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Nist NIN testing multi-crop

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This commit is contained in:
Joseph Redmon
2014-08-11 12:52:07 -07:00
parent 7add111509
commit 176d65b765
11 changed files with 288 additions and 51 deletions
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60
src/network.c
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@ -4,6 +4,7 @@
#include "data.h"
#include "utils.h"
#include "crop_layer.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
@ -56,6 +57,11 @@ void forward_network(network net, float *input, int train)
forward_softmax_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
forward_crop_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);
@ -85,6 +91,11 @@ void forward_network(network net, float *input, int train)
forward_connected_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
forward_crop_layer(layer, input);
input = layer.output;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
forward_softmax_layer(layer, input);
@ -266,12 +277,14 @@ float train_network_sgd(network net, data d, int n)
int i,j;
float sum = 0;
int index = 0;
for(i = 0; i < n; ++i){
for(j = 0; j < batch; ++j){
int index = rand()%d.X.rows;
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);
sum += err;
//train_network_datum(net, X, y);
@ -300,6 +313,7 @@ float train_network_sgd(network net, data d, int n)
//}
}
//printf("Accuracy: %f\n",(float) correct/n);
//show_image(float_to_image(32,32,3,X), "Orig");
free(X);
free(y);
return (float)sum/(n*batch);
@ -446,6 +460,10 @@ image get_network_image_layer(network net, int i)
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);
}
@ -464,6 +482,7 @@ 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){
@ -484,6 +503,31 @@ float *network_predict(network net, float *input)
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;
@ -525,6 +569,12 @@ void print_network(network net)
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;
@ -553,4 +603,12 @@ float network_accuracy(network net, data d)
return acc;
}
float network_accuracy_multi(network net, data d, int n)
{
matrix guess = network_predict_data_multi(net, d, n);
float acc = matrix_accuracy(d.y, guess);
free_matrix(guess);
return acc;
}