Joseph Redmon
2015-08-25 9d42f49a240136a8cd643cdc1f98230d4f22b05e
src/network.c
@@ -4,12 +4,14 @@
#include "image.h"
#include "data.h"
#include "utils.h"
#include "blas.h"
#include "crop_layer.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "deconvolutional_layer.h"
#include "detection_layer.h"
#include "region_layer.h"
#include "normalization_layer.h"
#include "maxpool_layer.h"
#include "avgpool_layer.h"
@@ -35,6 +37,8 @@
            return "softmax";
        case DETECTION:
            return "detection";
        case REGION:
            return "region";
        case DROPOUT:
            return "dropout";
        case CROP:
@@ -79,6 +83,8 @@
            forward_normalization_layer(l, state);
        } else if(l.type == DETECTION){
            forward_detection_layer(l, state);
        } else if(l.type == REGION){
            forward_region_layer(l, state);
        } else if(l.type == CONNECTED){
            forward_connected_layer(l, state);
        } else if(l.type == CROP){
@@ -125,13 +131,24 @@
float get_network_cost(network net)
{
    if(net.layers[net.n-1].type == COST){
        return net.layers[net.n-1].output[0];
    int i;
    float sum = 0;
    int count = 0;
    for(i = 0; i < net.n; ++i){
        if(net.layers[i].type == COST){
            sum += net.layers[i].output[0];
            ++count;
        }
        if(net.layers[i].type == DETECTION){
            sum += net.layers[i].cost[0];
            ++count;
        }
        if(net.layers[i].type == REGION){
            sum += net.layers[i].cost[0];
            ++count;
        }
    }
    if(net.layers[net.n-1].type == DETECTION){
        return net.layers[net.n-1].cost[0];
    }
    return 0;
    return sum/count;
}
int get_predicted_class_network(network net)
@@ -170,6 +187,8 @@
            backward_dropout_layer(l, state);
        } else if(l.type == DETECTION){
            backward_detection_layer(l, state);
        } else if(l.type == REGION){
            backward_region_layer(l, state);
        } else if(l.type == SOFTMAX){
            if(i != 0) backward_softmax_layer(l, state);
        } else if(l.type == CONNECTED){
@@ -184,9 +203,9 @@
float train_network_datum(network net, float *x, float *y)
{
    #ifdef GPU
#ifdef GPU
    if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
    #endif
#endif
    network_state state;
    state.input = x;
    state.delta = 0;