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Added batch to col2im, padding option

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This commit is contained in:
Joseph Redmon
2014-07-13 22:07:51 -07:00
parent cd8d53df21
commit 70d622ea54
20 changed files with 428 additions and 134 deletions
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112
src/network.c
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@ -113,10 +113,9 @@ void save_network(network net, char *filename)
fclose(fp);
}
#ifdef GPU
void forward_network(network net, float *input, int train)
{
int i;
#ifdef GPU
cl_setup();
size_t size = get_network_input_size(net);
if(!net.input_cl){
@ -126,16 +125,12 @@ void forward_network(network net, float *input, int train)
}
cl_write_array(net.input_cl, input, size);
cl_mem input_cl = net.input_cl;
#endif
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
#ifdef GPU
forward_convolutional_layer_gpu(layer, input_cl);
input_cl = layer.output_cl;
#else
forward_convolutional_layer(layer, input);
#endif
input = layer.output;
}
else if(net.types[i] == CONNECTED){
@ -161,6 +156,41 @@ void forward_network(network net, float *input, int train)
}
}
#else
void forward_network(network net, float *input, int train)
{
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
forward_convolutional_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
forward_connected_layer(layer, input, train);
input = layer.output;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
forward_softmax_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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;
}
}
}
#endif
void update_network(network net, float step, float momentum, float decay)
{
int i;
@ -238,9 +268,10 @@ float calculate_error_network(network net, float *truth)
float sum = 0;
float *delta = get_network_delta(net);
float *out = get_network_output(net);
int i, k = get_network_output_size(net);
for(i = 0; i < k; ++i){
//printf("%f, ", out[i]);
int i;
for(i = 0; i < get_network_output_size(net)*net.batch; ++i){
//if(i %get_network_output_size(net) == 0) printf("\n");
//printf("%5.2f %5.2f, ", out[i], truth[i]);
delta[i] = truth[i] - out[i];
sum += delta[i]*delta[i];
}
@ -305,20 +336,38 @@ float train_network_datum(network net, float *x, float *y, float step, float mom
float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
{
int i;
float error = 0;
int correct = 0;
int pos = 0;
int batch = net.batch;
float *X = calloc(batch*d.X.cols, sizeof(float));
float *y = calloc(batch*d.y.cols, sizeof(float));
int i,j;
float sum = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
float err = train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
for(j = 0; j < batch; ++j){
int index = rand()%d.X.rows;
memcpy(X+j*d.X.cols, d.X.vals[index], d.X.cols*sizeof(float));
memcpy(y+j*d.y.cols, d.y.vals[index], d.y.cols*sizeof(float));
}
float err = train_network_datum(net, X, y, step, momentum, decay);
sum += err;
//train_network_datum(net, X, y, 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;
*/
/*
for(j = 0; j < d.y.cols*batch; ++j){
printf("%6.3f ", y[j]);
}
printf("\n");
for(j = 0; j < d.y.cols*batch; ++j){
printf("%6.3f ", get_network_output(net)[j]);
}
printf("\n");
printf("\n");
*/
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
@ -327,7 +376,9 @@ float train_network_sgd(network net, data d, int n, float step, float momentum,f
//}
}
//printf("Accuracy: %f\n",(float) correct/n);
return error/pos;
free(X);
free(y);
return (float)sum/(n*batch);
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
{
@ -448,7 +499,7 @@ int get_network_output_size(network net)
int get_network_input_size(network net)
{
return get_network_output_size_layer(net, 0);
return get_network_input_size_layer(net, 0);
}
image get_network_image_layer(network net, int i)
@ -505,15 +556,24 @@ float *network_predict(network net, float *input)
matrix network_predict_data(network net, data test)
{
int i,j;
int i,j,b;
int k = get_network_output_size(net);
matrix pred = make_matrix(test.X.rows, k);
for(i = 0; i < test.X.rows; ++i){
float *out = network_predict(net, test.X.vals[i]);
for(j = 0; j < k; ++j){
pred.vals[i][j] = out[j];
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));
}
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;
}