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darknet/src/network.c
Joseph Redmon 00d483697a Small updates
2014-04-30 16:17:40 -07:00

566 lines
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#include <stdio.h>
#include "network.h"
#include "image.h"
#include "data.h"
#include "utils.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "softmax_layer.h"
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;
net.output = 0;
return net;
}
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
{
int i;
fprintf(fp, "[convolutional]\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, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
"activation=%s\n",
l->n, l->size, l->stride,
get_activation_string(l->activation));
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)
{
int i;
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));
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]);
fprintf(fp, "\n\n");
}
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);
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");
if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}
void save_network(network net, char *filename)
{
FILE *fp = fopen(filename, "w");
if(!fp) file_error(filename);
int i;
for(i = 0; i < net.n; ++i)
{
if(net.types[i] == CONVOLUTIONAL)
print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], i==0);
else if(net.types[i] == CONNECTED)
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);
}
fclose(fp);
}
void forward_network(network net, float *input)
{
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);
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;
}
}
}
void update_network(network net, float step, float momentum, float decay)
{
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
update_convolutional_layer(layer, step, momentum, decay);
}
else if(net.types[i] == MAXPOOL){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
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);
}
}
}
float *get_network_output_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.output;
} else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.output;
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.output;
} 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;
}
float *get_network_output(network net)
{
return get_network_output_layer(net, net.n-1);
}
float *get_network_delta_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.delta;
} else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.delta;
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.delta;
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
}
return 0;
}
float *get_network_delta(network net)
{
return get_network_delta_layer(net, net.n-1);
}
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]);
delta[i] = truth[i] - out[i];
sum += delta[i]*delta[i];
}
//printf("\n");
return sum;
}
int get_predicted_class_network(network net)
{
float *out = get_network_output(net);
int k = get_network_output_size(net);
return max_index(out, k);
}
float backward_network(network net, float *input, float *truth)
{
float error = calculate_error_network(net, truth);
int i;
float *prev_input;
float *prev_delta;
for(i = net.n-1; i >= 0; --i){
if(i == 0){
prev_input = input;
prev_delta = 0;
}else{
prev_input = get_network_output_layer(net, i-1);
prev_delta = get_network_delta_layer(net, i-1);
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
learn_convolutional_layer(layer);
//learn_convolutional_layer(layer);
if(i != 0) backward_convolutional_layer(layer, prev_delta);
}
else if(net.types[i] == MAXPOOL){
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);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
learn_connected_layer(layer, prev_input);
if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
}
}
return error;
}
float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
{
forward_network(net, x);
//int class = get_predicted_class_network(net);
float error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
//return (y[class]?1:0);
return error;
}
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;
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);
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/pos;
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
{
int i;
int correct = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
float *x = d.X.vals[index];
float *y = d.y.vals[index];
forward_network(net, x);
int class = get_predicted_class_network(net);
backward_network(net, x, y);
correct += (y[class]?1:0);
}
update_network(net, step, momentum, decay);
return (float)correct/n;
}
void train_network(network net, data d, float step, float momentum, float decay)
{
int i;
int correct = 0;
for(i = 0; i < d.X.rows; ++i){
correct += train_network_datum(net, d.X.vals[i], d.y.vals[i], step, momentum, decay);
if(i%100 == 0){
visualize_network(net);
cvWaitKey(10);
}
}
visualize_network(net);
cvWaitKey(100);
fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
}
int get_network_output_size_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
image output = get_convolutional_image(layer);
return output.h*output.w*output.c;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
image output = get_maxpool_image(layer);
return output.h*output.w*output.c;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.outputs;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
}
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)
{
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 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");
}
}
return 0;
}
int get_network_output_size(network net)
{
int i = net.n-1;
return get_network_output_size_layer(net, i);
}
image get_network_image_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return get_convolutional_image(layer);
}
else if(net.types[i] == MAXPOOL){
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);
}
image get_network_image(network net)
{
int i;
for(i = net.n-1; i >= 0; --i){
image m = get_network_image_layer(net, i);
if(m.h != 0) return m;
}
return make_empty_image(0,0,0);
}
void visualize_network(network net)
{
image *prev = 0;
int i;
char buff[256];
for(i = 0; i < net.n; ++i){
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
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);
}
}
}
float *network_predict(network net, float *input)
{
forward_network(net, input);
float *out = get_network_output(net);
return out;
}
matrix network_predict_data(network net, data test)
{
int i,j;
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];
}
}
return pred;
}
void print_network(network net)
{
int i,j;
for(i = 0; i < net.n; ++i){
float *output = 0;
int n = 0;
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
output = layer.output;
image m = get_convolutional_image(layer);
n = m.h*m.w*m.c;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
output = layer.output;
image m = get_maxpool_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;
n = layer.outputs;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
output = layer.output;
n = layer.inputs;
}
float mean = mean_array(output, n);
float vari = variance_array(output, n);
fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
if(n > 100) n = 100;
for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
if(n == 100)fprintf(stderr,".....\n");
fprintf(stderr, "\n");
}
}
float network_accuracy(network net, data d)
{
matrix guess = network_predict_data(net, d);
float acc = matrix_accuracy(d.y, guess);
free_matrix(guess);
return acc;
}
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