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Convolutional working on GPU

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This commit is contained in:
Joseph Redmon
2014-10-13 00:29:01 -07:00
parent 76ee68f96d
commit 787d534560
21 changed files with 643 additions and 93 deletions
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127
src/network.c
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@ -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 @@ network make_network(int n, int batch)
}
#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 forward_network_gpu(network net, cl_mem input_cl, int train)
}
}
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 @@ void forward_network(network net, float *input, int train)
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 @@ void forward_network(network net, float *input, int train)
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);
}
}
}
@ -159,7 +240,9 @@ float *get_network_output_layer(network net, int i)
}
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)
@ -182,6 +265,14 @@ float *get_network_delta_layer(network net, int i)
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 +303,8 @@ int get_predicted_class_network(network net)
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 +336,19 @@ float backward_network(network net, float *input, float *truth)
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 +381,9 @@ float train_network_batch(network net, data d, int n)
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);
}
@ -351,7 +446,8 @@ int get_network_output_size_layer(network net, int i)
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;
}
@ -396,7 +492,8 @@ int resize_network(network net, int h, int w, int c)
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 +554,7 @@ void visualize_network(network net)
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;
}