From b4b729a15e577c68f64e0ac69fb299de6f5f706c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 17 Apr 2014 16:58:24 +0000
Subject: [PATCH] Merge branch 'master' of pjreddie.com:jnet
---
src/parser.c | 54 +++++++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 49 insertions(+), 5 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index cf35a94..4aa0a79 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -7,6 +7,7 @@
#include "convolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
+#include "normalization_layer.h"
#include "softmax_layer.h"
#include "list.h"
#include "option_list.h"
@@ -21,6 +22,7 @@
int is_connected(section *s);
int is_maxpool(section *s);
int is_softmax(section *s);
+int is_normalization(section *s);
list *read_cfg(char *filename);
void free_section(section *s)
@@ -52,6 +54,7 @@
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);
}else{
image m = get_network_image_layer(net, count-1);
h = m.h;
@@ -59,7 +62,7 @@
c = m.c;
if(h == 0) error("Layer before convolutional layer must output image.");
}
- convolutional_layer *layer = make_convolutional_layer(h,w,c,n,size,stride, activation);
+ convolutional_layer *layer = make_convolutional_layer(net.batch,h,w,c,n,size,stride, activation);
char *data = option_find_str(options, "data", 0);
if(data){
char *curr = data;
@@ -90,10 +93,11 @@
ACTIVATION activation = get_activation(activation_s);
if(count == 0){
input = option_find_int(options, "input",1);
+ net.batch = option_find_int(options, "batch",1);
}else{
input = get_network_output_size_layer(net, count-1);
}
- connected_layer *layer = make_connected_layer(input, output, activation);
+ connected_layer *layer = make_connected_layer(net.batch, input, output, activation);
char *data = option_find_str(options, "data", 0);
if(data){
char *curr = data;
@@ -120,10 +124,11 @@
int input;
if(count == 0){
input = option_find_int(options, "input",1);
+ net.batch = option_find_int(options, "batch",1);
}else{
input = get_network_output_size_layer(net, count-1);
}
- softmax_layer *layer = make_softmax_layer(input);
+ softmax_layer *layer = make_softmax_layer(net.batch, input);
option_unused(options);
return layer;
}
@@ -136,6 +141,7 @@
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);
}else{
image m = get_network_image_layer(net, count-1);
h = m.h;
@@ -143,7 +149,31 @@
c = m.c;
if(h == 0) error("Layer before convolutional layer must output image.");
}
- maxpool_layer *layer = make_maxpool_layer(h,w,c,stride);
+ maxpool_layer *layer = make_maxpool_layer(net.batch,h,w,c,stride);
+ 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);
+ 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);
+ }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);
option_unused(options);
return layer;
}
@@ -151,7 +181,7 @@
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
- network net = make_network(sections->size);
+ network net = make_network(sections->size, 0);
node *n = sections->front;
int count = 0;
@@ -162,18 +192,27 @@
convolutional_layer *layer = parse_convolutional(options, net, count);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
+ net.batch = layer->batch;
}else if(is_connected(s)){
connected_layer *layer = parse_connected(options, net, count);
net.types[count] = CONNECTED;
net.layers[count] = layer;
+ net.batch = layer->batch;
}else if(is_softmax(s)){
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);
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);
+ net.types[count] = NORMALIZATION;
+ net.layers[count] = layer;
+ net.batch = layer->batch;
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
@@ -208,6 +247,11 @@
return (strcmp(s->type, "[soft]")==0
|| strcmp(s->type, "[softmax]")==0);
}
+int is_normalization(section *s)
+{
+ return (strcmp(s->type, "[lrnorm]")==0
+ || strcmp(s->type, "[localresponsenormalization]")==0);
+}
int read_option(char *s, list *options)
{
--
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