#include <stdio.h>
|
#include <time.h>
|
|
#include "network.h"
|
#include "image.h"
|
#include "data.h"
|
#include "utils.h"
|
|
#include "crop_layer.h"
|
#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"
|
|
#ifdef GPU
|
cl_mem get_network_output_cl_layer(network net, int i);
|
cl_mem get_network_delta_cl_layer(network net, int i);
|
|
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);
|
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_gpu(layer, input);
|
input = layer.output_cl;
|
}
|
else if(net.types[i] == MAXPOOL){
|
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
|
forward_maxpool_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_gpu(layer, input);
|
input = layer.output_cl;
|
}
|
else if(net.types[i] == DROPOUT){
|
if(!train) continue;
|
dropout_layer layer = *(dropout_layer *)net.layers[i];
|
forward_dropout_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_gpu(layer, input);
|
input = layer.output_cl;
|
}
|
}
|
}
|
|
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_input, 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);
|
}
|
else if(net.types[i] == CONNECTED){
|
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] == DROPOUT){
|
dropout_layer layer = *(dropout_layer *)net.layers[i];
|
backward_dropout_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);
|
}
|
}
|
}
|
|
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] == CONNECTED){
|
connected_layer layer = *(connected_layer *)net.layers[i];
|
update_connected_layer_gpu(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;
|
}
|
else if(net.types[i] == CONNECTED){
|
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] == CROP){
|
crop_layer layer = *(crop_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;
|
} else if(net.types[i] == DROPOUT){
|
dropout_layer layer = *(dropout_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;
|
}
|
else if(net.types[i] == CONNECTED){
|
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;
|
} else if(net.types[i] == DROPOUT){
|
if(i == 0) return 0;
|
return get_network_delta_cl_layer(net, i-1);
|
}
|
return 0;
|
}
|
|
float train_network_datum_gpu(network net, float *x, float *y)
|
{
|
int x_size = get_network_input_size(net)*net.batch;
|
int y_size = get_network_output_size(net)*net.batch;
|
if(!*net.input_cl){
|
*net.input_cl = cl_make_array(x, x_size);
|
*net.truth_cl = cl_make_array(y, y_size);
|
}else{
|
cl_write_array(*net.input_cl, x, x_size);
|
cl_write_array(*net.truth_cl, y, y_size);
|
}
|
forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
|
backward_network_gpu(net, *net.input_cl);
|
update_network_gpu(net);
|
float error = get_network_cost(net);
|
return error;
|
}
|
|
float *get_network_output_layer_gpu(network net, int i)
|
{
|
if(net.types[i] == CONVOLUTIONAL){
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
return layer.output;
|
}
|
else if(net.types[i] == CONNECTED){
|
connected_layer layer = *(connected_layer *)net.layers[i];
|
cl_read_array(layer.output_cl, layer.output, layer.outputs*layer.batch);
|
return layer.output;
|
}
|
else if(net.types[i] == MAXPOOL){
|
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
|
return layer.output;
|
}
|
else if(net.types[i] == SOFTMAX){
|
softmax_layer layer = *(softmax_layer *)net.layers[i];
|
pull_softmax_layer_output(layer);
|
return layer.output;
|
}
|
return 0;
|
}
|
|
float *get_network_output_gpu(network net)
|
{
|
int i;
|
for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
|
return get_network_output_layer_gpu(net, i);
|
}
|
|
float *network_predict_gpu(network net, float *input)
|
{
|
|
int size = get_network_input_size(net) * net.batch;
|
cl_mem input_cl = cl_make_array(input, size);
|
forward_network_gpu(net, input_cl, 0, 0);
|
float *out = get_network_output_gpu(net);
|
clReleaseMemObject(input_cl);
|
return out;
|
}
|
|
#endif
|
