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darknet/src/parser.c

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#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include "parser.h"
#include "activations.h"
#include "crop_layer.h"
#include "cost_layer.h"
#include "convolutional_layer.h"
#include "deconvolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "detection_layer.h"
#include "route_layer.h"
#include "list.h"
#include "option_list.h"
#include "utils.h"
typedef struct{
char *type;
list *options;
}section;
int is_network(section *s);
int is_convolutional(section *s);
int is_deconvolutional(section *s);
int is_connected(section *s);
int is_maxpool(section *s);
int is_dropout(section *s);
int is_softmax(section *s);
int is_crop(section *s);
int is_cost(section *s);
int is_detection(section *s);
int is_route(section *s);
list *read_cfg(char *filename);
void free_section(section *s)
{
free(s->type);
node *n = s->options->front;
while(n){
kvp *pair = (kvp *)n->val;
free(pair->key);
free(pair);
node *next = n->next;
free(n);
n = next;
}
free(s->options);
free(s);
}
void parse_data(char *data, float *a, int n)
{
int i;
if(!data) return;
char *curr = data;
char *next = data;
int done = 0;
for(i = 0; i < n && !done; ++i){
while(*++next !='\0' && *next != ',');
if(*next == '\0') done = 1;
*next = '\0';
sscanf(curr, "%g", &a[i]);
curr = next+1;
}
}
typedef struct size_params{
int batch;
int inputs;
int h;
int w;
int c;
} size_params;
deconvolutional_layer parse_deconvolutional(list *options, size_params params)
{
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", "logistic");
ACTIVATION activation = get_activation(activation_s);
int batch,h,w,c;
h = params.h;
w = params.w;
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before deconvolutional layer must output image.");
deconvolutional_layer layer = make_deconvolutional_layer(batch,h,w,c,n,size,stride,activation);
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(weights, layer.filters, c*n*size*size);
parse_data(biases, layer.biases, n);
#ifdef GPU
if(weights || biases) push_deconvolutional_layer(layer);
#endif
option_unused(options);
return layer;
}
convolutional_layer parse_convolutional(list *options, size_params params)
{
int n = option_find_int(options, "filters",1);
int size = option_find_int(options, "size",1);
int stride = option_find_int(options, "stride",1);
int pad = option_find_int(options, "pad",0);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
int batch,h,w,c;
h = params.h;
w = params.w;
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before convolutional layer must output image.");
convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation);
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(weights, layer.filters, c*n*size*size);
parse_data(biases, layer.biases, n);
#ifdef GPU
if(weights || biases) push_convolutional_layer(layer);
#endif
option_unused(options);
return layer;
}
connected_layer parse_connected(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
connected_layer layer = make_connected_layer(params.batch, params.inputs, output, activation);
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(biases, layer.biases, output);
parse_data(weights, layer.weights, params.inputs*output);
#ifdef GPU
if(weights || biases) push_connected_layer(layer);
#endif
option_unused(options);
return layer;
}
softmax_layer parse_softmax(list *options, size_params params)
{
int groups = option_find_int(options, "groups",1);
softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
option_unused(options);
return layer;
}
detection_layer parse_detection(list *options, size_params params)
{
int coords = option_find_int(options, "coords", 1);
int classes = option_find_int(options, "classes", 1);
int rescore = option_find_int(options, "rescore", 0);
int joint = option_find_int(options, "joint", 0);
int objectness = option_find_int(options, "objectness", 0);
int background = option_find_int(options, "background", 1);
detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, joint, rescore, background, objectness);
option_unused(options);
return layer;
}
cost_layer parse_cost(list *options, size_params params)
{
char *type_s = option_find_str(options, "type", "sse");
COST_TYPE type = get_cost_type(type_s);
cost_layer layer = make_cost_layer(params.batch, params.inputs, type);
option_unused(options);
return layer;
}
crop_layer parse_crop(list *options, size_params params)
{
int crop_height = option_find_int(options, "crop_height",1);
int crop_width = option_find_int(options, "crop_width",1);
int flip = option_find_int(options, "flip",0);
float angle = option_find_float(options, "angle",0);
float saturation = option_find_float(options, "saturation",1);
float exposure = option_find_float(options, "exposure",1);
int batch,h,w,c;
h = params.h;
w = params.w;
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before crop layer must output image.");
crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
option_unused(options);
return l;
}
maxpool_layer parse_maxpool(list *options, size_params params)
{
int stride = option_find_int(options, "stride",1);
int size = option_find_int(options, "size",stride);
int batch,h,w,c;
h = params.h;
w = params.w;
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before maxpool layer must output image.");
maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride);
option_unused(options);
return layer;
}
dropout_layer parse_dropout(list *options, size_params params)
{
float probability = option_find_float(options, "probability", .5);
dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability);
option_unused(options);
return layer;
}
route_layer parse_route(list *options, size_params params, network net)
{
char *l = option_find(options, "layers");
int len = strlen(l);
if(!l) error("Route Layer must specify input layers");
int n = 1;
int i;
for(i = 0; i < len; ++i){
if (l[i] == ',') ++n;
}
int *layers = calloc(n, sizeof(int));
int *sizes = calloc(n, sizeof(int));
for(i = 0; i < n; ++i){
int index = atoi(l);
l = strchr(l, ',')+1;
layers[i] = index;
sizes[i] = net.layers[index].outputs;
}
int batch = params.batch;
route_layer layer = make_route_layer(batch, n, layers, sizes);
convolutional_layer first = net.layers[layers[0]];
layer.out_w = first.out_w;
layer.out_h = first.out_h;
layer.out_c = first.out_c;
for(i = 1; i < n; ++i){
int index = layers[i];
convolutional_layer next = net.layers[index];
if(next.out_w == first.out_w && next.out_h == first.out_h){
layer.out_c += next.out_c;
}else{
layer.out_h = layer.out_w = layer.out_c = 0;
}
}
option_unused(options);
return layer;
}
void parse_net_options(list *options, network *net)
{
net->batch = option_find_int(options, "batch",1);
net->learning_rate = option_find_float(options, "learning_rate", .001);
net->momentum = option_find_float(options, "momentum", .9);
net->decay = option_find_float(options, "decay", .0001);
net->seen = option_find_int(options, "seen",0);
int subdivs = option_find_int(options, "subdivisions",1);
net->batch /= subdivs;
net->subdivisions = subdivs;
net->h = option_find_int_quiet(options, "height",0);
net->w = option_find_int_quiet(options, "width",0);
net->c = option_find_int_quiet(options, "channels",0);
net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
option_unused(options);
}
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;
n = n->next;
int count = 0;
while(n){
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_deconvolutional(s)){
l = parse_deconvolutional(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_maxpool(s)){
l = parse_maxpool(options, params);
}else if(is_route(s)){
l = parse_route(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);
}
net.layers[count] = l;
free_section(s);
n = n->next;
if(n){
params.h = l.out_h;
params.w = l.out_w;
params.c = l.out_c;
params.inputs = l.outputs;
}
++count;
}
free_list(sections);
net.outputs = get_network_output_size(net);
net.output = get_network_output(net);
return net;
}
int is_crop(section *s)
{
return (strcmp(s->type, "[crop]")==0);
}
int is_cost(section *s)
{
return (strcmp(s->type, "[cost]")==0);
}
int is_detection(section *s)
{
return (strcmp(s->type, "[detection]")==0);
}
int is_deconvolutional(section *s)
{
return (strcmp(s->type, "[deconv]")==0
|| strcmp(s->type, "[deconvolutional]")==0);
}
int is_convolutional(section *s)
{
return (strcmp(s->type, "[conv]")==0
|| strcmp(s->type, "[convolutional]")==0);
}
int is_network(section *s)
{
return (strcmp(s->type, "[net]")==0
|| strcmp(s->type, "[network]")==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 is_dropout(section *s)
{
return (strcmp(s->type, "[dropout]")==0);
}
int is_softmax(section *s)
{
return (strcmp(s->type, "[soft]")==0
|| strcmp(s->type, "[softmax]")==0);
}
int is_route(section *s)
{
return (strcmp(s->type, "[route]")==0);
}
int read_option(char *s, list *options)
{
size_t i;
size_t 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)){
fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
free(line);
}
break;
}
}
fclose(file);
return sections;
}
void save_weights(network net, char *filename)
{
fprintf(stderr, "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){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(l);
}
#endif
int num = l.n*l.c*l.size*l.size;
fwrite(l.biases, sizeof(float), l.n, fp);
fwrite(l.filters, sizeof(float), num, fp);
}
if(l.type == DECONVOLUTIONAL){
#ifdef GPU
if(gpu_index >= 0){
pull_deconvolutional_layer(l);
}
#endif
int num = l.n*l.c*l.size*l.size;
fwrite(l.biases, sizeof(float), l.n, fp);
fwrite(l.filters, sizeof(float), num, fp);
}
if(l.type == CONNECTED){
#ifdef GPU
if(gpu_index >= 0){
pull_connected_layer(l);
}
#endif
fwrite(l.biases, sizeof(float), l.outputs, fp);
fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp);
}
}
fclose(fp);
}
void load_weights_upto(network *net, char *filename, int cutoff)
{
fprintf(stderr, "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);
fprintf(stderr, "%f %f %f %d\n", net->learning_rate, net->momentum, net->decay, net->seen);
int i;
for(i = 0; i < net->n && i < cutoff; ++i){
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
int num = l.n*l.c*l.size*l.size;
fread(l.biases, sizeof(float), l.n, fp);
fread(l.filters, sizeof(float), num, fp);
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(l);
}
#endif
}
if(l.type == DECONVOLUTIONAL){
int num = l.n*l.c*l.size*l.size;
fread(l.biases, sizeof(float), l.n, fp);
fread(l.filters, sizeof(float), num, fp);
#ifdef GPU
if(gpu_index >= 0){
push_deconvolutional_layer(l);
}
#endif
}
if(l.type == CONNECTED){
fread(l.biases, sizeof(float), l.outputs, fp);
fread(l.weights, sizeof(float), l.outputs*l.inputs, fp);
#ifdef GPU
if(gpu_index >= 0){
push_connected_layer(l);
}
#endif
}
}
fclose(fp);
}
void load_weights(network *net, char *filename)
{
load_weights_upto(net, filename, net->n);
}
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