darknet/src/parser.c

169 lines
5.0 KiB
C
Raw Normal View History

2013-11-13 22:50:38 +04:00
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include "parser.h"
#include "activations.h"
#include "convolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
#include "list.h"
#include "option_list.h"
#include "utils.h"
typedef struct{
char *type;
list *options;
}section;
int is_convolutional(section *s);
int is_connected(section *s);
int is_maxpool(section *s);
list *read_cfg(char *filename);
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
network net = make_network(sections->size);
node *n = sections->front;
int count = 0;
while(n){
section *s = (section *)n->val;
list *options = s->options;
if(is_convolutional(s)){
int h,w,c;
int n = option_find_int(options, "filters",1);
int size = option_find_int(options, "size",1);
int stride = option_find_int(options, "stride",1);
char *activation_s = option_find_str(options, "activation", "sigmoid");
ACTIVATION activation = get_activation(activation_s);
if(count == 0){
h = option_find_int(options, "height",1);
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
}else{
image m = get_network_image_layer(net, count-1);
h = m.h;
w = m.w;
c = m.c;
if(h == 0) error("Layer before convolutional layer must output image.");
}
convolutional_layer *layer = make_convolutional_layer(h,w,c,n,size,stride, activation);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
option_unused(options);
}
else if(is_connected(s)){
int input;
int output = option_find_int(options, "output",1);
char *activation_s = option_find_str(options, "activation", "sigmoid");
ACTIVATION activation = get_activation(activation_s);
if(count == 0){
input = option_find_int(options, "input",1);
}else{
input = get_network_output_size_layer(net, count-1);
}
connected_layer *layer = make_connected_layer(input, output, activation);
net.types[count] = CONNECTED;
net.layers[count] = layer;
option_unused(options);
}else if(is_maxpool(s)){
int h,w,c;
int stride = option_find_int(options, "stride",1);
//char *activation_s = option_find_str(options, "activation", "sigmoid");
if(count == 0){
h = option_find_int(options, "height",1);
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
}else{
image m = get_network_image_layer(net, count-1);
h = m.h;
w = m.w;
c = m.c;
if(h == 0) error("Layer before convolutional layer must output image.");
}
maxpool_layer *layer = make_maxpool_layer(h,w,c,stride);
net.types[count] = MAXPOOL;
net.layers[count] = layer;
option_unused(options);
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
++count;
n = n->next;
}
return net;
}
int is_convolutional(section *s)
{
return (strcmp(s->type, "[conv]")==0
|| strcmp(s->type, "[convolutional]")==0);
}
int is_connected(section *s)
{
return (strcmp(s->type, "[conn]")==0
|| strcmp(s->type, "[connected]")==0);
}
int is_maxpool(section *s)
{
return (strcmp(s->type, "[max]")==0
|| strcmp(s->type, "[maxpool]")==0);
}
int read_option(char *s, list *options)
{
int i;
int len = strlen(s);
char *val = 0;
for(i = 0; i < len; ++i){
if(s[i] == '='){
s[i] = '\0';
val = s+i+1;
break;
}
}
if(i == len-1) return 0;
char *key = s;
option_insert(options, key, val);
return 1;
}
list *read_cfg(char *filename)
{
FILE *file = fopen(filename, "r");
if(file == 0) file_error(filename);
char *line;
int nu = 0;
list *sections = make_list();
section *current = 0;
while((line=fgetl(file)) != 0){
++ nu;
strip(line);
switch(line[0]){
case '[':
current = malloc(sizeof(section));
list_insert(sections, current);
current->options = make_list();
current->type = line;
break;
case '\0':
case '#':
case ';':
free(line);
break;
default:
if(!read_option(line, current->options)){
printf("Config file error line %d, could parse: %s\n", nu, line);
free(line);
}
break;
}
}
fclose(file);
return sections;
}