mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
701 lines
22 KiB
C
701 lines
22 KiB
C
#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 "crop_layer.h"
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#include "connected_layer.h"
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#include "convolutional_layer.h"
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#include "maxpool_layer.h"
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#include "cost_layer.h"
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#include "normalization_layer.h"
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#include "freeweight_layer.h"
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#include "softmax_layer.h"
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#include "dropout_layer.h"
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network make_network(int n, int batch)
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{
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network net;
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net.n = n;
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net.batch = batch;
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net.layers = calloc(net.n, sizeof(void *));
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net.types = calloc(net.n, sizeof(LAYER_TYPE));
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net.outputs = 0;
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net.output = 0;
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#ifdef GPU
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net.input_cl = calloc(1, sizeof(cl_mem));
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net.truth_cl = calloc(1, sizeof(cl_mem));
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#endif
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return net;
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}
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void forward_network(network net, float *input, float *truth, int train)
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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.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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forward_convolutional_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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forward_connected_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == CROP){
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crop_layer layer = *(crop_layer *)net.layers[i];
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forward_crop_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == COST){
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cost_layer layer = *(cost_layer *)net.layers[i];
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forward_cost_layer(layer, input, truth);
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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forward_softmax_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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forward_maxpool_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == NORMALIZATION){
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normalization_layer layer = *(normalization_layer *)net.layers[i];
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forward_normalization_layer(layer, input);
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input = layer.output;
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}
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else if(net.types[i] == DROPOUT){
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if(!train) continue;
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dropout_layer layer = *(dropout_layer *)net.layers[i];
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forward_dropout_layer(layer, input);
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}
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else if(net.types[i] == FREEWEIGHT){
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if(!train) continue;
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freeweight_layer layer = *(freeweight_layer *)net.layers[i];
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forward_freeweight_layer(layer, input);
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}
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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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for(i = 0; i < net.n; ++i){
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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update_convolutional_layer(layer);
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}
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else if(net.types[i] == MAXPOOL){
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//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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}
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else if(net.types[i] == SOFTMAX){
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//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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}
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else if(net.types[i] == NORMALIZATION){
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//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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update_connected_layer(layer);
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}
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}
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}
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float *get_network_output_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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return layer.output;
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} else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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return layer.output;
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} else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.output;
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} else if(net.types[i] == DROPOUT){
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return get_network_output_layer(net, i-1);
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} else if(net.types[i] == FREEWEIGHT){
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return get_network_output_layer(net, i-1);
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} else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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return layer.output;
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} else if(net.types[i] == CROP){
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crop_layer layer = *(crop_layer *)net.layers[i];
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return layer.output;
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} else if(net.types[i] == NORMALIZATION){
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normalization_layer layer = *(normalization_layer *)net.layers[i];
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return layer.output;
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}
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return 0;
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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.types[i] != COST) break;
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return get_network_output_layer(net, i);
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}
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float *get_network_delta_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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return layer.delta;
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} else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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return layer.delta;
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} else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.delta;
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} else if(net.types[i] == DROPOUT){
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return get_network_delta_layer(net, i-1);
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} else if(net.types[i] == FREEWEIGHT){
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return get_network_delta_layer(net, i-1);
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} else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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return layer.delta;
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}
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return 0;
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}
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float get_network_cost(network net)
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{
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if(net.types[net.n-1] == COST){
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return ((cost_layer *)net.layers[net.n-1])->output[0];
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}
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return 0;
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}
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float *get_network_delta(network net)
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{
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return get_network_delta_layer(net, net.n-1);
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}
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float calculate_error_network(network net, float *truth)
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{
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float sum = 0;
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float *delta = get_network_delta(net);
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float *out = get_network_output(net);
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int i;
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for(i = 0; i < get_network_output_size(net)*net.batch; ++i){
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//if(i %get_network_output_size(net) == 0) printf("\n");
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//printf("%5.2f %5.2f, ", out[i], truth[i]);
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//if(i == get_network_output_size(net)) printf("\n");
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delta[i] = truth[i] - out[i];
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//printf("%.10f, ", out[i]);
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sum += delta[i]*delta[i];
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}
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//printf("\n");
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return sum;
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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, float *input)
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{
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int i;
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float *prev_input;
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float *prev_delta;
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for(i = net.n-1; i >= 0; --i){
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if(i == 0){
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prev_input = input;
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prev_delta = 0;
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}else{
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prev_input = get_network_output_layer(net, i-1);
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prev_delta = get_network_delta_layer(net, i-1);
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}
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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backward_convolutional_layer(layer, prev_input, prev_delta);
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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if(i != 0) backward_maxpool_layer(layer, prev_delta);
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}
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else if(net.types[i] == DROPOUT){
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dropout_layer layer = *(dropout_layer *)net.layers[i];
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backward_dropout_layer(layer, prev_delta);
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}
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else if(net.types[i] == NORMALIZATION){
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normalization_layer layer = *(normalization_layer *)net.layers[i];
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if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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if(i != 0) backward_softmax_layer(layer, prev_delta);
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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backward_connected_layer(layer, prev_input, prev_delta);
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}
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else if(net.types[i] == COST){
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cost_layer layer = *(cost_layer *)net.layers[i];
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backward_cost_layer(layer, prev_input, prev_delta);
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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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forward_network(net, x, y, 1);
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backward_network(net, x);
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float error = get_network_cost(net);
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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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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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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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float *x = d.X.vals[index];
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float *y = d.y.vals[index];
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forward_network(net, x, y, 1);
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backward_network(net, x);
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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_learning_network(network *net, float rate, float momentum, float decay)
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{
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int i;
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net->learning_rate=rate;
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net->momentum = momentum;
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net->decay = decay;
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for(i = 0; i < net->n; ++i){
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if(net->types[i] == CONVOLUTIONAL){
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convolutional_layer *layer = (convolutional_layer *)net->layers[i];
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layer->learning_rate=rate;
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layer->momentum = momentum;
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layer->decay = decay;
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}
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else if(net->types[i] == CONNECTED){
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connected_layer *layer = (connected_layer *)net->layers[i];
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layer->learning_rate=rate;
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layer->momentum = momentum;
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layer->decay = decay;
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}
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}
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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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if(net->types[i] == CONVOLUTIONAL){
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convolutional_layer *layer = (convolutional_layer *)net->layers[i];
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layer->batch = b;
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}
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else if(net->types[i] == MAXPOOL){
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maxpool_layer *layer = (maxpool_layer *)net->layers[i];
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layer->batch = b;
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}
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else if(net->types[i] == CONNECTED){
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connected_layer *layer = (connected_layer *)net->layers[i];
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layer->batch = b;
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} else if(net->types[i] == DROPOUT){
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dropout_layer *layer = (dropout_layer *) net->layers[i];
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layer->batch = b;
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}
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else if(net->types[i] == FREEWEIGHT){
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freeweight_layer *layer = (freeweight_layer *) net->layers[i];
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layer->batch = b;
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}
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else if(net->types[i] == SOFTMAX){
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softmax_layer *layer = (softmax_layer *)net->layers[i];
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layer->batch = b;
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}
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else if(net->types[i] == COST){
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cost_layer *layer = (cost_layer *)net->layers[i];
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layer->batch = b;
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}
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}
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}
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int get_network_input_size_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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return layer.h*layer.w*layer.c;
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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return layer.h*layer.w*layer.c;
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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return layer.inputs;
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} else if(net.types[i] == DROPOUT){
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dropout_layer layer = *(dropout_layer *) net.layers[i];
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return layer.inputs;
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} else if(net.types[i] == CROP){
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crop_layer layer = *(crop_layer *) net.layers[i];
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return layer.c*layer.h*layer.w;
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}
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else if(net.types[i] == FREEWEIGHT){
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freeweight_layer layer = *(freeweight_layer *) net.layers[i];
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return layer.inputs;
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.inputs;
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}
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printf("Can't find input size\n");
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return 0;
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}
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int get_network_output_size_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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image output = get_convolutional_image(layer);
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return output.h*output.w*output.c;
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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image output = get_maxpool_image(layer);
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return output.h*output.w*output.c;
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}
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else if(net.types[i] == CROP){
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crop_layer layer = *(crop_layer *) net.layers[i];
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return layer.c*layer.crop_height*layer.crop_width;
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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return layer.outputs;
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}
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else if(net.types[i] == DROPOUT){
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dropout_layer layer = *(dropout_layer *) net.layers[i];
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return layer.inputs;
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}
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else if(net.types[i] == FREEWEIGHT){
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freeweight_layer layer = *(freeweight_layer *) net.layers[i];
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return layer.inputs;
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.inputs;
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}
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printf("Can't find output size\n");
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return 0;
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}
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int resize_network(network net, int h, int w, int c)
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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.types[i] == CONVOLUTIONAL){
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convolutional_layer *layer = (convolutional_layer *)net.layers[i];
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resize_convolutional_layer(layer, h, w, c);
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image output = get_convolutional_image(*layer);
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h = output.h;
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w = output.w;
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c = output.c;
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}else if(net.types[i] == MAXPOOL){
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maxpool_layer *layer = (maxpool_layer *)net.layers[i];
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resize_maxpool_layer(layer, h, w, c);
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image output = get_maxpool_image(*layer);
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h = output.h;
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w = output.w;
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c = output.c;
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}else if(net.types[i] == NORMALIZATION){
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normalization_layer *layer = (normalization_layer *)net.layers[i];
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resize_normalization_layer(layer, h, w, c);
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image output = get_normalization_image(*layer);
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h = output.h;
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w = output.w;
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c = output.c;
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}else{
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error("Cannot resize this type of layer");
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}
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}
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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.types[i] != COST) break;
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return get_network_output_size_layer(net, i);
|
|
}
|
|
|
|
int get_network_input_size(network net)
|
|
{
|
|
return get_network_input_size_layer(net, 0);
|
|
}
|
|
|
|
image get_network_image_layer(network net, int i)
|
|
{
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
|
return get_convolutional_image(layer);
|
|
}
|
|
else if(net.types[i] == MAXPOOL){
|
|
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
|
|
return get_maxpool_image(layer);
|
|
}
|
|
else if(net.types[i] == NORMALIZATION){
|
|
normalization_layer layer = *(normalization_layer *)net.layers[i];
|
|
return get_normalization_image(layer);
|
|
}
|
|
else if(net.types[i] == CROP){
|
|
crop_layer layer = *(crop_layer *)net.layers[i];
|
|
return get_crop_image(layer);
|
|
}
|
|
return make_empty_image(0,0,0);
|
|
}
|
|
|
|
image get_network_image(network net)
|
|
{
|
|
int i;
|
|
for(i = net.n-1; i >= 0; --i){
|
|
image m = get_network_image_layer(net, i);
|
|
if(m.h != 0) return m;
|
|
}
|
|
return make_empty_image(0,0,0);
|
|
}
|
|
|
|
void visualize_network(network net)
|
|
{
|
|
image *prev = 0;
|
|
int i;
|
|
char buff[256];
|
|
//show_image(get_network_image_layer(net, 0), "Crop");
|
|
for(i = 0; i < net.n; ++i){
|
|
sprintf(buff, "Layer %d", i);
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
|
prev = visualize_convolutional_layer(layer, buff, prev);
|
|
}
|
|
if(net.types[i] == NORMALIZATION){
|
|
normalization_layer layer = *(normalization_layer *)net.layers[i];
|
|
visualize_normalization_layer(layer, buff);
|
|
}
|
|
}
|
|
}
|
|
|
|
void top_predictions(network net, int k, int *index)
|
|
{
|
|
int size = get_network_output_size(net);
|
|
float *out = get_network_output(net);
|
|
top_k(out, size, k, index);
|
|
}
|
|
|
|
|
|
float *network_predict(network net, float *input)
|
|
{
|
|
#ifdef GPU
|
|
if(gpu_index >= 0) return network_predict_gpu(net, input);
|
|
#endif
|
|
|
|
forward_network(net, input, 0, 0);
|
|
float *out = get_network_output(net);
|
|
return out;
|
|
}
|
|
|
|
matrix network_predict_data_multi(network net, data test, int n)
|
|
{
|
|
int i,j,b,m;
|
|
int k = get_network_output_size(net);
|
|
matrix pred = make_matrix(test.X.rows, k);
|
|
float *X = calloc(net.batch*test.X.rows, sizeof(float));
|
|
for(i = 0; i < test.X.rows; i += net.batch){
|
|
for(b = 0; b < net.batch; ++b){
|
|
if(i+b == test.X.rows) break;
|
|
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
|
|
}
|
|
for(m = 0; m < n; ++m){
|
|
float *out = network_predict(net, X);
|
|
for(b = 0; b < net.batch; ++b){
|
|
if(i+b == test.X.rows) break;
|
|
for(j = 0; j < k; ++j){
|
|
pred.vals[i+b][j] += out[j+b*k]/n;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
free(X);
|
|
return pred;
|
|
}
|
|
|
|
matrix network_predict_data(network net, data test)
|
|
{
|
|
int i,j,b;
|
|
int k = get_network_output_size(net);
|
|
matrix pred = make_matrix(test.X.rows, k);
|
|
float *X = calloc(net.batch*test.X.cols, sizeof(float));
|
|
for(i = 0; i < test.X.rows; i += net.batch){
|
|
for(b = 0; b < net.batch; ++b){
|
|
if(i+b == test.X.rows) break;
|
|
memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
|
|
}
|
|
float *out = network_predict(net, X);
|
|
for(b = 0; b < net.batch; ++b){
|
|
if(i+b == test.X.rows) break;
|
|
for(j = 0; j < k; ++j){
|
|
pred.vals[i+b][j] = out[j+b*k];
|
|
}
|
|
}
|
|
}
|
|
free(X);
|
|
return pred;
|
|
}
|
|
|
|
void print_network(network net)
|
|
{
|
|
int i,j;
|
|
for(i = 0; i < net.n; ++i){
|
|
float *output = 0;
|
|
int n = 0;
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
|
|
output = layer.output;
|
|
image m = get_convolutional_image(layer);
|
|
n = m.h*m.w*m.c;
|
|
}
|
|
else if(net.types[i] == MAXPOOL){
|
|
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
|
|
output = layer.output;
|
|
image m = get_maxpool_image(layer);
|
|
n = m.h*m.w*m.c;
|
|
}
|
|
else if(net.types[i] == CROP){
|
|
crop_layer layer = *(crop_layer *)net.layers[i];
|
|
output = layer.output;
|
|
image m = get_crop_image(layer);
|
|
n = m.h*m.w*m.c;
|
|
}
|
|
else if(net.types[i] == CONNECTED){
|
|
connected_layer layer = *(connected_layer *)net.layers[i];
|
|
output = layer.output;
|
|
n = layer.outputs;
|
|
}
|
|
else if(net.types[i] == SOFTMAX){
|
|
softmax_layer layer = *(softmax_layer *)net.layers[i];
|
|
output = layer.output;
|
|
n = layer.inputs;
|
|
}
|
|
float mean = mean_array(output, n);
|
|
float vari = variance_array(output, n);
|
|
fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
|
|
if(n > 100) n = 100;
|
|
for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
|
|
if(n == 100)fprintf(stderr,".....\n");
|
|
fprintf(stderr, "\n");
|
|
}
|
|
}
|
|
|
|
void compare_networks(network n1, network n2, data test)
|
|
{
|
|
matrix g1 = network_predict_data(n1, test);
|
|
matrix g2 = network_predict_data(n2, test);
|
|
int i;
|
|
int a,b,c,d;
|
|
a = b = c = d = 0;
|
|
for(i = 0; i < g1.rows; ++i){
|
|
int truth = max_index(test.y.vals[i], test.y.cols);
|
|
int p1 = max_index(g1.vals[i], g1.cols);
|
|
int p2 = max_index(g2.vals[i], g2.cols);
|
|
if(p1 == truth){
|
|
if(p2 == truth) ++d;
|
|
else ++c;
|
|
}else{
|
|
if(p2 == truth) ++b;
|
|
else ++a;
|
|
}
|
|
}
|
|
printf("%5d %5d\n%5d %5d\n", a, b, c, d);
|
|
float num = pow((abs(b - c) - 1.), 2.);
|
|
float den = b + c;
|
|
printf("%f\n", num/den);
|
|
}
|
|
|
|
float network_accuracy(network net, data d)
|
|
{
|
|
matrix guess = network_predict_data(net, d);
|
|
float acc = matrix_topk_accuracy(d.y, guess,1);
|
|
free_matrix(guess);
|
|
return acc;
|
|
}
|
|
|
|
float *network_accuracies(network net, data d)
|
|
{
|
|
static float acc[2];
|
|
matrix guess = network_predict_data(net, d);
|
|
acc[0] = matrix_topk_accuracy(d.y, guess,1);
|
|
acc[1] = matrix_topk_accuracy(d.y, guess,5);
|
|
free_matrix(guess);
|
|
return acc;
|
|
}
|
|
|
|
|
|
float network_accuracy_multi(network net, data d, int n)
|
|
{
|
|
matrix guess = network_predict_data_multi(net, d, n);
|
|
float acc = matrix_topk_accuracy(d.y, guess,1);
|
|
free_matrix(guess);
|
|
return acc;
|
|
}
|
|
|
|
|