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refactoring and added DARK ZONE

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This commit is contained in:
Joseph Redmon
2015-03-11 22:20:15 -07:00
parent f047cfff99
commit dcb000b553
37 changed files with 640 additions and 918 deletions
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474
src/parser.c
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@ -14,7 +14,6 @@
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "detection_layer.h"
#include "freeweight_layer.h"
#include "list.h"
#include "option_list.h"
#include "utils.h"
@ -24,12 +23,12 @@ typedef struct{
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_freeweight(section *s);
int is_softmax(section *s);
int is_crop(section *s);
int is_cost(section *s);
@ -69,38 +68,31 @@ void parse_data(char *data, float *a, int n)
}
}
deconvolutional_layer *parse_deconvolutional(list *options, network *net, int count)
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 h,w,c;
float learning_rate, momentum, decay;
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);
if(count == 0){
learning_rate = option_find_float(options, "learning_rate", .001);
momentum = option_find_float(options, "momentum", .9);
decay = option_find_float(options, "decay", .0001);
h = option_find_int(options, "height",1);
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
net->batch = option_find_int(options, "batch",1);
net->learning_rate = learning_rate;
net->momentum = momentum;
net->decay = decay;
net->seen = option_find_int(options, "seen",0);
}else{
learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
momentum = option_find_float_quiet(options, "momentum", net->momentum);
decay = option_find_float_quiet(options, "decay", net->decay);
image m = get_network_image_layer(*net, count-1);
h = m.h;
w = m.w;
c = m.c;
if(h == 0) error("Layer before deconvolutional layer must output image.");
}
deconvolutional_layer *layer = make_deconvolutional_layer(net->batch,h,w,c,n,size,stride,activation,learning_rate,momentum,decay);
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);
@ -112,39 +104,24 @@ deconvolutional_layer *parse_deconvolutional(list *options, network *net, int co
return layer;
}
convolutional_layer *parse_convolutional(list *options, network *net, int count)
convolutional_layer *parse_convolutional(list *options, size_params params)
{
int h,w,c;
float learning_rate, momentum, decay;
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);
if(count == 0){
learning_rate = option_find_float(options, "learning_rate", .001);
momentum = option_find_float(options, "momentum", .9);
decay = option_find_float(options, "decay", .0001);
h = option_find_int(options, "height",1);
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
net->batch = option_find_int(options, "batch",1);
net->learning_rate = learning_rate;
net->momentum = momentum;
net->decay = decay;
net->seen = option_find_int(options, "seen",0);
}else{
learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
momentum = option_find_float_quiet(options, "momentum", net->momentum);
decay = option_find_float_quiet(options, "decay", net->decay);
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(net->batch,h,w,c,n,size,stride,pad,activation,learning_rate,momentum,decay);
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);
@ -156,33 +133,18 @@ convolutional_layer *parse_convolutional(list *options, network *net, int count)
return layer;
}
connected_layer *parse_connected(list *options, network *net, int count)
connected_layer *parse_connected(list *options, size_params params)
{
int input;
float learning_rate, momentum, decay;
int output = option_find_int(options, "output",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
if(count == 0){
input = option_find_int(options, "input",1);
net->batch = option_find_int(options, "batch",1);
learning_rate = option_find_float(options, "learning_rate", .001);
momentum = option_find_float(options, "momentum", .9);
decay = option_find_float(options, "decay", .0001);
net->learning_rate = learning_rate;
net->momentum = momentum;
net->decay = decay;
}else{
learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
momentum = option_find_float_quiet(options, "momentum", net->momentum);
decay = option_find_float_quiet(options, "decay", net->decay);
input = get_network_output_size_layer(*net, count-1);
}
connected_layer *layer = make_connected_layer(net->batch, input, output, activation,learning_rate,momentum,decay);
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, input*output);
parse_data(weights, layer->weights, params.inputs*output);
#ifdef GPU
if(weights || biases) push_connected_layer(*layer);
#endif
@ -190,235 +152,188 @@ connected_layer *parse_connected(list *options, network *net, int count)
return layer;
}
softmax_layer *parse_softmax(list *options, network *net, int count)
softmax_layer *parse_softmax(list *options, size_params params)
{
int input;
int groups = option_find_int(options, "groups",1);
if(count == 0){
input = option_find_int(options, "input",1);
net->batch = option_find_int(options, "batch",1);
net->seen = option_find_int(options, "seen",0);
}else{
input = get_network_output_size_layer(*net, count-1);
}
softmax_layer *layer = make_softmax_layer(net->batch, groups, input);
softmax_layer *layer = make_softmax_layer(params.batch, params.inputs, groups);
option_unused(options);
return layer;
}
detection_layer *parse_detection(list *options, network *net, int count)
detection_layer *parse_detection(list *options, size_params params)
{
int input;
if(count == 0){
input = option_find_int(options, "input",1);
net->batch = option_find_int(options, "batch",1);
net->seen = option_find_int(options, "seen",0);
}else{
input = get_network_output_size_layer(*net, count-1);
}
int coords = option_find_int(options, "coords", 1);
int classes = option_find_int(options, "classes", 1);
int rescore = option_find_int(options, "rescore", 1);
detection_layer *layer = make_detection_layer(net->batch, input, classes, coords, rescore);
detection_layer *layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore);
option_unused(options);
return layer;
}
cost_layer *parse_cost(list *options, network *net, int count)
cost_layer *parse_cost(list *options, size_params params)
{
int input;
if(count == 0){
input = option_find_int(options, "input",1);
net->batch = option_find_int(options, "batch",1);
net->seen = option_find_int(options, "seen",0);
}else{
input = get_network_output_size_layer(*net, count-1);
}
char *type_s = option_find_str(options, "type", "sse");
COST_TYPE type = get_cost_type(type_s);
cost_layer *layer = make_cost_layer(net->batch, input, type);
cost_layer *layer = make_cost_layer(params.batch, params.inputs, type);
option_unused(options);
return layer;
}
crop_layer *parse_crop(list *options, network *net, int count)
crop_layer *parse_crop(list *options, size_params params)
{
float learning_rate, momentum, decay;
int h,w,c;
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);
if(count == 0){
h = option_find_int(options, "height",1);
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
net->batch = option_find_int(options, "batch",1);
learning_rate = option_find_float(options, "learning_rate", .001);
momentum = option_find_float(options, "momentum", .9);
decay = option_find_float(options, "decay", .0001);
net->learning_rate = learning_rate;
net->momentum = momentum;
net->decay = decay;
net->seen = option_find_int(options, "seen",0);
}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 crop layer must output image.");
}
crop_layer *layer = make_crop_layer(net->batch,h,w,c,crop_height,crop_width,flip);
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 *layer = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip);
option_unused(options);
return layer;
}
maxpool_layer *parse_maxpool(list *options, network *net, int count)
maxpool_layer *parse_maxpool(list *options, size_params params)
{
int h,w,c;
int stride = option_find_int(options, "stride",1);
int size = option_find_int(options, "size",stride);
if(count == 0){
h = option_find_int(options, "height",1);
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
net->batch = option_find_int(options, "batch",1);
net->seen = option_find_int(options, "seen",0);
}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(net->batch,h,w,c,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;
}
/*
freeweight_layer *parse_freeweight(list *options, network *net, int count)
dropout_layer *parse_dropout(list *options, size_params params)
{
int input;
if(count == 0){
net->batch = option_find_int(options, "batch",1);
input = option_find_int(options, "input",1);
}else{
input = get_network_output_size_layer(*net, count-1);
}
freeweight_layer *layer = make_freeweight_layer(net->batch,input);
option_unused(options);
return layer;
}
*/
dropout_layer *parse_dropout(list *options, network *net, int count)
{
int input;
float probability = option_find_float(options, "probability", .5);
if(count == 0){
net->batch = option_find_int(options, "batch",1);
input = option_find_int(options, "input",1);
float learning_rate = option_find_float(options, "learning_rate", .001);
float momentum = option_find_float(options, "momentum", .9);
float decay = option_find_float(options, "decay", .0001);
net->learning_rate = learning_rate;
net->momentum = momentum;
net->decay = decay;
net->seen = option_find_int(options, "seen",0);
}else{
input = get_network_output_size_layer(*net, count-1);
}
dropout_layer *layer = make_dropout_layer(net->batch,input,probability);
dropout_layer *layer = make_dropout_layer(params.batch, params.inputs, probability);
option_unused(options);
return layer;
}
normalization_layer *parse_normalization(list *options, network *net, int count)
normalization_layer *parse_normalization(list *options, size_params params)
{
int h,w,c;
int size = option_find_int(options, "size",1);
float alpha = option_find_float(options, "alpha", 0.);
float beta = option_find_float(options, "beta", 1.);
float kappa = option_find_float(options, "kappa", 1.);
if(count == 0){
h = option_find_int(options, "height",1);
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
net->batch = option_find_int(options, "batch",1);
net->seen = option_find_int(options, "seen",0);
}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.");
}
normalization_layer *layer = make_normalization_layer(net->batch,h,w,c,size, alpha, beta, kappa);
int batch,h,w,c;
h = params.h;
w = params.w;
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before normalization layer must output image.");
normalization_layer *layer = make_normalization_layer(batch,h,w,c,size, alpha, beta, kappa);
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);
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");
}
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
network net = make_network(sections->size, 0);
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){
section *s = (section *)n->val;
list *options = s->options;
fprintf(stderr, "%d: ", count);
s = (section *)n->val;
options = s->options;
if(is_convolutional(s)){
convolutional_layer *layer = parse_convolutional(options, &net, count);
convolutional_layer *layer = parse_convolutional(options, params);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
}else if(is_deconvolutional(s)){
deconvolutional_layer *layer = parse_deconvolutional(options, &net, count);
deconvolutional_layer *layer = parse_deconvolutional(options, params);
net.types[count] = DECONVOLUTIONAL;
net.layers[count] = layer;
}else if(is_connected(s)){
connected_layer *layer = parse_connected(options, &net, count);
connected_layer *layer = parse_connected(options, params);
net.types[count] = CONNECTED;
net.layers[count] = layer;
}else if(is_crop(s)){
crop_layer *layer = parse_crop(options, &net, count);
crop_layer *layer = parse_crop(options, params);
net.types[count] = CROP;
net.layers[count] = layer;
}else if(is_cost(s)){
cost_layer *layer = parse_cost(options, &net, count);
cost_layer *layer = parse_cost(options, params);
net.types[count] = COST;
net.layers[count] = layer;
}else if(is_detection(s)){
detection_layer *layer = parse_detection(options, &net, count);
detection_layer *layer = parse_detection(options, params);
net.types[count] = DETECTION;
net.layers[count] = layer;
}else if(is_softmax(s)){
softmax_layer *layer = parse_softmax(options, &net, count);
softmax_layer *layer = parse_softmax(options, params);
net.types[count] = SOFTMAX;
net.layers[count] = layer;
}else if(is_maxpool(s)){
maxpool_layer *layer = parse_maxpool(options, &net, count);
maxpool_layer *layer = parse_maxpool(options, params);
net.types[count] = MAXPOOL;
net.layers[count] = layer;
}else if(is_normalization(s)){
normalization_layer *layer = parse_normalization(options, &net, count);
normalization_layer *layer = parse_normalization(options, params);
net.types[count] = NORMALIZATION;
net.layers[count] = layer;
}else if(is_dropout(s)){
dropout_layer *layer = parse_dropout(options, &net, count);
dropout_layer *layer = parse_dropout(options, params);
net.types[count] = DROPOUT;
net.layers[count] = layer;
}else if(is_freeweight(s)){
//freeweight_layer *layer = parse_freeweight(options, &net, count);
//net.types[count] = FREEWEIGHT;
//net.layers[count] = layer;
fprintf(stderr, "Type not recognized: %s\n", s->type);
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
free_section(s);
++count;
n = n->next;
if(n){
image im = get_network_image_layer(net, count);
params.h = im.h;
params.w = im.w;
params.c = im.c;
params.inputs = get_network_output_size_layer(net, count);
}
++count;
}
free_list(sections);
net.outputs = get_network_output_size(net);
@ -448,6 +363,11 @@ 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
@ -462,10 +382,6 @@ int is_dropout(section *s)
{
return (strcmp(s->type, "[dropout]")==0);
}
int is_freeweight(section *s)
{
return (strcmp(s->type, "[freeweight]")==0);
}
int is_softmax(section *s)
{
@ -533,29 +449,11 @@ list *read_cfg(char *filename)
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
{
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0) pull_convolutional_layer(*l);
#endif
#endif
int i;
fprintf(fp, "[convolutional]\n");
if(count == 0) {
fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n"
"learning_rate=%g\n"
"momentum=%g\n"
"decay=%g\n"
"seen=%d\n",
l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay, net.seen);
} else {
if(l->learning_rate != net.learning_rate)
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
if(l->momentum != net.momentum)
fprintf(fp, "momentum=%g\n", l->momentum);
if(l->decay != net.decay)
fprintf(fp, "decay=%g\n", l->decay);
}
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
@ -573,29 +471,11 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int
void print_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net, int count)
{
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0) pull_deconvolutional_layer(*l);
#endif
#endif
int i;
fprintf(fp, "[deconvolutional]\n");
if(count == 0) {
fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n"
"learning_rate=%g\n"
"momentum=%g\n"
"decay=%g\n"
"seen=%d\n",
l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay, net.seen);
} else {
if(l->learning_rate != net.learning_rate)
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
if(l->momentum != net.momentum)
fprintf(fp, "momentum=%g\n", l->momentum);
if(l->decay != net.decay)
fprintf(fp, "decay=%g\n", l->decay);
}
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
@ -610,47 +490,19 @@ void print_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net,
fprintf(fp, "\n\n");
}
void print_freeweight_cfg(FILE *fp, freeweight_layer *l, network net, int count)
{
fprintf(fp, "[freeweight]\n");
if(count == 0){
fprintf(fp, "batch=%d\ninput=%d\n",l->batch, l->inputs);
}
fprintf(fp, "\n");
}
void print_dropout_cfg(FILE *fp, dropout_layer *l, network net, int count)
{
fprintf(fp, "[dropout]\n");
if(count == 0){
fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
}
fprintf(fp, "probability=%g\n\n", l->probability);
}
void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
{
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0) pull_connected_layer(*l);
#endif
#endif
int i;
fprintf(fp, "[connected]\n");
if(count == 0){
fprintf(fp, "batch=%d\n"
"input=%d\n"
"learning_rate=%g\n"
"momentum=%g\n"
"decay=%g\n"
"seen=%d\n",
l->batch, l->inputs, l->learning_rate, l->momentum, l->decay, net.seen);
} else {
if(l->learning_rate != net.learning_rate)
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
if(l->momentum != net.momentum)
fprintf(fp, "momentum=%g\n", l->momentum);
if(l->decay != net.decay)
fprintf(fp, "decay=%g\n", l->decay);
}
fprintf(fp, "output=%d\n"
"activation=%s\n",
l->outputs,
@ -666,39 +518,18 @@ void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count)
{
fprintf(fp, "[crop]\n");
if(count == 0) {
fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n"
"learning_rate=%g\n"
"momentum=%g\n"
"decay=%g\n"
"seen=%d\n",
l->batch,l->h, l->w, l->c, net.learning_rate, net.momentum, net.decay, net.seen);
}
fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip);
}
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, network net, int count)
{
fprintf(fp, "[maxpool]\n");
if(count == 0) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->batch,l->h, l->w, l->c);
fprintf(fp, "size=%d\nstride=%d\n\n", l->size, l->stride);
}
void print_normalization_cfg(FILE *fp, normalization_layer *l, network net, int count)
{
fprintf(fp, "[localresponsenormalization]\n");
if(count == 0) fprintf(fp, "batch=%d\n"
"height=%d\n"
"width=%d\n"
"channels=%d\n",
l->batch,l->h, l->w, l->c);
fprintf(fp, "size=%d\n"
"alpha=%g\n"
"beta=%g\n"
@ -708,7 +539,6 @@ void print_normalization_cfg(FILE *fp, normalization_layer *l, network net, int
void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
{
fprintf(fp, "[softmax]\n");
if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}
@ -722,7 +552,6 @@ void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count)
void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
{
fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}
@ -741,33 +570,33 @@ void save_weights(network net, char *filename)
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net.layers[i];
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(layer);
}
#endif
#endif
int num = layer.n*layer.c*layer.size*layer.size;
fwrite(layer.biases, sizeof(float), layer.n, fp);
fwrite(layer.filters, sizeof(float), num, fp);
}
if(net.types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *) net.layers[i];
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0){
pull_deconvolutional_layer(layer);
}
#endif
#endif
int num = layer.n*layer.c*layer.size*layer.size;
fwrite(layer.biases, sizeof(float), layer.n, fp);
fwrite(layer.filters, sizeof(float), num, fp);
}
if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net.layers[i];
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0){
pull_connected_layer(layer);
}
#endif
#endif
fwrite(layer.biases, sizeof(float), layer.outputs, fp);
fwrite(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
}
@ -785,8 +614,7 @@ void load_weights_upto(network *net, char *filename, int cutoff)
fread(&net->momentum, sizeof(float), 1, fp);
fread(&net->decay, sizeof(float), 1, fp);
fread(&net->seen, sizeof(int), 1, fp);
set_learning_network(net, net->learning_rate, net->momentum, net->decay);
int i;
for(i = 0; i < net->n && i < cutoff; ++i){
if(net->types[i] == CONVOLUTIONAL){
@ -794,32 +622,32 @@ void load_weights_upto(network *net, char *filename, int cutoff)
int num = layer.n*layer.c*layer.size*layer.size;
fread(layer.biases, sizeof(float), layer.n, fp);
fread(layer.filters, sizeof(float), num, fp);
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(layer);
}
#endif
#endif
}
if(net->types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *) net->layers[i];
int num = layer.n*layer.c*layer.size*layer.size;
fread(layer.biases, sizeof(float), layer.n, fp);
fread(layer.filters, sizeof(float), num, fp);
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0){
push_deconvolutional_layer(layer);
}
#endif
#endif
}
if(net->types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net->layers[i];
fread(layer.biases, sizeof(float), layer.outputs, fp);
fread(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
#ifdef GPU
#ifdef GPU
if(gpu_index >= 0){
push_connected_layer(layer);
}
#endif
#endif
}
}
fclose(fp);
@ -847,8 +675,6 @@ void save_network(network net, char *filename)
print_crop_cfg(fp, (crop_layer *)net.layers[i], net, i);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], net, i);
else if(net.types[i] == FREEWEIGHT)
print_freeweight_cfg(fp, (freeweight_layer *)net.layers[i], net, i);
else if(net.types[i] == DROPOUT)
print_dropout_cfg(fp, (dropout_layer *)net.layers[i], net, i);
else if(net.types[i] == NORMALIZATION)