From 2b4e07f13e94a5fe36dcdb28156c70540eaadcb6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 10 Jul 2015 23:38:39 +0000
Subject: [PATCH] small parser change

---
 src/parser.c |   34 +++++++++++++++++++++++-----------
 1 files changed, 23 insertions(+), 11 deletions(-)

diff --git a/src/parser.c b/src/parser.c
index 2caf96e..6d9116f 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);
@@ -100,7 +102,6 @@
     #ifdef GPU
     if(weights || biases) push_deconvolutional_layer(layer);
     #endif
-    option_unused(options);
     return layer;
 }
 
@@ -129,7 +130,6 @@
     #ifdef GPU
     if(weights || biases) push_convolutional_layer(layer);
     #endif
-    option_unused(options);
     return layer;
 }
 
@@ -148,7 +148,6 @@
     #ifdef GPU
     if(weights || biases) push_connected_layer(layer);
     #endif
-    option_unused(options);
     return layer;
 }
 
@@ -156,7 +155,6 @@
 {
     int groups = option_find_int(options, "groups",1);
     softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
-    option_unused(options);
     return layer;
 }
 
@@ -169,7 +167,6 @@
     int objectness = option_find_int(options, "objectness", 0);
     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;
 }
 
@@ -178,7 +175,6 @@
     char *type_s = option_find_str(options, "type", "sse");
     COST_TYPE type = get_cost_type(type_s);
     cost_layer layer = make_cost_layer(params.batch, params.inputs, type);
-    option_unused(options);
     return layer;
 }
 
@@ -199,7 +195,6 @@
     if(!(h && w && c)) error("Layer before crop layer must output image.");
 
     crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
-    option_unused(options);
     return l;
 }
 
@@ -216,7 +211,6 @@
     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;
 }
 
@@ -224,10 +218,19 @@
 {
     float probability = option_find_float(options, "probability", .5);
     dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability);
-    option_unused(options);
     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);
+    return l;
+}
+
 route_layer parse_route(list *options, size_params params, network net)
 {
     char *l = option_find(options, "layers");   
@@ -265,7 +268,6 @@
         }
     }
 
-    option_unused(options);
     return layer;
 }
 
@@ -285,7 +287,6 @@
     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");
-    option_unused(options);
 }
 
 network parse_network_cfg(char *filename)
@@ -328,6 +329,8 @@
             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 +346,8 @@
         }else{
             fprintf(stderr, "Type not recognized: %s\n", s->type);
         }
+        l.dontload = option_find_int_quiet(options, "dontload", 0);
+        option_unused(options);
         net.layers[count] = l;
         free_section(s);
         n = n->next;
@@ -402,6 +407,12 @@
     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
@@ -527,6 +538,7 @@
     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);

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