darknet/src/network.c
2014-11-05 14:49:58 -08:00

617 lines
19 KiB
C

#include <stdio.h>
#include <time.h>
#include "network.h"
#include "image.h"
#include "data.h"
#include "utils.h"
#include "crop_layer.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
#include "cost_layer.h"
#include "normalization_layer.h"
#include "freeweight_layer.h"
#include "softmax_layer.h"
#include "dropout_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;
#ifdef GPU
net.input_cl = calloc(1, sizeof(cl_mem));
net.truth_cl = calloc(1, sizeof(cl_mem));
#endif
return net;
}
void forward_network(network net, float *input, float *truth, 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);
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] == COST){
cost_layer layer = *(cost_layer *)net.layers[i];
forward_cost_layer(layer, input, truth);
}
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;
}
else if(net.types[i] == DROPOUT){
if(!train) continue;
dropout_layer layer = *(dropout_layer *)net.layers[i];
forward_dropout_layer(layer, input);
}
else if(net.types[i] == FREEWEIGHT){
if(!train) continue;
freeweight_layer layer = *(freeweight_layer *)net.layers[i];
forward_freeweight_layer(layer, input);
}
}
}
void update_network(network net)
{
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);
}
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);
}
}
}
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] == DROPOUT){
return get_network_output_layer(net, i-1);
} else if(net.types[i] == FREEWEIGHT){
return get_network_output_layer(net, i-1);
} 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)
{
int i;
for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
return get_network_output_layer(net, i);
}
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] == DROPOUT){
return get_network_delta_layer(net, i-1);
} else if(net.types[i] == FREEWEIGHT){
return get_network_delta_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
}
return 0;
}
float get_network_cost(network net)
{
if(net.types[net.n-1] == COST){
return ((cost_layer *)net.layers[net.n-1])->output[0];
}
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;
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]);
//if(i == get_network_output_size(net)) printf("\n");
delta[i] = truth[i] - out[i];
//printf("%.10f, ", 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);
}
void backward_network(network net, float *input)
{
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];
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_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_delta);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
backward_connected_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == COST){
cost_layer layer = *(cost_layer *)net.layers[i];
backward_cost_layer(layer, prev_input, prev_delta);
}
}
}
float train_network_datum(network net, float *x, float *y)
{
forward_network(net, x, y, 1);
//int class = get_predicted_class_network(net);
backward_network(net, x);
float error = get_network_cost(net);
update_network(net);
//return (y[class]?1:0);
return error;
}
float train_network_sgd(network net, data d, int n)
{
int batch = net.batch;
float *X = calloc(batch*d.X.cols, sizeof(float));
float *y = calloc(batch*d.y.cols, sizeof(float));
int i;
float sum = 0;
for(i = 0; i < n; ++i){
get_random_batch(d, batch, X, y);
float err = train_network_datum(net, X, y);
sum += err;
}
free(X);
free(y);
return (float)sum/(n*batch);
}
float train_network_batch(network net, data d, int n)
{
int i,j;
float sum = 0;
int batch = 2;
for(i = 0; i < n; ++i){
for(j = 0; j < batch; ++j){
int index = rand()%d.X.rows;
float *x = d.X.vals[index];
float *y = d.y.vals[index];
forward_network(net, x, y, 1);
backward_network(net, x);
sum += get_network_cost(net);
}
update_network(net);
}
return (float)sum/(n*batch);
}
float train_network_data_cpu(network net, data d, int n)
{
int batch = net.batch;
float *X = calloc(batch*d.X.cols, sizeof(float));
float *y = calloc(batch*d.y.cols, sizeof(float));
int i;
float sum = 0;
for(i = 0; i < n; ++i){
get_next_batch(d, batch, i*batch, X, y);
float err = train_network_datum(net, X, y);
sum += err;
}
free(X);
free(y);
return (float)sum/(n*batch);
}
void train_network(network net, data d)
{
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]);
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_input_size_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.h*layer.w*layer.c;
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.h*layer.w*layer.c;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.inputs;
} else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
else if(net.types[i] == FREEWEIGHT){
freeweight_layer layer = *(freeweight_layer *) net.layers[i];
return layer.inputs;
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
}
return 0;
}
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] == DROPOUT){
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
else if(net.types[i] == FREEWEIGHT){
freeweight_layer layer = *(freeweight_layer *) net.layers[i];
return layer.inputs;
}
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];
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;
for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
return get_network_output_size_layer(net, i);
}
int get_network_input_size(network net)
{
return get_network_input_size_layer(net, 0);
}
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);
}
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);
}
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];
//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){
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);
}
}
}
void top_predictions(network net, int n, int *index)
{
int i,j;
int k = get_network_output_size(net);
float *out = get_network_output(net);
float thresh = FLT_MAX;
for(i = 0; i < n; ++i){
float max = -FLT_MAX;
int max_i = -1;
for(j = 0; j < k; ++j){
float val = out[j];
if(val > max && val < thresh){
max = val;
max_i = j;
}
}
index[i] = max_i;
thresh = max;
}
}
float *network_predict(network net, float *input)
{
forward_network(net, input, 0, 0);
float *out = get_network_output(net);
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;
int k = get_network_output_size(net);
matrix pred = make_matrix(test.X.rows, k);
float *X = calloc(net.batch*test.X.cols, 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;
}
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] == 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;
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;
}
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;
}