#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 "blas.h"
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#include "crop_layer.h"
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#include "connected_layer.h"
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#include "convolutional_layer.h"
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#include "deconvolutional_layer.h"
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#include "detection_layer.h"
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#include "normalization_layer.h"
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#include "maxpool_layer.h"
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#include "avgpool_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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char *get_layer_string(LAYER_TYPE a)
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{
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switch(a){
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case CONVOLUTIONAL:
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return "convolutional";
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case DECONVOLUTIONAL:
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return "deconvolutional";
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case CONNECTED:
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return "connected";
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case MAXPOOL:
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return "maxpool";
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case AVGPOOL:
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return "avgpool";
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case SOFTMAX:
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return "softmax";
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case DETECTION:
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return "detection";
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case DROPOUT:
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return "dropout";
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case CROP:
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return "crop";
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case COST:
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return "cost";
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case ROUTE:
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return "route";
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case NORMALIZATION:
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return "normalization";
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default:
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break;
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}
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return "none";
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}
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network make_network(int n)
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{
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network net = {0};
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net.n = n;
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net.layers = calloc(net.n, sizeof(layer));
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#ifdef GPU
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net.input_gpu = calloc(1, sizeof(float *));
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net.truth_gpu = calloc(1, sizeof(float *));
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#endif
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return net;
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}
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void forward_network(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.delta){
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scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
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}
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if(l.type == CONVOLUTIONAL){
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forward_convolutional_layer(l, state);
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} else if(l.type == DECONVOLUTIONAL){
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forward_deconvolutional_layer(l, state);
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} else if(l.type == NORMALIZATION){
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forward_normalization_layer(l, state);
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} else if(l.type == DETECTION){
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forward_detection_layer(l, state);
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} else if(l.type == CONNECTED){
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forward_connected_layer(l, state);
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} else if(l.type == CROP){
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forward_crop_layer(l, state);
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} else if(l.type == COST){
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forward_cost_layer(l, state);
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} else if(l.type == SOFTMAX){
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forward_softmax_layer(l, state);
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} else if(l.type == MAXPOOL){
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forward_maxpool_layer(l, state);
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} else if(l.type == AVGPOOL){
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forward_avgpool_layer(l, state);
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} else if(l.type == DROPOUT){
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forward_dropout_layer(l, state);
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} else if(l.type == ROUTE){
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forward_route_layer(l, net);
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}
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state.input = l.output;
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}
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}
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void update_network(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(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(l, net.learning_rate, net.momentum, net.decay);
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} else if(l.type == CONNECTED){
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update_connected_layer(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 *get_network_output(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 net.layers[i].output;
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}
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float get_network_cost(network net)
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{
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int i;
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float sum = 0;
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int count = 0;
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for(i = 0; i < net.n; ++i){
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if(net.layers[net.n-1].type == COST){
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sum += net.layers[net.n-1].output[0];
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++count;
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}
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if(net.layers[net.n-1].type == DETECTION){
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sum += net.layers[net.n-1].cost[0];
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++count;
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}
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}
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return sum/count;
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}
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int get_predicted_class_network(network net)
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{
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float *out = get_network_output(net);
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int k = get_network_output_size(net);
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return max_index(out, k);
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}
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void backward_network(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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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;
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state.delta = prev.delta;
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}
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layer l = net.layers[i];
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if(l.type == CONVOLUTIONAL){
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backward_convolutional_layer(l, state);
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} else if(l.type == DECONVOLUTIONAL){
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backward_deconvolutional_layer(l, state);
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} else if(l.type == NORMALIZATION){
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backward_normalization_layer(l, state);
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} else if(l.type == MAXPOOL){
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if(i != 0) backward_maxpool_layer(l, state);
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} else if(l.type == AVGPOOL){
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backward_avgpool_layer(l, state);
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} else if(l.type == DROPOUT){
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backward_dropout_layer(l, state);
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} else if(l.type == DETECTION){
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backward_detection_layer(l, state);
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} else if(l.type == SOFTMAX){
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if(i != 0) backward_softmax_layer(l, state);
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} else if(l.type == CONNECTED){
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backward_connected_layer(l, state);
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} else if(l.type == COST){
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backward_cost_layer(l, state);
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} else if(l.type == ROUTE){
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backward_route_layer(l, net);
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}
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}
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}
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float train_network_datum(network net, float *x, float *y)
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{
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#ifdef GPU
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if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
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#endif
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network_state state;
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state.input = x;
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state.delta = 0;
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state.truth = y;
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state.train = 1;
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forward_network(net, state);
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backward_network(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(net);
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return error;
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}
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float train_network_sgd(network net, data d, int n)
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{
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int batch = net.batch;
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float *X = calloc(batch*d.X.cols, sizeof(float));
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float *y = calloc(batch*d.y.cols, sizeof(float));
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int i;
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float sum = 0;
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for(i = 0; i < n; ++i){
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net.seen += batch;
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get_random_batch(d, batch, X, y);
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float err = train_network_datum(net, X, y);
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sum += err;
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}
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free(X);
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free(y);
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return (float)sum/(n*batch);
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}
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float train_network(network net, data d)
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{
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int batch = net.batch;
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int n = d.X.rows / batch;
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float *X = calloc(batch*d.X.cols, sizeof(float));
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float *y = calloc(batch*d.y.cols, sizeof(float));
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int i;
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float sum = 0;
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for(i = 0; i < n; ++i){
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get_next_batch(d, batch, i*batch, X, y);
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net.seen += batch;
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float err = train_network_datum(net, X, y);
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sum += err;
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}
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free(X);
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free(y);
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return (float)sum/(n*batch);
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}
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float train_network_batch(network net, data d, int n)
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{
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int i,j;
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network_state state;
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state.train = 1;
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state.delta = 0;
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float sum = 0;
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int batch = 2;
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for(i = 0; i < n; ++i){
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for(j = 0; j < batch; ++j){
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int index = rand()%d.X.rows;
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state.input = d.X.vals[index];
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state.truth = d.y.vals[index];
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forward_network(net, state);
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backward_network(net, state);
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sum += get_network_cost(net);
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}
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update_network(net);
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}
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return (float)sum/(n*batch);
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}
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void set_batch_network(network *net, int b)
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{
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net->batch = b;
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int i;
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for(i = 0; i < net->n; ++i){
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net->layers[i].batch = b;
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}
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}
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int resize_network(network *net, int w, int h)
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{
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int i;
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//if(w == net->w && h == net->h) return 0;
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net->w = w;
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net->h = h;
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//fprintf(stderr, "Resizing to %d x %d...", w, h);
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//fflush(stderr);
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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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resize_convolutional_layer(&l, w, h);
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}else if(l.type == MAXPOOL){
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resize_maxpool_layer(&l, w, h);
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}else if(l.type == AVGPOOL){
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resize_avgpool_layer(&l, w, h);
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break;
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}else if(l.type == NORMALIZATION){
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resize_normalization_layer(&l, w, h);
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}else{
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error("Cannot resize this type of layer");
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}
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net->layers[i] = l;
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w = l.out_w;
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h = l.out_h;
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}
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//fprintf(stderr, " Done!\n");
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return 0;
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}
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int get_network_output_size(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 net.layers[i].outputs;
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}
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int get_network_input_size(network net)
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{
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return net.layers[0].inputs;
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}
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detection_layer get_network_detection_layer(network net)
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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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if(net.layers[i].type == DETECTION){
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return net.layers[i];
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}
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}
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fprintf(stderr, "Detection layer not found!!\n");
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detection_layer l = {0};
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return l;
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}
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image get_network_image_layer(network net, int i)
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{
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layer l = net.layers[i];
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if (l.out_w && l.out_h && l.out_c){
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return float_to_image(l.out_w, l.out_h, l.out_c, l.output);
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}
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image def = {0};
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return def;
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}
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image get_network_image(network net)
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{
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int i;
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for(i = net.n-1; i >= 0; --i){
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image m = get_network_image_layer(net, i);
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if(m.h != 0) return m;
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}
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image def = {0};
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return def;
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}
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void visualize_network(network net)
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{
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image *prev = 0;
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int i;
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char buff[256];
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for(i = 0; i < net.n; ++i){
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sprintf(buff, "Layer %d", i);
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layer l = net.layers[i];
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if(l.type == CONVOLUTIONAL){
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prev = visualize_convolutional_layer(l, buff, prev);
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}
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}
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}
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void top_predictions(network net, int k, int *index)
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{
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int size = get_network_output_size(net);
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float *out = get_network_output(net);
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top_k(out, size, k, index);
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}
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float *network_predict(network net, float *input)
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{
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#ifdef GPU
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if(gpu_index >= 0) return network_predict_gpu(net, input);
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#endif
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network_state state;
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state.input = input;
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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(net, state);
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float *out = get_network_output(net);
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return out;
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}
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matrix network_predict_data_multi(network net, data test, int n)
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{
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int i,j,b,m;
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int k = get_network_output_size(net);
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matrix pred = make_matrix(test.X.rows, k);
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float *X = calloc(net.batch*test.X.rows, sizeof(float));
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for(i = 0; i < test.X.rows; i += net.batch){
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for(b = 0; b < net.batch; ++b){
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if(i+b == test.X.rows) break;
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memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
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}
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for(m = 0; m < n; ++m){
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float *out = network_predict(net, X);
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for(b = 0; b < net.batch; ++b){
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if(i+b == test.X.rows) break;
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for(j = 0; j < k; ++j){
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pred.vals[i+b][j] += out[j+b*k]/n;
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}
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}
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}
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}
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free(X);
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return pred;
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}
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matrix network_predict_data(network net, data test)
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{
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int i,j,b;
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int k = get_network_output_size(net);
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matrix pred = make_matrix(test.X.rows, k);
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float *X = calloc(net.batch*test.X.cols, sizeof(float));
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for(i = 0; i < test.X.rows; i += net.batch){
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for(b = 0; b < net.batch; ++b){
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if(i+b == test.X.rows) break;
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memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
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}
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float *out = network_predict(net, X);
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for(b = 0; b < net.batch; ++b){
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if(i+b == test.X.rows) break;
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for(j = 0; j < k; ++j){
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pred.vals[i+b][j] = out[j+b*k];
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}
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}
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}
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free(X);
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return pred;
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}
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void print_network(network net)
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{
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int i,j;
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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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float *output = l.output;
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int n = l.outputs;
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float mean = mean_array(output, n);
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float vari = variance_array(output, n);
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fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
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if(n > 100) n = 100;
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for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
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if(n == 100)fprintf(stderr,".....\n");
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fprintf(stderr, "\n");
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}
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}
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void compare_networks(network n1, network n2, data test)
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{
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matrix g1 = network_predict_data(n1, test);
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matrix g2 = network_predict_data(n2, test);
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int i;
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int a,b,c,d;
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a = b = c = d = 0;
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for(i = 0; i < g1.rows; ++i){
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int truth = max_index(test.y.vals[i], test.y.cols);
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int p1 = max_index(g1.vals[i], g1.cols);
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int p2 = max_index(g2.vals[i], g2.cols);
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if(p1 == truth){
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if(p2 == truth) ++d;
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else ++c;
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}else{
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if(p2 == truth) ++b;
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else ++a;
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}
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}
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printf("%5d %5d\n%5d %5d\n", a, b, c, d);
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float num = pow((abs(b - c) - 1.), 2.);
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float den = b + c;
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printf("%f\n", num/den);
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}
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float network_accuracy(network net, data d)
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{
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matrix guess = network_predict_data(net, d);
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float acc = matrix_topk_accuracy(d.y, guess,1);
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free_matrix(guess);
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return acc;
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}
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float *network_accuracies(network net, data d)
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{
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static float acc[2];
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matrix guess = network_predict_data(net, d);
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acc[0] = matrix_topk_accuracy(d.y, guess,1);
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acc[1] = matrix_topk_accuracy(d.y, guess,5);
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free_matrix(guess);
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return acc;
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}
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float network_accuracy_multi(network net, data d, int n)
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{
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matrix guess = network_predict_data_multi(net, d, n);
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float acc = matrix_topk_accuracy(d.y, guess,1);
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free_matrix(guess);
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return acc;
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}
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