From 7756cccb793bb4950c241f2804195ea859d1b407 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 14 Oct 2014 05:31:48 +0000
Subject: [PATCH] Refactored connected to use blas
---
src/network.c | 139 +++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 124 insertions(+), 15 deletions(-)
diff --git a/src/network.c b/src/network.c
index 3761bf9..e4e4c8e 100644
--- a/src/network.c
+++ b/src/network.c
@@ -8,7 +8,9 @@
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
+#include "cost_layer.h"
#include "normalization_layer.h"
+#include "freeweight_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
@@ -28,14 +30,18 @@
}
#ifdef GPU
-void forward_network_gpu(network net, cl_mem input_cl, int train)
+void forward_network_gpu(network net, cl_mem input, cl_mem truth, 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_gpu(layer, input_cl);
- input_cl = layer.output_cl;
+ forward_convolutional_layer_gpu(layer, input);
+ input = layer.output_cl;
+ }
+ else if(net.types[i] == COST){
+ cost_layer layer = *(cost_layer *)net.layers[i];
+ forward_cost_layer_gpu(layer, input, truth);
}
/*
else if(net.types[i] == CONNECTED){
@@ -67,9 +73,75 @@
}
}
+void backward_network_gpu(network net, cl_mem input)
+{
+ int i;
+ cl_mem prev_input;
+ cl_mem prev_delta;
+ for(i = net.n-1; i >= 0; --i){
+ if(i == 0){
+ prev_input = input;
+ prev_delta = 0;
+ }else{
+ prev_input = get_network_output_cl_layer(net, i-1);
+ prev_delta = get_network_delta_cl_layer(net, i-1);
+ }
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ backward_convolutional_layer_gpu(layer, prev_delta);
+ }
+ else if(net.types[i] == COST){
+ cost_layer layer = *(cost_layer *)net.layers[i];
+ backward_cost_layer_gpu(layer, prev_input, prev_delta);
+ }
+ }
+}
+
+void update_network_gpu(network net)
+{
+ int i;
+ for(i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ update_convolutional_layer_gpu(layer);
+ }
+ else if(net.types[i] == MAXPOOL){
+ //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ }
+ else if(net.types[i] == SOFTMAX){
+ //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ }
+ else if(net.types[i] == NORMALIZATION){
+ //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ update_connected_layer(layer);
+ }
+ }
+}
+
+cl_mem get_network_output_cl_layer(network net, int i)
+{
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ return layer.output_cl;
+ }
+ return 0;
+}
+
+cl_mem get_network_delta_cl_layer(network net, int i)
+{
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ return layer.delta_cl;
+ }
+ return 0;
+}
+
#endif
-void forward_network(network net, float *input, int train)
+void forward_network(network net, float *input, float *truth, int train)
{
int i;
for(i = 0; i < net.n; ++i){
@@ -88,6 +160,10 @@
forward_crop_layer(layer, input);
input = layer.output;
}
+ else if(net.types[i] == COST){
+ cost_layer layer = *(cost_layer *)net.layers[i];
+ forward_cost_layer(layer, input, truth);
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
forward_softmax_layer(layer, input);
@@ -108,6 +184,11 @@
dropout_layer layer = *(dropout_layer *)net.layers[i];
forward_dropout_layer(layer, input);
}
+ else if(net.types[i] == FREEWEIGHT){
+ if(!train) continue;
+ freeweight_layer layer = *(freeweight_layer *)net.layers[i];
+ forward_freeweight_layer(layer, input);
+ }
}
}
@@ -148,6 +229,8 @@
return layer.output;
} else if(net.types[i] == DROPOUT){
return get_network_output_layer(net, i-1);
+ } else if(net.types[i] == FREEWEIGHT){
+ return get_network_output_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
@@ -159,7 +242,9 @@
}
float *get_network_output(network net)
{
- return get_network_output_layer(net, net.n-1);
+ int i;
+ for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
+ return get_network_output_layer(net, i);
}
float *get_network_delta_layer(network net, int i)
@@ -175,6 +260,8 @@
return layer.delta;
} else if(net.types[i] == DROPOUT){
return get_network_delta_layer(net, i-1);
+ } else if(net.types[i] == FREEWEIGHT){
+ return get_network_delta_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
@@ -182,6 +269,14 @@
return 0;
}
+float get_network_cost(network net)
+{
+ if(net.types[net.n-1] == COST){
+ return ((cost_layer *)net.layers[net.n-1])->output[0];
+ }
+ return 0;
+}
+
float *get_network_delta(network net)
{
return get_network_delta_layer(net, net.n-1);
@@ -212,9 +307,8 @@
return max_index(out, k);
}
-float backward_network(network net, float *input, float *truth)
+void backward_network(network net, float *input)
{
- float error = calculate_error_network(net, truth);
int i;
float *prev_input;
float *prev_delta;
@@ -246,15 +340,19 @@
connected_layer layer = *(connected_layer *)net.layers[i];
backward_connected_layer(layer, prev_input, prev_delta);
}
+ else if(net.types[i] == COST){
+ cost_layer layer = *(cost_layer *)net.layers[i];
+ backward_cost_layer(layer, prev_input, prev_delta);
+ }
}
- return error;
}
float train_network_datum(network net, float *x, float *y)
{
- forward_network(net, x, 1);
+ forward_network(net, x, y, 1);
//int class = get_predicted_class_network(net);
- float error = backward_network(net, x, y);
+ backward_network(net, x);
+ float error = get_network_cost(net);
update_network(net);
//return (y[class]?1:0);
return error;
@@ -287,8 +385,9 @@
int index = rand()%d.X.rows;
float *x = d.X.vals[index];
float *y = d.y.vals[index];
- forward_network(net, x, 1);
- sum += backward_network(net, x, y);
+ forward_network(net, x, y, 1);
+ backward_network(net, x);
+ sum += get_network_cost(net);
}
update_network(net);
}
@@ -329,6 +428,10 @@
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
+ else if(net.types[i] == FREEWEIGHT){
+ freeweight_layer layer = *(freeweight_layer *) net.layers[i];
+ return layer.inputs;
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
@@ -351,10 +454,15 @@
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.outputs;
- } else if(net.types[i] == DROPOUT){
+ }
+ else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
+ else if(net.types[i] == FREEWEIGHT){
+ freeweight_layer layer = *(freeweight_layer *) net.layers[i];
+ return layer.inputs;
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
@@ -396,7 +504,8 @@
int get_network_output_size(network net)
{
- int i = net.n-1;
+ int i;
+ for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
return get_network_output_size_layer(net, i);
}
@@ -457,7 +566,7 @@
float *network_predict(network net, float *input)
{
- forward_network(net, input, 0);
+ forward_network(net, input, 0, 0);
float *out = get_network_output(net);
return out;
}
--
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