From 75db98db253adf7fbde293f102ab095b02402f9e Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 10 Jul 2015 23:38:30 +0000
Subject: [PATCH] normalization layer

---
 src/parser.c |   30 +++++++++++++++++++++++++++---
 1 files changed, 27 insertions(+), 3 deletions(-)

diff --git a/src/parser.c b/src/parser.c
index c0db443..3646cf2 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -7,6 +7,7 @@
 #include "crop_layer.h"
 #include "cost_layer.h"
 #include "convolutional_layer.h"
+#include "normalization_layer.h"
 #include "deconvolutional_layer.h"
 #include "connected_layer.h"
 #include "maxpool_layer.h"
@@ -30,6 +31,7 @@
 int is_maxpool(section *s);
 int is_dropout(section *s);
 int is_softmax(section *s);
+int is_normalization(section *s);
 int is_crop(section *s);
 int is_cost(section *s);
 int is_detection(section *s);
@@ -167,7 +169,7 @@
     int rescore = option_find_int(options, "rescore", 0);
     int joint = option_find_int(options, "joint", 0);
     int objectness = option_find_int(options, "objectness", 0);
-    int background = option_find_int(options, "background", 1);
+    int background = option_find_int(options, "background", 0);
     detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, joint, rescore, background, objectness);
     option_unused(options);
     return layer;
@@ -228,6 +230,17 @@
     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);
+    option_unused(options);
+    return l;
+}
+
 route_layer parse_route(list *options, size_params params, network net)
 {
     char *l = option_find(options, "layers");   
@@ -328,6 +341,8 @@
             l = parse_detection(options, params);
         }else if(is_softmax(s)){
             l = parse_softmax(options, params);
+        }else if(is_normalization(s)){
+            l = parse_normalization(options, params);
         }else if(is_maxpool(s)){
             l = parse_maxpool(options, params);
         }else if(is_route(s)){
@@ -343,6 +358,7 @@
         }else{
             fprintf(stderr, "Type not recognized: %s\n", s->type);
         }
+        l.dontload = option_find_int_quiet(options, "dontload", 0);
         net.layers[count] = l;
         free_section(s);
         n = n->next;
@@ -402,6 +418,12 @@
     return (strcmp(s->type, "[dropout]")==0);
 }
 
+int is_normalization(section *s)
+{
+    return (strcmp(s->type, "[lrn]")==0
+            || strcmp(s->type, "[normalization]")==0);
+}
+
 int is_softmax(section *s)
 {
     return (strcmp(s->type, "[soft]")==0
@@ -514,7 +536,8 @@
 
 void load_weights_upto(network *net, char *filename, int cutoff)
 {
-    fprintf(stderr, "Loading weights from %s\n", filename);
+    fprintf(stderr, "Loading weights from %s...", filename);
+    fflush(stdout);
     FILE *fp = fopen(filename, "r");
     if(!fp) file_error(filename);
 
@@ -522,11 +545,11 @@
     fread(&net->momentum, sizeof(float), 1, fp);
     fread(&net->decay, sizeof(float), 1, fp);
     fread(&net->seen, sizeof(int), 1, fp);
-    fprintf(stderr, "%f %f %f %d\n", net->learning_rate, net->momentum, net->decay, net->seen);
 
     int i;
     for(i = 0; i < net->n && i < cutoff; ++i){
         layer l = net->layers[i];
+        if (l.dontload) continue;
         if(l.type == CONVOLUTIONAL){
             int num = l.n*l.c*l.size*l.size;
             fread(l.biases, sizeof(float), l.n, fp);
@@ -557,6 +580,7 @@
 #endif
         }
     }
+    fprintf(stderr, "Done!\n");
     fclose(fp);
 }
 

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