step, momentum, decay);
+ /*
float *y = d.y.vals[index];
int class = get_predicted_class_network(net);
correct += (y[class]?1:0);
- if(y[1]){
- error += err;
- ++pos;
+ */
+
+/*
+ for(j = 0; j < d.y.cols*batch; ++j){
+ printf("%6.3f ", y[j]);
}
+ printf("\n");
+ for(j = 0; j < d.y.cols*batch; ++j){
+ printf("%6.3f ", get_network_output(net)[j]);
+ }
+ printf("\n");
+ printf("\n");
+ */
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
@@ -327,7 +376,9 @@
//}
}
//printf("Accuracy: %f\n",(float) correct/n);
- return error/pos;
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
{
@@ -448,7 +499,7 @@
int get_network_input_size(network net)
{
- return get_network_output_size_layer(net, 0);
+ return get_network_input_size_layer(net, 0);
}
image get_network_image_layer(network net, int i)
@@ -505,15 +556,24 @@
matrix network_predict_data(network net, data test)
{
- int i,j;
+ int i,j,b;
int k = get_network_output_size(net);
matrix pred = make_matrix(test.X.rows, k);
- for(i = 0; i < test.X.rows; ++i){
- float *out = network_predict(net, test.X.vals[i]);
- for(j = 0; j < k; ++j){
- pred.vals[i][j] = out[j];
+ float *X = calloc(net.batch*test.X.rows, sizeof(float));
+ for(i = 0; i < test.X.rows; i += net.batch){
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
+ }
+ float *out = network_predict(net, X);
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ for(j = 0; j < k; ++j){
+ pred.vals[i+b][j] = out[j+b*k];
+ }
}
}
+ free(X);
return pred;
}
--
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