#include <stdio.h>
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#include <time.h>
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#include <assert.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 "gru_layer.h"
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#include "rnn_layer.h"
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#include "crnn_layer.h"
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#include "local_layer.h"
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#include "convolutional_layer.h"
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#include "activation_layer.h"
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#include "detection_layer.h"
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#include "region_layer.h"
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#include "normalization_layer.h"
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#include "batchnorm_layer.h"
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#include "maxpool_layer.h"
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#include "reorg_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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#include "shortcut_layer.h"
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int get_current_batch(network net)
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{
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int batch_num = (*net.seen)/(net.batch*net.subdivisions);
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return batch_num;
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}
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void reset_momentum(network net)
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{
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if (net.momentum == 0) return;
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net.learning_rate = 0;
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net.momentum = 0;
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net.decay = 0;
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#ifdef GPU
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//if(net.gpu_index >= 0) update_network_gpu(net);
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#endif
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}
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float get_current_rate(network net)
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{
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int batch_num = get_current_batch(net);
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int i;
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float rate;
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if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
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switch (net.policy) {
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case CONSTANT:
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return net.learning_rate;
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case STEP:
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return net.learning_rate * pow(net.scale, batch_num/net.step);
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case STEPS:
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rate = net.learning_rate;
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for(i = 0; i < net.num_steps; ++i){
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if(net.steps[i] > batch_num) return rate;
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rate *= net.scales[i];
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//if(net.steps[i] > batch_num - 1 && net.scales[i] > 1) reset_momentum(net);
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}
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return rate;
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case EXP:
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return net.learning_rate * pow(net.gamma, batch_num);
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case POLY:
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return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
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//if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
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//return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
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case RANDOM:
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return net.learning_rate * pow(rand_uniform(0,1), net.power);
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case SIG:
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return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
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default:
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fprintf(stderr, "Policy is weird!\n");
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return net.learning_rate;
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}
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}
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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 ACTIVE:
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return "activation";
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case LOCAL:
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return "local";
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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 RNN:
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return "rnn";
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case GRU:
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return "gru";
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case CRNN:
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return "crnn";
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case MAXPOOL:
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return "maxpool";
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case REORG:
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return "reorg";
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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 REGION:
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return "region";
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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 SHORTCUT:
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return "shortcut";
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case NORMALIZATION:
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return "normalization";
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case BATCHNORM:
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return "batchnorm";
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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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net.seen = calloc(1, sizeof(int));
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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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state.workspace = net.workspace;
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int i;
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for(i = 0; i < net.n; ++i){
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state.index = 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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l.forward(l, state);
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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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float rate = get_current_rate(net);
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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.update){
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l.update(l, update_batch, 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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#ifdef GPU
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if (gpu_index >= 0) return get_network_output_gpu(net);
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#endif
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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[i].cost){
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sum += net.layers[i].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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state.workspace = net.workspace;
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for(i = net.n-1; i >= 0; --i){
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state.index = 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.stopbackward) break;
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l.backward(l, state);
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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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*net.seen += net.batch;
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state.index = 0;
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state.net = net;
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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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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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assert(d.X.rows % net.batch == 0);
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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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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.index = 0;
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state.net = net;
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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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#ifdef CUDNN
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if(net->layers[i].type == CONVOLUTIONAL){
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cudnn_convolutional_setup(net->layers + i);
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}
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#endif
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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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#ifdef GPU
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cuda_set_device(net->gpu_index);
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if(gpu_index >= 0){
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cuda_free(net->workspace);
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}
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#endif
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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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int inputs = 0;
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size_t workspace_size = 0;
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//fprintf(stderr, "Resizing to %d x %d...\n", 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 == CROP){
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resize_crop_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 == REGION){
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resize_region_layer(&l, w, h);
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}else if(l.type == ROUTE){
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resize_route_layer(&l, net);
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}else if(l.type == REORG){
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resize_reorg_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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}else if(l.type == NORMALIZATION){
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resize_normalization_layer(&l, w, h);
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}else if(l.type == COST){
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resize_cost_layer(&l, inputs);
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}else{
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fprintf(stderr, "Resizing type %d \n", (int)l.type);
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error("Cannot resize this type of layer");
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}
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if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
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inputs = l.outputs;
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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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if(l.type == AVGPOOL) break;
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}
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#ifdef GPU
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if(gpu_index >= 0){
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if(net->input_gpu) {
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cuda_free(*net->input_gpu);
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*net->input_gpu = 0;
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cuda_free(*net->truth_gpu);
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*net->truth_gpu = 0;
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}
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net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
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}else {
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free(net->workspace);
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net->workspace = calloc(1, workspace_size);
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}
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#else
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free(net->workspace);
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net->workspace = calloc(1, workspace_size);
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#endif
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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.net = net;
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state.index = 0;
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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, int n)
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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, n);
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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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void free_network(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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free_layer(net.layers[i]);
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}
|
free(net.layers);
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#ifdef GPU
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if (gpu_index >= 0) cuda_free(net.workspace);
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else free(net.workspace);
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if (*net.input_gpu) cuda_free(*net.input_gpu);
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if (*net.truth_gpu) cuda_free(*net.truth_gpu);
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if (net.input_gpu) free(net.input_gpu);
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if (net.truth_gpu) free(net.truth_gpu);
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#else
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free(net.workspace);
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#endif
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}
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