fint MemObj::total_size_in_oops() {
if (is_activation()) {
return ((ActivationObj*)this)->total_size_in_oops();
} else if (is_byteVector()) {
return ((ByteVectorObj*)this)->total_size_in_oops();
} else {
oop_t* start = (oop_t*)this;
oop_t* end = start;
while ( !is_mark(*++end) ) {}
return end - start;
}
}
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
node *n = sections->front;
if(!n) error("Config file has no sections");
network net = make_network(sections->size - 1);
size_params params;
section *s = (section *)n->val;
list *options = s->options;
if(!is_network(s)) error("First section must be [net] or [network]");
parse_net_options(options, &net);
params.h = net.h;
params.w = net.w;
params.c = net.c;
params.inputs = net.inputs;
params.batch = net.batch;
params.time_steps = net.time_steps;
size_t workspace_size = 0;
n = n->next;
int count = 0;
free_section(s);
while(n){
params.index = count;
fprintf(stderr, "%d: ", count);
s = (section *)n->val;
options = s->options;
layer l = {0};
if(is_convolutional(s)){
l = parse_convolutional(options, params);
}else if(is_local(s)){
l = parse_local(options, params);
}else if(is_activation(s)){
l = parse_activation(options, params);
}else if(is_deconvolutional(s)){
l = parse_deconvolutional(options, params);
}else if(is_rnn(s)){
l = parse_rnn(options, params);
}else if(is_gru(s)){
l = parse_gru(options, params);
}else if(is_crnn(s)){
l = parse_crnn(options, params);
}else if(is_connected(s)){
l = parse_connected(options, params);
}else if(is_crop(s)){
l = parse_crop(options, params);
}else if(is_cost(s)){
l = parse_cost(options, params);
}else if(is_detection(s)){
l = parse_detection(options, params);
}else if(is_softmax(s)){
l = parse_softmax(options, params);
}else if(is_normalization(s)){
l = parse_normalization(options, params);
}else if(is_batchnorm(s)){
l = parse_batchnorm(options, params);
}else if(is_maxpool(s)){
l = parse_maxpool(options, params);
}else if(is_avgpool(s)){
l = parse_avgpool(options, params);
}else if(is_route(s)){
l = parse_route(options, params, net);
}else if(is_shortcut(s)){
l = parse_shortcut(options, params, net);
}else if(is_dropout(s)){
l = parse_dropout(options, params);
l.output = net.layers[count-1].output;
l.delta = net.layers[count-1].delta;
#ifdef GPU
l.output_gpu = net.layers[count-1].output_gpu;
l.delta_gpu = net.layers[count-1].delta_gpu;
#endif
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
l.dontload = option_find_int_quiet(options, "dontload", 0);
l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
option_unused(options);
net.layers[count] = l;
if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
free_section(s);
n = n->next;
++count;
if(n){
params.h = l.out_h;
params.w = l.out_w;
params.c = l.out_c;
params.inputs = l.outputs;
}
}
free_list(sections);
net.outputs = get_network_output_size(net);
net.output = get_network_output(net);
if(workspace_size){
//printf("%ld\n", workspace_size);
#ifdef GPU
net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
#else
net.workspace = calloc(1, workspace_size);
#endif
}
return net;
}