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darknet/src/parser.c

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#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <assert.h>
#include "activation_layer.h"
#include "logistic_layer.h"
#include "l2norm_layer.h"
#include "activations.h"
#include "avgpool_layer.h"
#include "batchnorm_layer.h"
#include "blas.h"
#include "connected_layer.h"
#include "deconvolutional_layer.h"
#include "convolutional_layer.h"
#include "cost_layer.h"
#include "crnn_layer.h"
#include "crop_layer.h"
#include "detection_layer.h"
#include "dropout_layer.h"
#include "gru_layer.h"
#include "list.h"
#include "local_layer.h"
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "option_list.h"
#include "parser.h"
#include "region_layer.h"
#include "yolo_layer.h"
#include "iseg_layer.h"
#include "reorg_layer.h"
#include "rnn_layer.h"
#include "route_layer.h"
#include "upsample_layer.h"
#include "shortcut_layer.h"
#include "softmax_layer.h"
#include "lstm_layer.h"
#include "utils.h"
typedef struct{
char *type;
list *options;
}section;
list *read_cfg(char *filename);
LAYER_TYPE string_to_layer_type(char * type)
{
if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
if (strcmp(type, "[crop]")==0) return CROP;
if (strcmp(type, "[cost]")==0) return COST;
if (strcmp(type, "[detection]")==0) return DETECTION;
if (strcmp(type, "[region]")==0) return REGION;
if (strcmp(type, "[yolo]")==0) return YOLO;
if (strcmp(type, "[iseg]")==0) return ISEG;
if (strcmp(type, "[local]")==0) return LOCAL;
if (strcmp(type, "[conv]")==0
|| strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
if (strcmp(type, "[deconv]")==0
|| strcmp(type, "[deconvolutional]")==0) return DECONVOLUTIONAL;
if (strcmp(type, "[activation]")==0) return ACTIVE;
if (strcmp(type, "[logistic]")==0) return LOGXENT;
if (strcmp(type, "[l2norm]")==0) return L2NORM;
if (strcmp(type, "[net]")==0
|| strcmp(type, "[network]")==0) return NETWORK;
if (strcmp(type, "[crnn]")==0) return CRNN;
if (strcmp(type, "[gru]")==0) return GRU;
if (strcmp(type, "[lstm]") == 0) return LSTM;
if (strcmp(type, "[rnn]")==0) return RNN;
if (strcmp(type, "[conn]")==0
|| strcmp(type, "[connected]")==0) return CONNECTED;
if (strcmp(type, "[max]")==0
|| strcmp(type, "[maxpool]")==0) return MAXPOOL;
if (strcmp(type, "[reorg]")==0) return REORG;
if (strcmp(type, "[avg]")==0
|| strcmp(type, "[avgpool]")==0) return AVGPOOL;
if (strcmp(type, "[dropout]")==0) return DROPOUT;
if (strcmp(type, "[lrn]")==0
|| strcmp(type, "[normalization]")==0) return NORMALIZATION;
if (strcmp(type, "[batchnorm]")==0) return BATCHNORM;
if (strcmp(type, "[soft]")==0
|| strcmp(type, "[softmax]")==0) return SOFTMAX;
if (strcmp(type, "[route]")==0) return ROUTE;
if (strcmp(type, "[upsample]")==0) return UPSAMPLE;
return BLANK;
}
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;
int index;
int time_steps;
network *net;
} size_params;
local_layer parse_local(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 local layer must output image.");
local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation);
return layer;
}
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.");
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
int pad = option_find_int_quiet(options, "pad",0);
int padding = option_find_int_quiet(options, "padding",0);
if(pad) padding = size/2;
layer l = make_deconvolutional_layer(batch,h,w,c,n,size,stride,padding, activation, batch_normalize, params.net->adam);
return l;
}
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_quiet(options, "pad",0);
int padding = option_find_int_quiet(options, "padding",0);
int groups = option_find_int_quiet(options, "groups", 1);
if(pad) padding = size/2;
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.");
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
int binary = option_find_int_quiet(options, "binary", 0);
int xnor = option_find_int_quiet(options, "xnor", 0);
convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,groups,size,stride,padding,activation, batch_normalize, binary, xnor, params.net->adam);
layer.flipped = option_find_int_quiet(options, "flipped", 0);
layer.dot = option_find_float_quiet(options, "dot", 0);
return layer;
}
layer parse_crnn(list *options, size_params params)
{
int output_filters = option_find_int(options, "output_filters",1);
int hidden_filters = option_find_int(options, "hidden_filters",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, activation, batch_normalize);
l.shortcut = option_find_int_quiet(options, "shortcut", 0);
return l;
}
layer parse_rnn(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);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_rnn_layer(params.batch, params.inputs, output, params.time_steps, activation, batch_normalize, params.net->adam);
l.shortcut = option_find_int_quiet(options, "shortcut", 0);
return l;
}
layer parse_gru(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam);
l.tanh = option_find_int_quiet(options, "tanh", 0);
return l;
}
layer parse_lstm(list *options, size_params params)
{
int output = option_find_int(options, "output", 1);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam);
return l;
}
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);
int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
layer l = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize, params.net->adam);
return l;
}
layer parse_softmax(list *options, size_params params)
{
int groups = option_find_int_quiet(options, "groups",1);
layer l = make_softmax_layer(params.batch, params.inputs, groups);
l.temperature = option_find_float_quiet(options, "temperature", 1);
char *tree_file = option_find_str(options, "tree", 0);
if (tree_file) l.softmax_tree = read_tree(tree_file);
l.w = params.w;
l.h = params.h;
l.c = params.c;
l.spatial = option_find_float_quiet(options, "spatial", 0);
l.noloss = option_find_int_quiet(options, "noloss", 0);
return l;
}
int *parse_yolo_mask(char *a, int *num)
{
int *mask = 0;
if(a){
int len = strlen(a);
int n = 1;
int i;
for(i = 0; i < len; ++i){
if (a[i] == ',') ++n;
}
mask = calloc(n, sizeof(int));
for(i = 0; i < n; ++i){
int val = atoi(a);
mask[i] = val;
a = strchr(a, ',')+1;
}
*num = n;
}
return mask;
}
layer parse_yolo(list *options, size_params params)
{
int classes = option_find_int(options, "classes", 20);
int total = option_find_int(options, "num", 1);
int num = total;
char *a = option_find_str(options, "mask", 0);
int *mask = parse_yolo_mask(a, &num);
layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes);
assert(l.outputs == params.inputs);
l.max_boxes = option_find_int_quiet(options, "max",90);
l.jitter = option_find_float(options, "jitter", .2);
l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
l.truth_thresh = option_find_float(options, "truth_thresh", 1);
l.random = option_find_int_quiet(options, "random", 0);
char *map_file = option_find_str(options, "map", 0);
if (map_file) l.map = read_map(map_file);
a = option_find_str(options, "anchors", 0);
if(a){
int len = strlen(a);
int n = 1;
int i;
for(i = 0; i < len; ++i){
if (a[i] == ',') ++n;
}
for(i = 0; i < n; ++i){
float bias = atof(a);
l.biases[i] = bias;
a = strchr(a, ',')+1;
}
}
return l;
}
layer parse_iseg(list *options, size_params params)
{
int classes = option_find_int(options, "classes", 20);
int ids = option_find_int(options, "ids", 32);
layer l = make_iseg_layer(params.batch, params.w, params.h, classes, ids);
assert(l.outputs == params.inputs);
return l;
}
layer parse_region(list *options, size_params params)
{
int coords = option_find_int(options, "coords", 4);
int classes = option_find_int(options, "classes", 20);
int num = option_find_int(options, "num", 1);
layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords);
assert(l.outputs == params.inputs);
l.log = option_find_int_quiet(options, "log", 0);
l.sqrt = option_find_int_quiet(options, "sqrt", 0);
l.softmax = option_find_int(options, "softmax", 0);
l.background = option_find_int_quiet(options, "background", 0);
l.max_boxes = option_find_int_quiet(options, "max",30);
l.jitter = option_find_float(options, "jitter", .2);
l.rescore = option_find_int_quiet(options, "rescore",0);
l.thresh = option_find_float(options, "thresh", .5);
l.classfix = option_find_int_quiet(options, "classfix", 0);
l.absolute = option_find_int_quiet(options, "absolute", 0);
l.random = option_find_int_quiet(options, "random", 0);
l.coord_scale = option_find_float(options, "coord_scale", 1);
l.object_scale = option_find_float(options, "object_scale", 1);
l.noobject_scale = option_find_float(options, "noobject_scale", 1);
l.mask_scale = option_find_float(options, "mask_scale", 1);
l.class_scale = option_find_float(options, "class_scale", 1);
l.bias_match = option_find_int_quiet(options, "bias_match",0);
char *tree_file = option_find_str(options, "tree", 0);
if (tree_file) l.softmax_tree = read_tree(tree_file);
char *map_file = option_find_str(options, "map", 0);
if (map_file) l.map = read_map(map_file);
char *a = option_find_str(options, "anchors", 0);
if(a){
int len = strlen(a);
int n = 1;
int i;
for(i = 0; i < len; ++i){
if (a[i] == ',') ++n;
}
for(i = 0; i < n; ++i){
float bias = atof(a);
l.biases[i] = bias;
a = strchr(a, ',')+1;
}
}
return l;
}
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 num = option_find_int(options, "num", 1);
int side = option_find_int(options, "side", 7);
detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
layer.softmax = option_find_int(options, "softmax", 0);
layer.sqrt = option_find_int(options, "sqrt", 0);
layer.max_boxes = option_find_int_quiet(options, "max",90);
layer.coord_scale = option_find_float(options, "coord_scale", 1);
layer.forced = option_find_int(options, "forced", 0);
layer.object_scale = option_find_float(options, "object_scale", 1);
layer.noobject_scale = option_find_float(options, "noobject_scale", 1);
layer.class_scale = option_find_float(options, "class_scale", 1);
layer.jitter = option_find_float(options, "jitter", .2);
layer.random = option_find_int_quiet(options, "random", 0);
layer.reorg = option_find_int_quiet(options, "reorg", 0);
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);
float scale = option_find_float_quiet(options, "scale",1);
cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale);
layer.ratio = option_find_float_quiet(options, "ratio",0);
layer.noobject_scale = option_find_float_quiet(options, "noobj", 1);
layer.thresh = option_find_float_quiet(options, "thresh",0);
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.");
int noadjust = option_find_int_quiet(options, "noadjust",0);
crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
l.shift = option_find_float(options, "shift", 0);
l.noadjust = noadjust;
return l;
}
layer parse_reorg(list *options, size_params params)
{
int stride = option_find_int(options, "stride",1);
int reverse = option_find_int_quiet(options, "reverse",0);
int flatten = option_find_int_quiet(options, "flatten",0);
int extra = option_find_int_quiet(options, "extra",0);
int batch,h,w,c;
h = params.h;
w = params.w;
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before reorg layer must output image.");
layer layer = make_reorg_layer(batch,w,h,c,stride,reverse, flatten, extra);
return layer;
}
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 padding = option_find_int_quiet(options, "padding", size-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 maxpool layer must output image.");
maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride,padding);
return layer;
}
avgpool_layer parse_avgpool(list *options, size_params params)
{
int batch,w,h,c;
w = params.w;
h = params.h;
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before avgpool layer must output image.");
avgpool_layer layer = make_avgpool_layer(batch,w,h,c);
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);
layer.out_w = params.w;
layer.out_h = params.h;
layer.out_c = params.c;
return layer;
}
layer parse_normalization(list *options, size_params params)
{
float alpha = option_find_float(options, "alpha", .0001);
float beta = option_find_float(options, "beta" , .75);
float kappa = option_find_float(options, "kappa", 1);
int size = option_find_int(options, "size", 5);
layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa);
return l;
}
layer parse_batchnorm(list *options, size_params params)
{
layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c);
return l;
}
layer parse_shortcut(list *options, size_params params, network *net)
{
char *l = option_find(options, "from");
int index = atoi(l);
if(index < 0) index = params.index + index;
int batch = params.batch;
layer from = net->layers[index];
layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c);
char *activation_s = option_find_str(options, "activation", "linear");
ACTIVATION activation = get_activation(activation_s);
s.activation = activation;
s.alpha = option_find_float_quiet(options, "alpha", 1);
s.beta = option_find_float_quiet(options, "beta", 1);
return s;
}
layer parse_l2norm(list *options, size_params params)
{
layer l = make_l2norm_layer(params.batch, params.inputs);
l.h = l.out_h = params.h;
l.w = l.out_w = params.w;
l.c = l.out_c = params.c;
return l;
}
layer parse_logistic(list *options, size_params params)
{
layer l = make_logistic_layer(params.batch, params.inputs);
l.h = l.out_h = params.h;
l.w = l.out_w = params.w;
l.c = l.out_c = params.c;
return l;
}
layer parse_activation(list *options, size_params params)
{
char *activation_s = option_find_str(options, "activation", "linear");
ACTIVATION activation = get_activation(activation_s);
layer l = make_activation_layer(params.batch, params.inputs, activation);
l.h = l.out_h = params.h;
l.w = l.out_w = params.w;
l.c = l.out_c = params.c;
return l;
}
layer parse_upsample(list *options, size_params params, network *net)
{
int stride = option_find_int(options, "stride",2);
layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride);
l.scale = option_find_float_quiet(options, "scale", 1);
return l;
}
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;
if(index < 0) index = params.index + index;
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;
}
}
return layer;
}
learning_rate_policy get_policy(char *s)
{
if (strcmp(s, "random")==0) return RANDOM;
if (strcmp(s, "poly")==0) return POLY;
if (strcmp(s, "constant")==0) return CONSTANT;
if (strcmp(s, "step")==0) return STEP;
if (strcmp(s, "exp")==0) return EXP;
if (strcmp(s, "sigmoid")==0) return SIG;
if (strcmp(s, "steps")==0) return STEPS;
fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
return CONSTANT;
}
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);
int subdivs = option_find_int(options, "subdivisions",1);
net->time_steps = option_find_int_quiet(options, "time_steps",1);
net->notruth = option_find_int_quiet(options, "notruth",0);
net->batch /= subdivs;
net->batch *= net->time_steps;
net->subdivisions = subdivs;
net->random = option_find_int_quiet(options, "random", 0);
net->adam = option_find_int_quiet(options, "adam", 0);
if(net->adam){
net->B1 = option_find_float(options, "B1", .9);
net->B2 = option_find_float(options, "B2", .999);
net->eps = option_find_float(options, "eps", .0000001);
}
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);
net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2);
net->min_crop = option_find_int_quiet(options, "min_crop",net->w);
net->max_ratio = option_find_float_quiet(options, "max_ratio", (float) net->max_crop / net->w);
net->min_ratio = option_find_float_quiet(options, "min_ratio", (float) net->min_crop / net->w);
net->center = option_find_int_quiet(options, "center",0);
net->clip = option_find_float_quiet(options, "clip", 0);
net->angle = option_find_float_quiet(options, "angle", 0);
net->aspect = option_find_float_quiet(options, "aspect", 1);
net->saturation = option_find_float_quiet(options, "saturation", 1);
net->exposure = option_find_float_quiet(options, "exposure", 1);
net->hue = option_find_float_quiet(options, "hue", 0);
if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
char *policy_s = option_find_str(options, "policy", "constant");
net->policy = get_policy(policy_s);
net->burn_in = option_find_int_quiet(options, "burn_in", 0);
net->power = option_find_float_quiet(options, "power", 4);
if(net->policy == STEP){
net->step = option_find_int(options, "step", 1);
net->scale = option_find_float(options, "scale", 1);
} else if (net->policy == STEPS){
char *l = option_find(options, "steps");
char *p = option_find(options, "scales");
if(!l || !p) error("STEPS policy must have steps and scales in cfg file");
int len = strlen(l);
int n = 1;
int i;
for(i = 0; i < len; ++i){
if (l[i] == ',') ++n;
}
int *steps = calloc(n, sizeof(int));
float *scales = calloc(n, sizeof(float));
for(i = 0; i < n; ++i){
int step = atoi(l);
float scale = atof(p);
l = strchr(l, ',')+1;
p = strchr(p, ',')+1;
steps[i] = step;
scales[i] = scale;
}
net->scales = scales;
net->steps = steps;
net->num_steps = n;
} else if (net->policy == EXP){
net->gamma = option_find_float(options, "gamma", 1);
} else if (net->policy == SIG){
net->gamma = option_find_float(options, "gamma", 1);
net->step = option_find_int(options, "step", 1);
} else if (net->policy == POLY || net->policy == RANDOM){
}
net->max_batches = option_find_int(options, "max_batches", 0);
}
int is_network(section *s)
{
return (strcmp(s->type, "[net]")==0
|| strcmp(s->type, "[network]")==0);
}
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);
net->gpu_index = gpu_index;
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;
params.net = net;
size_t workspace_size = 0;
n = n->next;
int count = 0;
free_section(s);
fprintf(stderr, "layer filters size input output\n");
while(n){
params.index = count;
fprintf(stderr, "%5d ", count);
s = (section *)n->val;
options = s->options;
layer l = {0};
LAYER_TYPE lt = string_to_layer_type(s->type);
if(lt == CONVOLUTIONAL){
l = parse_convolutional(options, params);
}else if(lt == DECONVOLUTIONAL){
l = parse_deconvolutional(options, params);
}else if(lt == LOCAL){
l = parse_local(options, params);
}else if(lt == ACTIVE){
l = parse_activation(options, params);
}else if(lt == LOGXENT){
l = parse_logistic(options, params);
}else if(lt == L2NORM){
l = parse_l2norm(options, params);
}else if(lt == RNN){
l = parse_rnn(options, params);
}else if(lt == GRU){
l = parse_gru(options, params);
}else if (lt == LSTM) {
l = parse_lstm(options, params);
}else if(lt == CRNN){
l = parse_crnn(options, params);
}else if(lt == CONNECTED){
l = parse_connected(options, params);
}else if(lt == CROP){
l = parse_crop(options, params);
}else if(lt == COST){
l = parse_cost(options, params);
}else if(lt == REGION){
l = parse_region(options, params);
}else if(lt == YOLO){
l = parse_yolo(options, params);
}else if(lt == ISEG){
l = parse_iseg(options, params);
}else if(lt == DETECTION){
l = parse_detection(options, params);
}else if(lt == SOFTMAX){
l = parse_softmax(options, params);
net->hierarchy = l.softmax_tree;
}else if(lt == NORMALIZATION){
l = parse_normalization(options, params);
}else if(lt == BATCHNORM){
l = parse_batchnorm(options, params);
}else if(lt == MAXPOOL){
l = parse_maxpool(options, params);
}else if(lt == REORG){
l = parse_reorg(options, params);
}else if(lt == AVGPOOL){
l = parse_avgpool(options, params);
}else if(lt == ROUTE){
l = parse_route(options, params, net);
}else if(lt == UPSAMPLE){
l = parse_upsample(options, params, net);
}else if(lt == SHORTCUT){
l = parse_shortcut(options, params, net);
}else if(lt == DROPOUT){
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.clip = net->clip;
l.truth = option_find_int_quiet(options, "truth", 0);
l.onlyforward = option_find_int_quiet(options, "onlyforward", 0);
l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
l.dontsave = option_find_int_quiet(options, "dontsave", 0);
l.dontload = option_find_int_quiet(options, "dontload", 0);
l.numload = option_find_int_quiet(options, "numload", 0);
l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1);
l.smooth = option_find_float_quiet(options, "smooth", 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);
layer out = get_network_output_layer(net);
net->outputs = out.outputs;
net->truths = out.outputs;
if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths;
net->output = out.output;
net->input = calloc(net->inputs*net->batch, sizeof(float));
net->truth = calloc(net->truths*net->batch, sizeof(float));
#ifdef GPU
net->output_gpu = out.output_gpu;
net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch);
net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch);
#endif
if(workspace_size){
//printf("%ld\n", workspace_size);
#ifdef GPU
if(gpu_index >= 0){
net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
}else {
net->workspace = calloc(1, workspace_size);
}
#else
net->workspace = calloc(1, workspace_size);
#endif
}
return net;
}
list *read_cfg(char *filename)
{
FILE *file = fopen(filename, "r");
if(file == 0) file_error(filename);
char *line;
int nu = 0;
list *options = make_list();
section *current = 0;
while((line=fgetl(file)) != 0){
++ nu;
strip(line);
switch(line[0]){
case '[':
current = malloc(sizeof(section));
list_insert(options, 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 options;
}
void save_convolutional_weights_binary(layer l, FILE *fp)
{
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(l);
}
#endif
binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
int size = l.c*l.size*l.size;
int i, j, k;
fwrite(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize){
fwrite(l.scales, sizeof(float), l.n, fp);
fwrite(l.rolling_mean, sizeof(float), l.n, fp);
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
for(i = 0; i < l.n; ++i){
float mean = l.binary_weights[i*size];
if(mean < 0) mean = -mean;
fwrite(&mean, sizeof(float), 1, fp);
for(j = 0; j < size/8; ++j){
int index = i*size + j*8;
unsigned char c = 0;
for(k = 0; k < 8; ++k){
if (j*8 + k >= size) break;
if (l.binary_weights[index + k] > 0) c = (c | 1<<k);
}
fwrite(&c, sizeof(char), 1, fp);
}
}
}
void save_convolutional_weights(layer l, FILE *fp)
{
if(l.binary){
//save_convolutional_weights_binary(l, fp);
//return;
}
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(l);
}
#endif
int num = l.nweights;
fwrite(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize){
fwrite(l.scales, sizeof(float), l.n, fp);
fwrite(l.rolling_mean, sizeof(float), l.n, fp);
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
fwrite(l.weights, sizeof(float), num, fp);
}
void save_batchnorm_weights(layer l, FILE *fp)
{
#ifdef GPU
if(gpu_index >= 0){
pull_batchnorm_layer(l);
}
#endif
fwrite(l.scales, sizeof(float), l.c, fp);
fwrite(l.rolling_mean, sizeof(float), l.c, fp);
fwrite(l.rolling_variance, sizeof(float), l.c, fp);
}
void save_connected_weights(layer l, FILE *fp)
{
#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);
if (l.batch_normalize){
fwrite(l.scales, sizeof(float), l.outputs, fp);
fwrite(l.rolling_mean, sizeof(float), l.outputs, fp);
fwrite(l.rolling_variance, sizeof(float), l.outputs, fp);
}
}
void save_weights_upto(network *net, char *filename, int cutoff)
{
#ifdef GPU
if(net->gpu_index >= 0){
cuda_set_device(net->gpu_index);
}
#endif
fprintf(stderr, "Saving weights to %s\n", filename);
FILE *fp = fopen(filename, "wb");
if(!fp) file_error(filename);
int major = 0;
int minor = 2;
int revision = 0;
fwrite(&major, sizeof(int), 1, fp);
fwrite(&minor, sizeof(int), 1, fp);
fwrite(&revision, sizeof(int), 1, fp);
fwrite(net->seen, sizeof(size_t), 1, fp);
int i;
for(i = 0; i < net->n && i < cutoff; ++i){
layer l = net->layers[i];
if (l.dontsave) continue;
if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
save_convolutional_weights(l, fp);
} if(l.type == CONNECTED){
save_connected_weights(l, fp);
} if(l.type == BATCHNORM){
save_batchnorm_weights(l, fp);
} if(l.type == RNN){
save_connected_weights(*(l.input_layer), fp);
save_connected_weights(*(l.self_layer), fp);
save_connected_weights(*(l.output_layer), fp);
} if (l.type == LSTM) {
save_connected_weights(*(l.wi), fp);
save_connected_weights(*(l.wf), fp);
save_connected_weights(*(l.wo), fp);
save_connected_weights(*(l.wg), fp);
save_connected_weights(*(l.ui), fp);
save_connected_weights(*(l.uf), fp);
save_connected_weights(*(l.uo), fp);
save_connected_weights(*(l.ug), fp);
} if (l.type == GRU) {
if(1){
save_connected_weights(*(l.wz), fp);
save_connected_weights(*(l.wr), fp);
save_connected_weights(*(l.wh), fp);
save_connected_weights(*(l.uz), fp);
save_connected_weights(*(l.ur), fp);
save_connected_weights(*(l.uh), fp);
}else{
save_connected_weights(*(l.reset_layer), fp);
save_connected_weights(*(l.update_layer), fp);
save_connected_weights(*(l.state_layer), fp);
}
} if(l.type == CRNN){
save_convolutional_weights(*(l.input_layer), fp);
save_convolutional_weights(*(l.self_layer), fp);
save_convolutional_weights(*(l.output_layer), fp);
} if(l.type == LOCAL){
#ifdef GPU
if(gpu_index >= 0){
pull_local_layer(l);
}
#endif
int locations = l.out_w*l.out_h;
int size = l.size*l.size*l.c*l.n*locations;
fwrite(l.biases, sizeof(float), l.outputs, fp);
fwrite(l.weights, sizeof(float), size, fp);
}
}
fclose(fp);
}
void save_weights(network *net, char *filename)
{
save_weights_upto(net, filename, net->n);
}
void transpose_matrix(float *a, int rows, int cols)
{
float *transpose = calloc(rows*cols, sizeof(float));
int x, y;
for(x = 0; x < rows; ++x){
for(y = 0; y < cols; ++y){
transpose[y*rows + x] = a[x*cols + y];
}
}
memcpy(a, transpose, rows*cols*sizeof(float));
free(transpose);
}
void load_connected_weights(layer l, FILE *fp, int transpose)
{
fread(l.biases, sizeof(float), l.outputs, fp);
fread(l.weights, sizeof(float), l.outputs*l.inputs, fp);
if(transpose){
transpose_matrix(l.weights, l.inputs, l.outputs);
}
//printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
//printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.outputs, fp);
fread(l.rolling_mean, sizeof(float), l.outputs, fp);
fread(l.rolling_variance, sizeof(float), l.outputs, fp);
//printf("Scales: %f mean %f variance\n", mean_array(l.scales, l.outputs), variance_array(l.scales, l.outputs));
//printf("rolling_mean: %f mean %f variance\n", mean_array(l.rolling_mean, l.outputs), variance_array(l.rolling_mean, l.outputs));
//printf("rolling_variance: %f mean %f variance\n", mean_array(l.rolling_variance, l.outputs), variance_array(l.rolling_variance, l.outputs));
}
#ifdef GPU
if(gpu_index >= 0){
push_connected_layer(l);
}
#endif
}
void load_batchnorm_weights(layer l, FILE *fp)
{
fread(l.scales, sizeof(float), l.c, fp);
fread(l.rolling_mean, sizeof(float), l.c, fp);
fread(l.rolling_variance, sizeof(float), l.c, fp);
#ifdef GPU
if(gpu_index >= 0){
push_batchnorm_layer(l);
}
#endif
}
void load_convolutional_weights_binary(layer l, FILE *fp)
{
fread(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.n, fp);
fread(l.rolling_mean, sizeof(float), l.n, fp);
fread(l.rolling_variance, sizeof(float), l.n, fp);
}
int size = l.c*l.size*l.size;
int i, j, k;
for(i = 0; i < l.n; ++i){
float mean = 0;
fread(&mean, sizeof(float), 1, fp);
for(j = 0; j < size/8; ++j){
int index = i*size + j*8;
unsigned char c = 0;
fread(&c, sizeof(char), 1, fp);
for(k = 0; k < 8; ++k){
if (j*8 + k >= size) break;
l.weights[index + k] = (c & 1<<k) ? mean : -mean;
}
}
}
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(l);
}
#endif
}
void load_convolutional_weights(layer l, FILE *fp)
{
if(l.binary){
//load_convolutional_weights_binary(l, fp);
//return;
}
if(l.numload) l.n = l.numload;
int num = l.c/l.groups*l.n*l.size*l.size;
fread(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.n, fp);
fread(l.rolling_mean, sizeof(float), l.n, fp);
fread(l.rolling_variance, sizeof(float), l.n, fp);
if(0){
int i;
for(i = 0; i < l.n; ++i){
printf("%g, ", l.rolling_mean[i]);
}
printf("\n");
for(i = 0; i < l.n; ++i){
printf("%g, ", l.rolling_variance[i]);
}
printf("\n");
}
if(0){
fill_cpu(l.n, 0, l.rolling_mean, 1);
fill_cpu(l.n, 0, l.rolling_variance, 1);
}
if(0){
int i;
for(i = 0; i < l.n; ++i){
printf("%g, ", l.rolling_mean[i]);
}
printf("\n");
for(i = 0; i < l.n; ++i){
printf("%g, ", l.rolling_variance[i]);
}
printf("\n");
}
}
fread(l.weights, sizeof(float), num, fp);
//if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1);
if (l.flipped) {
transpose_matrix(l.weights, l.c*l.size*l.size, l.n);
}
//if (l.binary) binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.weights);
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(l);
}
#endif
}
void load_weights_upto(network *net, char *filename, int start, int cutoff)
{
#ifdef GPU
if(net->gpu_index >= 0){
cuda_set_device(net->gpu_index);
}
#endif
fprintf(stderr, "Loading weights from %s...", filename);
fflush(stdout);
FILE *fp = fopen(filename, "rb");
if(!fp) file_error(filename);
int major;
int minor;
int revision;
fread(&major, sizeof(int), 1, fp);
fread(&minor, sizeof(int), 1, fp);
fread(&revision, sizeof(int), 1, fp);
if ((major*10 + minor) >= 2 && major < 1000 && minor < 1000){
fread(net->seen, sizeof(size_t), 1, fp);
} else {
int iseen = 0;
fread(&iseen, sizeof(int), 1, fp);
*net->seen = iseen;
}
int transpose = (major > 1000) || (minor > 1000);
int i;
for(i = start; i < net->n && i < cutoff; ++i){
layer l = net->layers[i];
if (l.dontload) continue;
if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
load_convolutional_weights(l, fp);
}
if(l.type == CONNECTED){
load_connected_weights(l, fp, transpose);
}
if(l.type == BATCHNORM){
load_batchnorm_weights(l, fp);
}
if(l.type == CRNN){
load_convolutional_weights(*(l.input_layer), fp);
load_convolutional_weights(*(l.self_layer), fp);
load_convolutional_weights(*(l.output_layer), fp);
}
if(l.type == RNN){
load_connected_weights(*(l.input_layer), fp, transpose);
load_connected_weights(*(l.self_layer), fp, transpose);
load_connected_weights(*(l.output_layer), fp, transpose);
}
if (l.type == LSTM) {
load_connected_weights(*(l.wi), fp, transpose);
load_connected_weights(*(l.wf), fp, transpose);
load_connected_weights(*(l.wo), fp, transpose);
load_connected_weights(*(l.wg), fp, transpose);
load_connected_weights(*(l.ui), fp, transpose);
load_connected_weights(*(l.uf), fp, transpose);
load_connected_weights(*(l.uo), fp, transpose);
load_connected_weights(*(l.ug), fp, transpose);
}
if (l.type == GRU) {
if(1){
load_connected_weights(*(l.wz), fp, transpose);
load_connected_weights(*(l.wr), fp, transpose);
load_connected_weights(*(l.wh), fp, transpose);
load_connected_weights(*(l.uz), fp, transpose);
load_connected_weights(*(l.ur), fp, transpose);
load_connected_weights(*(l.uh), fp, transpose);
}else{
load_connected_weights(*(l.reset_layer), fp, transpose);
load_connected_weights(*(l.update_layer), fp, transpose);
load_connected_weights(*(l.state_layer), fp, transpose);
}
}
if(l.type == LOCAL){
int locations = l.out_w*l.out_h;
int size = l.size*l.size*l.c*l.n*locations;
fread(l.biases, sizeof(float), l.outputs, fp);
fread(l.weights, sizeof(float), size, fp);
#ifdef GPU
if(gpu_index >= 0){
push_local_layer(l);
}
#endif
}
}
fprintf(stderr, "Done!\n");
fclose(fp);
}
void load_weights(network *net, char *filename)
{
load_weights_upto(net, filename, 0, net->n);
}
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