From cd8d53df21f3ad2810add2a8cff766c745f55a17 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 09 May 2014 22:14:52 +0000
Subject: [PATCH] So there WAS this huge bug. Gone now
---
src/network.c | 103 ++++++++++++++++++++++++++-------------------------
1 files changed, 52 insertions(+), 51 deletions(-)
diff --git a/src/network.c b/src/network.c
index a77a28e..b75eddf 100644
--- a/src/network.c
+++ b/src/network.c
@@ -19,6 +19,9 @@
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
net.output = 0;
+ #ifdef GPU
+ net.input_cl = 0;
+ #endif
return net;
}
@@ -40,17 +43,6 @@
fprintf(fp, "data=");
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
- /*
- int j,k;
- for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
- for(i = 0; i < l->n; ++i){
- for(j = l->c-1; j >= 0; --j){
- for(k = 0; k < l->size*l->size; ++k){
- fprintf(fp, "%g,", l->filters[i*(l->c*l->size*l->size)+j*l->size*l->size+k]);
- }
- }
- }
- */
fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l, int first)
@@ -121,18 +113,34 @@
fclose(fp);
}
-void forward_network(network net, float *input)
+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){
+ net.input_cl = clCreateBuffer(cl.context,
+ CL_MEM_READ_WRITE, size*sizeof(float), 0, &cl.error);
+ check_error(cl);
+ }
+ cl_write_array(net.input_cl, input, size);
+ cl_mem input_cl = net.input_cl;
+ #endif
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){
connected_layer layer = *(connected_layer *)net.layers[i];
- forward_connected_layer(layer, input);
+ forward_connected_layer(layer, input, train);
input = layer.output;
}
else if(net.types[i] == SOFTMAX){
@@ -263,9 +271,7 @@
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- learn_convolutional_layer(layer);
- //learn_convolutional_layer(layer);
- if(i != 0) backward_convolutional_layer(layer, prev_delta);
+ backward_convolutional_layer(layer, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@@ -281,8 +287,7 @@
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- learn_connected_layer(layer, prev_input);
- if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
+ backward_connected_layer(layer, prev_input, prev_delta);
}
}
return error;
@@ -290,7 +295,7 @@
float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
{
- forward_network(net, x);
+ forward_network(net, x, 1);
//int class = get_predicted_class_network(net);
float error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
@@ -332,7 +337,7 @@
int index = rand()%d.X.rows;
float *x = d.X.vals[index];
float *y = d.y.vals[index];
- forward_network(net, x);
+ forward_network(net, x, 1);
int class = get_predicted_class_network(net);
backward_network(net, x, y);
correct += (y[class]?1:0);
@@ -359,6 +364,27 @@
fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
}
+int get_network_input_size_layer(network net, int i)
+{
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ return layer.h*layer.w*layer.c;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ return layer.h*layer.w*layer.c;
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ return layer.inputs;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.inputs;
+ }
+ return 0;
+}
+
int get_network_output_size_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
@@ -382,36 +408,6 @@
return 0;
}
-/*
- int resize_network(network net, int h, int w, int c)
- {
- int i;
- for (i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer *layer = (convolutional_layer *)net.layers[i];
- layer->h = h;
- layer->w = w;
- layer->c = c;
- image output = get_convolutional_image(*layer);
- h = output.h;
- w = output.w;
- c = output.c;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer *layer = (maxpool_layer *)net.layers[i];
- layer->h = h;
- layer->w = w;
- layer->c = c;
- image output = get_maxpool_image(*layer);
- h = output.h;
- w = output.w;
- c = output.c;
- }
- }
- return 0;
- }
- */
-
int resize_network(network net, int h, int w, int c)
{
int i;
@@ -450,6 +446,11 @@
return get_network_output_size_layer(net, i);
}
+int get_network_input_size(network net)
+{
+ return get_network_output_size_layer(net, 0);
+}
+
image get_network_image_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
@@ -497,7 +498,7 @@
float *network_predict(network net, float *input)
{
- forward_network(net, input);
+ forward_network(net, input, 0);
float *out = get_network_output(net);
return out;
}
--
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