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saving weight files as binaries, hell yeah

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This commit is contained in:
Joseph Redmon
2015-02-06 18:53:53 -08:00
parent bfffadc755
commit 2f62fe33c9
9 changed files with 147 additions and 47 deletions
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80
src/parser.c
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@ -103,7 +103,7 @@ convolutional_layer *parse_convolutional(list *options, network *net, int count)
parse_data(weights, layer->filters, c*n*size*size);
parse_data(biases, layer->biases, n);
#ifdef GPU
push_convolutional_layer(*layer);
if(weights || biases) push_convolutional_layer(*layer);
#endif
option_unused(options);
return layer;
@ -137,7 +137,7 @@ connected_layer *parse_connected(list *options, network *net, int count)
parse_data(biases, layer->biases, output);
parse_data(weights, layer->weights, input*output);
#ifdef GPU
push_connected_layer(*layer);
if(weights || biases) push_connected_layer(*layer);
#endif
option_unused(options);
return layer;
@ -597,6 +597,82 @@ void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
fprintf(fp, "\n");
}
void save_weights(network net, char *filename)
{
printf("Saving weights to %s\n", filename);
FILE *fp = fopen(filename, "w");
if(!fp) file_error(filename);
fwrite(&net.learning_rate, sizeof(float), 1, fp);
fwrite(&net.momentum, sizeof(float), 1, fp);
fwrite(&net.decay, sizeof(float), 1, fp);
fwrite(&net.seen, sizeof(int), 1, fp);
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net.layers[i];
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(layer);
}
#endif
int num = layer.n*layer.c*layer.size*layer.size;
fwrite(layer.biases, sizeof(float), layer.n, fp);
fwrite(layer.filters, sizeof(float), num, fp);
}
if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net.layers[i];
#ifdef GPU
if(gpu_index >= 0){
pull_connected_layer(layer);
}
#endif
fwrite(layer.biases, sizeof(float), layer.outputs, fp);
fwrite(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
}
}
fclose(fp);
}
void load_weights(network *net, char *filename)
{
printf("Loading weights from %s\n", filename);
FILE *fp = fopen(filename, "r");
if(!fp) file_error(filename);
fread(&net->learning_rate, sizeof(float), 1, fp);
fread(&net->momentum, sizeof(float), 1, fp);
fread(&net->decay, sizeof(float), 1, fp);
fread(&net->seen, sizeof(int), 1, fp);
set_learning_network(net, net->learning_rate, net->momentum, net->decay);
int i;
for(i = 0; i < net->n; ++i){
if(net->types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net->layers[i];
int num = layer.n*layer.c*layer.size*layer.size;
fread(layer.biases, sizeof(float), layer.n, fp);
fread(layer.filters, sizeof(float), num, fp);
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(layer);
}
#endif
}
if(net->types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net->layers[i];
fread(layer.biases, sizeof(float), layer.outputs, fp);
fread(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
#ifdef GPU
if(gpu_index >= 0){
push_connected_layer(layer);
}
#endif
}
}
fclose(fp);
}
void save_network(network net, char *filename)
{