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Better VOC handling and resizing

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This commit is contained in:
Joseph Redmon
2014-03-12 21:57:34 -07:00
parent 15e86996d6
commit 2ea63c0e99
21 changed files with 288 additions and 173 deletions
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67
src/network.c
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@ -10,10 +10,11 @@
#include "maxpool_layer.h"
#include "softmax_layer.h"
network make_network(int n)
network make_network(int n, int batch)
{
network net;
net.n = n;
net.batch = batch;
net.layers = calloc(net.n, sizeof(void *));
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
@ -25,10 +26,11 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
{
int i;
fprintf(fp, "[convolutional]\n");
if(first) fprintf(fp, "height=%d\n"
if(first) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->h, l->w, l->c);
l->batch,l->h, l->w, l->c);
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
@ -44,7 +46,7 @@ void print_connected_cfg(FILE *fp, connected_layer *l, int first)
{
int i;
fprintf(fp, "[connected]\n");
if(first) fprintf(fp, "input=%d\n", l->inputs);
if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "output=%d\n"
"activation=%s\n",
l->outputs,
@ -58,17 +60,18 @@ void print_connected_cfg(FILE *fp, connected_layer *l, int first)
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
{
fprintf(fp, "[maxpool]\n");
if(first) fprintf(fp, "height=%d\n"
if(first) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->h, l->w, l->c);
l->batch,l->h, l->w, l->c);
fprintf(fp, "stride=%d\n\n", l->stride);
}
void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
fprintf(fp, "[softmax]\n");
if(first) fprintf(fp, "input=%d\n", l->inputs);
if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}
@ -191,11 +194,11 @@ float calculate_error_network(network net, float *truth)
float *out = get_network_output(net);
int i, k = get_network_output_size(net);
for(i = 0; i < k; ++i){
printf("%f, ", out[i]);
//printf("%f, ", out[i]);
delta[i] = truth[i] - out[i];
sum += delta[i]*delta[i];
}
printf("\n");
//printf("\n");
return sum;
}
@ -258,19 +261,26 @@ float train_network_sgd(network net, data d, int n, float step, float momentum,f
int i;
float error = 0;
int correct = 0;
int pos = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
float err = train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
float *y = d.y.vals[index];
int class = get_predicted_class_network(net);
correct += (y[class]?1:0);
if(y[1]){
error += err;
++pos;
}
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
//if((i+1)%10 == 0){
// printf("%d: %f\n", (i+1), (float)correct/(i+1));
//}
}
printf("Accuracy: %f\n",(float) correct/n);
return error/n;
//printf("Accuracy: %f\n",(float) correct/n);
return error/pos;
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
{
@ -304,7 +314,7 @@ void train_network(network net, data d, float step, float momentum, float decay)
}
visualize_network(net);
cvWaitKey(100);
printf("Accuracy: %f\n", (float)correct/d.X.rows);
fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
}
int get_network_output_size_layer(network net, int i)
@ -330,7 +340,8 @@ int get_network_output_size_layer(network net, int i)
return 0;
}
int reset_network_size(network net, int h, int w, int c)
/*
int resize_network(network net, int h, int w, int c)
{
int i;
for (i = 0; i < net.n; ++i){
@ -357,6 +368,34 @@ int reset_network_size(network net, int h, int w, int 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];
resize_convolutional_layer(layer, h, w, 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];
resize_maxpool_layer(layer, h, w, c);
image output = get_maxpool_image(*layer);
h = output.h;
w = output.w;
c = output.c;
}
else{
error("Cannot resize this type of layer");
}
}
return 0;
}
int get_network_output_size(network net)
{