From 787d5345609459f21fd65d2d8b4fcd55201e21a1 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 13 Oct 2014 07:31:10 +0000
Subject: [PATCH] Convolutional working on GPU
---
src/network.c | 178 +++++++++++++++++++++++++++++++++++++++--------------------
1 files changed, 117 insertions(+), 61 deletions(-)
diff --git a/src/network.c b/src/network.c
index 292bba0..5833166 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"
@@ -28,25 +30,20 @@
}
#ifdef GPU
-void forward_network(network net, float *input, int train)
+void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
{
- 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;
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;
- input = layer.output;
+ 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){
connected_layer layer = *(connected_layer *)net.layers[i];
forward_connected_layer(layer, input, train);
@@ -72,12 +69,79 @@
forward_normalization_layer(layer, input);
input = layer.output;
}
+ */
}
}
-#else
+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 forward_network(network net, float *input, int train)
+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, float *truth, int train)
{
int i;
for(i = 0; i < net.n; ++i){
@@ -96,6 +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);
@@ -116,9 +184,13 @@
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);
+ }
}
}
-#endif
void update_network(network net)
{
@@ -168,7 +240,9 @@
}
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)
@@ -191,6 +265,14 @@
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);
@@ -221,9 +303,8 @@
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;
@@ -255,15 +336,19 @@
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;
@@ -275,45 +360,13 @@
float *X = calloc(batch*d.X.cols, sizeof(float));
float *y = calloc(batch*d.y.cols, sizeof(float));
- int i,j;
+ int i;
float sum = 0;
- int index = 0;
for(i = 0; i < n; ++i){
- for(j = 0; j < batch; ++j){
- 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));
- }
-
+ get_batch(d, batch, X, y);
float err = train_network_datum(net, X, y);
sum += err;
- //train_network_datum(net, X, y);
- /*
- float *y = d.y.vals[index];
- int class = get_predicted_class_network(net);
- correct += (y[class]?1:0);
- */
-
-/*
- 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]);
- //if((i+1)%10 == 0){
- // printf("%d: %f\n", (i+1), (float)correct/(i+1));
- //}
}
- //printf("Accuracy: %f\n",(float) correct/n);
- //show_image(float_to_image(32,32,3,X), "Orig");
free(X);
free(y);
return (float)sum/(n*batch);
@@ -328,8 +381,9 @@
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);
}
@@ -392,7 +446,8 @@
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;
}
@@ -437,7 +492,8 @@
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);
}
@@ -498,7 +554,7 @@
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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