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So there WAS this huge bug. Gone now

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This commit is contained in:
Joseph Redmon
2014-05-09 15:14:52 -07:00
parent 5ef74c2031
commit cd8d53df21
22 changed files with 769 additions and 193 deletions
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103
src/network.c
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@ -19,6 +19,9 @@ network make_network(int n, int batch)
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
net.output = 0;
#ifdef GPU
net.input_cl = 0;
#endif
return net;
}
@ -40,17 +43,6 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
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)
@ -121,18 +113,34 @@ void save_network(network net, char *filename)
fclose(fp);
}
void forward_network(network net, float *input)
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){
net.input_cl = clCreateBuffer(cl.context,
CL_MEM_READ_WRITE, size*sizeof(float), 0, &cl.error);
check_error(cl);
}
cl_write_array(net.input_cl, input, size);
cl_mem input_cl = net.input_cl;
#endif
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){
connected_layer layer = *(connected_layer *)net.layers[i];
forward_connected_layer(layer, input);
forward_connected_layer(layer, input, train);
input = layer.output;
}
else if(net.types[i] == SOFTMAX){
@ -263,9 +271,7 @@ float backward_network(network net, float *input, float *truth)
}
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);
backward_convolutional_layer(layer, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@ -281,8 +287,7 @@ float backward_network(network net, float *input, float *truth)
}
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);
backward_connected_layer(layer, prev_input, prev_delta);
}
}
return error;
@ -290,7 +295,7 @@ float backward_network(network net, float *input, float *truth)
float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
{
forward_network(net, x);
forward_network(net, x, 1);
//int class = get_predicted_class_network(net);
float error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
@ -332,7 +337,7 @@ float train_network_batch(network net, data d, int n, float step, float momentum
int index = rand()%d.X.rows;
float *x = d.X.vals[index];
float *y = d.y.vals[index];
forward_network(net, x);
forward_network(net, x, 1);
int class = get_predicted_class_network(net);
backward_network(net, x, y);
correct += (y[class]?1:0);
@ -359,6 +364,27 @@ void train_network(network net, data d, float step, float momentum, float decay)
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] == 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){
@ -382,36 +408,6 @@ int get_network_output_size_layer(network net, int i)
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;
@ -450,6 +446,11 @@ int get_network_output_size(network net)
return get_network_output_size_layer(net, i);
}
int get_network_input_size(network net)
{
return get_network_output_size_layer(net, 0);
}
image get_network_image_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
@ -497,7 +498,7 @@ void visualize_network(network net)
float *network_predict(network net, float *input)
{
forward_network(net, input);
forward_network(net, input, 0);
float *out = get_network_output(net);
return out;
}