extern "C" {
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#include <stdio.h>
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#include <time.h>
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#include "network.h"
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#include "image.h"
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#include "data.h"
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#include "utils.h"
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#include "params.h"
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#include "parser.h"
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#include "crop_layer.h"
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#include "connected_layer.h"
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#include "detection_layer.h"
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#include "convolutional_layer.h"
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#include "deconvolutional_layer.h"
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#include "maxpool_layer.h"
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#include "avgpool_layer.h"
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#include "normalization_layer.h"
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#include "cost_layer.h"
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#include "softmax_layer.h"
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#include "dropout_layer.h"
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#include "route_layer.h"
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}
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float * get_network_output_gpu_layer(network net, int i);
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float * get_network_delta_gpu_layer(network net, int i);
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float * get_network_output_gpu(network net);
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void forward_network_gpu(network net, network_state state)
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{
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int i;
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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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if(l.type == CONVOLUTIONAL){
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forward_convolutional_layer_gpu(l, state);
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} else if(l.type == DECONVOLUTIONAL){
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forward_deconvolutional_layer_gpu(l, state);
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} else if(l.type == DETECTION){
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forward_detection_layer_gpu(l, state);
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} else if(l.type == CONNECTED){
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forward_connected_layer_gpu(l, state);
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} else if(l.type == CROP){
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forward_crop_layer_gpu(l, state);
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} else if(l.type == COST){
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forward_cost_layer_gpu(l, state);
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} else if(l.type == SOFTMAX){
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forward_softmax_layer_gpu(l, state);
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} else if(l.type == NORMALIZATION){
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forward_normalization_layer_gpu(l, state);
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} else if(l.type == MAXPOOL){
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forward_maxpool_layer_gpu(l, state);
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} else if(l.type == AVGPOOL){
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forward_avgpool_layer_gpu(l, state);
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} else if(l.type == DROPOUT){
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forward_dropout_layer_gpu(l, state);
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} else if(l.type == ROUTE){
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forward_route_layer_gpu(l, net);
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}
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state.input = l.output_gpu;
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}
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}
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void backward_network_gpu(network net, network_state state)
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{
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int i;
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float * original_input = state.input;
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float * original_delta = state.delta;
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for(i = net.n-1; i >= 0; --i){
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layer l = net.layers[i];
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if(i == 0){
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state.input = original_input;
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state.delta = original_delta;
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}else{
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layer prev = net.layers[i-1];
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state.input = prev.output_gpu;
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state.delta = prev.delta_gpu;
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}
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if(l.type == CONVOLUTIONAL){
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backward_convolutional_layer_gpu(l, state);
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} else if(l.type == DECONVOLUTIONAL){
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backward_deconvolutional_layer_gpu(l, state);
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} else if(l.type == MAXPOOL){
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if(i != 0) backward_maxpool_layer_gpu(l, state);
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} else if(l.type == AVGPOOL){
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if(i != 0) backward_avgpool_layer_gpu(l, state);
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} else if(l.type == DROPOUT){
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backward_dropout_layer_gpu(l, state);
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} else if(l.type == DETECTION){
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backward_detection_layer_gpu(l, state);
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} else if(l.type == NORMALIZATION){
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backward_normalization_layer_gpu(l, state);
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} else if(l.type == SOFTMAX){
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if(i != 0) backward_softmax_layer_gpu(l, state);
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} else if(l.type == CONNECTED){
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backward_connected_layer_gpu(l, state);
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} else if(l.type == COST){
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backward_cost_layer_gpu(l, state);
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} else if(l.type == ROUTE){
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backward_route_layer_gpu(l, net);
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}
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}
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}
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void update_network_gpu(network net)
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{
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int i;
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int update_batch = net.batch*net.subdivisions;
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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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if(l.type == CONVOLUTIONAL){
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update_convolutional_layer_gpu(l, update_batch, net.learning_rate, net.momentum, net.decay);
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} else if(l.type == DECONVOLUTIONAL){
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update_deconvolutional_layer_gpu(l, net.learning_rate, net.momentum, net.decay);
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} else if(l.type == CONNECTED){
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update_connected_layer_gpu(l, update_batch, net.learning_rate, net.momentum, net.decay);
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}
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}
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}
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float train_network_datum_gpu(network net, float *x, float *y)
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{
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network_state state;
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int x_size = get_network_input_size(net)*net.batch;
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int y_size = get_network_output_size(net)*net.batch;
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if(!*net.input_gpu){
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*net.input_gpu = cuda_make_array(x, x_size);
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*net.truth_gpu = cuda_make_array(y, y_size);
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}else{
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cuda_push_array(*net.input_gpu, x, x_size);
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cuda_push_array(*net.truth_gpu, y, y_size);
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}
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state.input = *net.input_gpu;
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state.delta = 0;
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state.truth = *net.truth_gpu;
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state.train = 1;
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forward_network_gpu(net, state);
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backward_network_gpu(net, state);
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float error = get_network_cost(net);
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if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
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return error;
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}
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float *get_network_output_layer_gpu(network net, int i)
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{
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layer l = net.layers[i];
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cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
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return l.output;
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}
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float *get_network_output_gpu(network net)
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{
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int i;
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for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
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return get_network_output_layer_gpu(net, i);
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}
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float *network_predict_gpu(network net, float *input)
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{
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int size = get_network_input_size(net) * net.batch;
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network_state state;
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state.input = cuda_make_array(input, size);
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state.truth = 0;
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state.train = 0;
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state.delta = 0;
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forward_network_gpu(net, state);
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float *out = get_network_output_gpu(net);
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cuda_free(state.input);
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return out;
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}
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