From f047cfff99e00e28c02eb59b6d32386c122f9af6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 08 Mar 2015 18:31:12 +0000
Subject: [PATCH] renamed sigmoid to logistic
---
src/parser.c | 697 ++++++++++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 626 insertions(+), 71 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index b008882..7b1057e 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -4,11 +4,17 @@
#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 "normalization_layer.h"
#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"
@@ -19,9 +25,15 @@
}section;
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);
+int is_detection(section *s);
int is_normalization(section *s);
list *read_cfg(char *filename);
@@ -41,122 +53,287 @@
free(s);
}
-convolutional_layer *parse_convolutional(list *options, network net, int count)
+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;
+ }
+}
+
+deconvolutional_layer *parse_deconvolutional(list *options, network *net, int count)
+{
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);
+ 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, network *net, int count)
+{
+ 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", "sigmoid");
+ 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->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{
- image m = get_network_image_layer(net, count-1);
+ 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);
- char *data = option_find_str(options, "data", 0);
- if(data){
- char *curr = data;
- char *next = data;
- for(i = 0; i < n; ++i){
- while(*++next !='\0' && *next != ',');
- *next = '\0';
- sscanf(curr, "%g", &layer->biases[i]);
- curr = next+1;
- }
- for(i = 0; i < c*n*size*size; ++i){
- while(*++next !='\0' && *next != ',');
- *next = '\0';
- sscanf(curr, "%g", &layer->filters[i]);
- curr = next+1;
- }
- }
+ convolutional_layer *layer = make_convolutional_layer(net->batch,h,w,c,n,size,stride,pad,activation,learning_rate,momentum,decay);
+ 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, network net, int count)
+connected_layer *parse_connected(list *options, network *net, int count)
{
- int i;
int input;
+ float learning_rate, momentum, decay;
int output = option_find_int(options, "output",1);
- float dropout = option_find_float(options, "dropout", 0.);
- char *activation_s = option_find_str(options, "activation", "sigmoid");
+ 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);
+ 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{
- input = get_network_output_size_layer(net, count-1);
+ 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, dropout, activation);
- char *data = option_find_str(options, "data", 0);
- if(data){
- char *curr = data;
- char *next = data;
- for(i = 0; i < output; ++i){
- while(*++next !='\0' && *next != ',');
- *next = '\0';
- sscanf(curr, "%g", &layer->biases[i]);
- curr = next+1;
- }
- for(i = 0; i < input*output; ++i){
- while(*++next !='\0' && *next != ',');
- *next = '\0';
- sscanf(curr, "%g", &layer->weights[i]);
- curr = next+1;
- }
- }
+ connected_layer *layer = make_connected_layer(net->batch, input, output, activation,learning_rate,momentum,decay);
+ 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);
+ #ifdef GPU
+ if(weights || biases) push_connected_layer(*layer);
+ #endif
option_unused(options);
return layer;
}
-softmax_layer *parse_softmax(list *options, network net, int count)
+softmax_layer *parse_softmax(list *options, network *net, int count)
+{
+ 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);
+ option_unused(options);
+ return layer;
+}
+
+detection_layer *parse_detection(list *options, network *net, int count)
{
int input;
if(count == 0){
input = option_find_int(options, "input",1);
- net.batch = option_find_int(options, "batch",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);
+ input = get_network_output_size_layer(*net, count-1);
}
- softmax_layer *layer = make_softmax_layer(net.batch, input);
+ 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);
option_unused(options);
return layer;
}
-maxpool_layer *parse_maxpool(list *options, network net, int count)
+cost_layer *parse_cost(list *options, network *net, int count)
+{
+ 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);
+ option_unused(options);
+ return layer;
+}
+
+crop_layer *parse_crop(list *options, network *net, int count)
+{
+ 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);
+ option_unused(options);
+ return layer;
+}
+
+maxpool_layer *parse_maxpool(list *options, network *net, int count)
{
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->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);
+ 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,stride);
+ maxpool_layer *layer = make_maxpool_layer(net->batch,h,w,c,size,stride);
option_unused(options);
return layer;
}
-normalization_layer *parse_normalization(list *options, network net, int count)
+/*
+freeweight_layer *parse_freeweight(list *options, network *net, int count)
+{
+ 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);
+ option_unused(options);
+ return layer;
+}
+
+normalization_layer *parse_normalization(list *options, network *net, int count)
{
int h,w,c;
int size = option_find_int(options, "size",1);
@@ -167,15 +344,16 @@
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->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);
+ 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);
+ normalization_layer *layer = make_normalization_layer(net->batch,h,w,c,size, alpha, beta, kappa);
option_unused(options);
return layer;
}
@@ -191,30 +369,50 @@
section *s = (section *)n->val;
list *options = s->options;
if(is_convolutional(s)){
- convolutional_layer *layer = parse_convolutional(options, net, count);
+ convolutional_layer *layer = parse_convolutional(options, &net, count);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
- net.batch = layer->batch;
+ }else if(is_deconvolutional(s)){
+ deconvolutional_layer *layer = parse_deconvolutional(options, &net, count);
+ 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, &net, count);
net.types[count] = CONNECTED;
net.layers[count] = layer;
- net.batch = layer->batch;
+ }else if(is_crop(s)){
+ crop_layer *layer = parse_crop(options, &net, count);
+ net.types[count] = CROP;
+ net.layers[count] = layer;
+ }else if(is_cost(s)){
+ cost_layer *layer = parse_cost(options, &net, count);
+ net.types[count] = COST;
+ net.layers[count] = layer;
+ }else if(is_detection(s)){
+ detection_layer *layer = parse_detection(options, &net, count);
+ 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, &net, count);
net.types[count] = SOFTMAX;
net.layers[count] = layer;
- net.batch = layer->batch;
}else if(is_maxpool(s)){
- maxpool_layer *layer = parse_maxpool(options, net, count);
+ maxpool_layer *layer = parse_maxpool(options, &net, count);
net.types[count] = MAXPOOL;
net.layers[count] = layer;
- net.batch = layer->batch;
}else if(is_normalization(s)){
- normalization_layer *layer = parse_normalization(options, net, count);
+ normalization_layer *layer = parse_normalization(options, &net, count);
net.types[count] = NORMALIZATION;
net.layers[count] = layer;
- net.batch = layer->batch;
+ }else if(is_dropout(s)){
+ dropout_layer *layer = parse_dropout(options, &net, count);
+ 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);
}
@@ -228,6 +426,23 @@
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
@@ -243,6 +458,14 @@
return (strcmp(s->type, "[max]")==0
|| strcmp(s->type, "[maxpool]")==0);
}
+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)
{
@@ -257,8 +480,8 @@
int read_option(char *s, list *options)
{
- int i;
- int len = strlen(s);
+ size_t i;
+ size_t len = strlen(s);
char *val = 0;
for(i = 0; i < len; ++i){
if(s[i] == '='){
@@ -298,7 +521,7 @@
break;
default:
if(!read_option(line, current->options)){
- printf("Config file error line %d, could parse: %s\n", nu, line);
+ fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
free(line);
}
break;
@@ -308,3 +531,335 @@
return sections;
}
+void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
+{
+ #ifdef GPU
+ if(gpu_index >= 0) pull_convolutional_layer(*l);
+ #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"
+ "pad=%d\n"
+ "activation=%s\n",
+ l->n, l->size, l->stride, l->pad,
+ get_activation_string(l->activation));
+ fprintf(fp, "biases=");
+ for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
+ fprintf(fp, "\n");
+ fprintf(fp, "weights=");
+ for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
+ fprintf(fp, "\n\n");
+}
+
+void print_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net, int count)
+{
+ #ifdef GPU
+ if(gpu_index >= 0) pull_deconvolutional_layer(*l);
+ #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"
+ "activation=%s\n",
+ l->n, l->size, l->stride,
+ get_activation_string(l->activation));
+ fprintf(fp, "biases=");
+ for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
+ fprintf(fp, "\n");
+ fprintf(fp, "weights=");
+ for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
+ 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
+ if(gpu_index >= 0) pull_connected_layer(*l);
+ #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,
+ get_activation_string(l->activation));
+ fprintf(fp, "biases=");
+ for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
+ fprintf(fp, "\n");
+ fprintf(fp, "weights=");
+ for(i = 0; i < l->outputs*l->inputs; ++i) fprintf(fp, "%g,", l->weights[i]);
+ fprintf(fp, "\n\n");
+}
+
+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"
+ "kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
+}
+
+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");
+}
+
+void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count)
+{
+ fprintf(fp, "[detection]\n");
+ fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\n", l->classes, l->coords, l->rescore);
+ fprintf(fp, "\n");
+}
+
+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");
+}
+
+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){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *) net.layers[i];
+ #ifdef GPU
+ if(gpu_index >= 0){
+ pull_convolutional_layer(layer);
+ }
+ #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
+ if(gpu_index >= 0){
+ pull_deconvolutional_layer(layer);
+ }
+ #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
+ if(gpu_index >= 0){
+ pull_connected_layer(layer);
+ }
+ #endif
+ fwrite(layer.biases, sizeof(float), layer.outputs, fp);
+ fwrite(layer.weights, sizeof(float), layer.outputs*layer.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);
+ 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){
+ convolutional_layer layer = *(convolutional_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
+ if(gpu_index >= 0){
+ push_convolutional_layer(layer);
+ }
+ #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
+ if(gpu_index >= 0){
+ push_deconvolutional_layer(layer);
+ }
+ #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
+ if(gpu_index >= 0){
+ push_connected_layer(layer);
+ }
+ #endif
+ }
+ }
+ fclose(fp);
+}
+
+void load_weights(network *net, char *filename)
+{
+ load_weights_upto(net, filename, net->n);
+}
+
+void save_network(network net, char *filename)
+{
+ FILE *fp = fopen(filename, "w");
+ if(!fp) file_error(filename);
+ int i;
+ for(i = 0; i < net.n; ++i)
+ {
+ if(net.types[i] == CONVOLUTIONAL)
+ print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], net, i);
+ else if(net.types[i] == DECONVOLUTIONAL)
+ print_deconvolutional_cfg(fp, (deconvolutional_layer *)net.layers[i], net, i);
+ else if(net.types[i] == CONNECTED)
+ print_connected_cfg(fp, (connected_layer *)net.layers[i], net, i);
+ else if(net.types[i] == CROP)
+ 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)
+ print_normalization_cfg(fp, (normalization_layer *)net.layers[i], net, i);
+ else if(net.types[i] == SOFTMAX)
+ print_softmax_cfg(fp, (softmax_layer *)net.layers[i], net, i);
+ else if(net.types[i] == DETECTION)
+ print_detection_cfg(fp, (detection_layer *)net.layers[i], net, i);
+ else if(net.types[i] == COST)
+ print_cost_cfg(fp, (cost_layer *)net.layers[i], net, i);
+ }
+ fclose(fp);
+}
+
--
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