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 | 55 +++++++++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 49 insertions(+), 6 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 3f94c80..7b1057e 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -13,6 +13,7 @@
#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"
@@ -32,6 +33,7 @@
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);
@@ -74,7 +76,7 @@
int n = option_find_int(options, "filters",1);
int size = option_find_int(options, "size",1);
int stride = option_find_int(options, "stride",1);
- char *activation_s = option_find_str(options, "activation", "sigmoid");
+ 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);
@@ -118,7 +120,7 @@
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);
@@ -159,7 +161,7 @@
int input;
float learning_rate, momentum, decay;
int output = option_find_int(options, "output",1);
- 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);
@@ -191,6 +193,7 @@
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);
@@ -198,7 +201,25 @@
}else{
input = get_network_output_size_layer(*net, count-1);
}
- softmax_layer *layer = make_softmax_layer(net->batch, input);
+ 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->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);
option_unused(options);
return layer;
}
@@ -367,6 +388,10 @@
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);
net.types[count] = SOFTMAX;
@@ -409,6 +434,10 @@
{
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
@@ -683,6 +712,13 @@
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));
@@ -739,7 +775,7 @@
fclose(fp);
}
-void load_weights(network *net, char *filename)
+void load_weights_upto(network *net, char *filename, int cutoff)
{
fprintf(stderr, "Loading weights from %s\n", filename);
FILE *fp = fopen(filename, "r");
@@ -752,7 +788,7 @@
set_learning_network(net, net->learning_rate, net->momentum, net->decay);
int i;
- for(i = 0; i < net->n; ++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;
@@ -789,6 +825,11 @@
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");
@@ -814,6 +855,8 @@
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);
}
--
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