mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
169 lines
5.0 KiB
C
169 lines
5.0 KiB
C
|
#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;
|
||
|
}
|
||
|
|