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This commit is contained in:
Joseph Redmon
2014-04-16 17:05:29 -07:00
parent cc06817efa
commit 738cd4c2d7
10 changed files with 438 additions and 55 deletions
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136
src/network.c
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@ -8,6 +8,7 @@
#include "convolutional_layer.h"
//#include "old_conv.h"
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "softmax_layer.h"
network make_network(int n, int batch)
@ -40,6 +41,17 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
fprintf(fp, "data=");
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
/*
int j,k;
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->n; ++i){
for(j = l->c-1; j >= 0; --j){
for(k = 0; k < l->size*l->size; ++k){
fprintf(fp, "%g,", l->filters[i*(l->c*l->size*l->size)+j*l->size*l->size+k]);
}
}
}
*/
fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l, int first)
@ -48,9 +60,9 @@ void print_connected_cfg(FILE *fp, connected_layer *l, int first)
fprintf(fp, "[connected]\n");
if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "output=%d\n"
"activation=%s\n",
l->outputs,
get_activation_string(l->activation));
"activation=%s\n",
l->outputs,
get_activation_string(l->activation));
fprintf(fp, "data=");
for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
@ -61,13 +73,27 @@ void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
{
fprintf(fp, "[maxpool]\n");
if(first) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->batch,l->h, l->w, l->c);
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->batch,l->h, l->w, l->c);
fprintf(fp, "stride=%d\n\n", l->stride);
}
void print_normalization_cfg(FILE *fp, normalization_layer *l, int first)
{
fprintf(fp, "[localresponsenormalization]\n");
if(first) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->batch,l->h, l->w, l->c);
fprintf(fp, "size=%d\n"
"alpha=%g\n"
"beta=%g\n"
"kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
}
void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
fprintf(fp, "[softmax]\n");
@ -88,6 +114,8 @@ void save_network(network net, char *filename)
print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
else if(net.types[i] == NORMALIZATION)
print_normalization_cfg(fp, (normalization_layer *)net.layers[i], i==0);
else if(net.types[i] == SOFTMAX)
print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
}
@ -118,6 +146,11 @@ void forward_network(network net, float *input)
forward_maxpool_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == NORMALIZATION){
normalization_layer layer = *(normalization_layer *)net.layers[i];
forward_normalization_layer(layer, input);
input = layer.output;
}
}
}
@ -135,6 +168,9 @@ void update_network(network net, float step, float momentum, float decay)
else if(net.types[i] == SOFTMAX){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
else if(net.types[i] == NORMALIZATION){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
update_connected_layer(layer, step, momentum, decay);
@ -156,6 +192,9 @@ float *get_network_output_layer(network net, int i)
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
} else if(net.types[i] == NORMALIZATION){
normalization_layer layer = *(normalization_layer *)net.layers[i];
return layer.output;
}
return 0;
}
@ -233,6 +272,10 @@ float backward_network(network net, float *input, float *truth)
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
if(i != 0) backward_maxpool_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == NORMALIZATION){
normalization_layer layer = *(normalization_layer *)net.layers[i];
if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
@ -272,7 +315,7 @@ float train_network_sgd(network net, data d, int n, float step, float momentum,f
error += err;
++pos;
}
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
//if((i+1)%10 == 0){
@ -341,34 +384,34 @@ int get_network_output_size_layer(network net, int i)
}
/*
int resize_network(network net, int h, int w, int c)
{
int i;
for (i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer *layer = (convolutional_layer *)net.layers[i];
layer->h = h;
layer->w = w;
layer->c = c;
image output = get_convolutional_image(*layer);
h = output.h;
w = output.w;
c = output.c;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer *layer = (maxpool_layer *)net.layers[i];
layer->h = h;
layer->w = w;
layer->c = c;
image output = get_maxpool_image(*layer);
h = output.h;
w = output.w;
c = output.c;
}
}
return 0;
}
*/
int resize_network(network net, int h, int w, int c)
{
int i;
for (i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer *layer = (convolutional_layer *)net.layers[i];
layer->h = h;
layer->w = w;
layer->c = c;
image output = get_convolutional_image(*layer);
h = output.h;
w = output.w;
c = output.c;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer *layer = (maxpool_layer *)net.layers[i];
layer->h = h;
layer->w = w;
layer->c = c;
image output = get_maxpool_image(*layer);
h = output.h;
w = output.w;
c = output.c;
}
}
return 0;
}
*/
int resize_network(network net, int h, int w, int c)
{
@ -381,16 +424,21 @@ int resize_network(network net, int h, int w, int c)
h = output.h;
w = output.w;
c = output.c;
}
else if(net.types[i] == MAXPOOL){
}else if(net.types[i] == MAXPOOL){
maxpool_layer *layer = (maxpool_layer *)net.layers[i];
resize_maxpool_layer(layer, h, w, c);
image output = get_maxpool_image(*layer);
h = output.h;
w = output.w;
c = output.c;
}
else{
}else if(net.types[i] == NORMALIZATION){
normalization_layer *layer = (normalization_layer *)net.layers[i];
resize_normalization_layer(layer, h, w, c);
image output = get_normalization_image(*layer);
h = output.h;
w = output.w;
c = output.c;
}else{
error("Cannot resize this type of layer");
}
}
@ -413,6 +461,10 @@ image get_network_image_layer(network net, int i)
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);
}
return make_empty_image(0,0,0);
}
@ -437,6 +489,10 @@ void visualize_network(network net)
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);
}
}
}