From 158bb1bee9951875dbe3474d84c6663431e18301 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 21 Oct 2014 21:49:18 +0000
Subject: [PATCH] softmax on gpu
---
src/network.c | 71 ++++++++++++++++++++++++-----------
1 files changed, 49 insertions(+), 22 deletions(-)
diff --git a/src/network.c b/src/network.c
index f9b4667..6696769 100644
--- a/src/network.c
+++ b/src/network.c
@@ -1,4 +1,5 @@
#include <stdio.h>
+#include <time.h>
#include "network.h"
#include "image.h"
#include "data.h"
@@ -31,8 +32,10 @@
}
#ifdef GPU
+
void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
{
+ //printf("start\n");
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
@@ -49,28 +52,28 @@
forward_connected_layer_gpu(layer, input);
input = layer.output_cl;
}
- /*
- 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] == CROP){
- crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_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;
+ forward_maxpool_layer_gpu(layer, input);
+ input = layer.output_cl;
}
- else if(net.types[i] == NORMALIZATION){
- normalization_layer layer = *(normalization_layer *)net.layers[i];
- forward_normalization_layer(layer, input);
- input = layer.output;
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ forward_softmax_layer_gpu(layer, input);
+ input = layer.output_cl;
}
- */
+ /*
+ else if(net.types[i] == CROP){
+ crop_layer layer = *(crop_layer *)net.layers[i];
+ forward_crop_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;
+ }
+ */
}
}
@@ -99,6 +102,14 @@
connected_layer layer = *(connected_layer *)net.layers[i];
backward_connected_layer_gpu(layer, prev_input, prev_delta);
}
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ backward_maxpool_layer_gpu(layer, prev_delta);
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ backward_softmax_layer_gpu(layer, prev_delta);
+ }
}
}
@@ -127,6 +138,14 @@
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output_cl;
}
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ return layer.output_cl;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.output_cl;
+ }
return 0;
}
@@ -140,6 +159,14 @@
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta_cl;
}
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ return layer.delta_cl;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.delta_cl;
+ }
return 0;
}
@@ -330,7 +357,7 @@
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- if(i != 0) backward_maxpool_layer(layer, prev_input, prev_delta);
+ if(i != 0) backward_maxpool_layer(layer, prev_delta);
}
else if(net.types[i] == NORMALIZATION){
normalization_layer layer = *(normalization_layer *)net.layers[i];
@@ -338,7 +365,7 @@
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
- if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
+ if(i != 0) backward_softmax_layer(layer, prev_delta);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
@@ -351,6 +378,7 @@
}
}
+
#ifdef GPU
float train_network_datum_gpu(network net, float *x, float *y)
{
@@ -364,13 +392,12 @@
cl_write_array(*net.truth_cl, y, y_size);
}
forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
- //int class = get_predicted_class_network(net);
backward_network_gpu(net, *net.input_cl);
float error = get_network_cost(net);
update_network_gpu(net);
- //return (y[class]?1:0);
return error;
}
+
float train_network_sgd_gpu(network net, data d, int n)
{
int batch = net.batch;
--
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