From 8bcdee86585f496afe1a8a38d608ea0504a11243 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 01 Sep 2015 18:22:03 +0000
Subject: [PATCH] Some bug fixes, random stuff

---
 src/parser.c |   62 +++++++++++++++++++++++++++++++
 1 files changed, 62 insertions(+), 0 deletions(-)

diff --git a/src/parser.c b/src/parser.c
index 5591dc3..ad324e9 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -14,6 +14,7 @@
 #include "softmax_layer.h"
 #include "dropout_layer.h"
 #include "detection_layer.h"
+#include "region_layer.h"
 #include "avgpool_layer.h"
 #include "route_layer.h"
 #include "list.h"
@@ -37,6 +38,7 @@
 int is_crop(section *s);
 int is_cost(section *s);
 int is_detection(section *s);
+int is_region(section *s);
 int is_route(section *s);
 list *read_cfg(char *filename);
 
@@ -172,6 +174,17 @@
     return layer;
 }
 
+region_layer parse_region(list *options, size_params params)
+{
+    int coords = option_find_int(options, "coords", 1);
+    int classes = option_find_int(options, "classes", 1);
+    int rescore = option_find_int(options, "rescore", 0);
+    int num = option_find_int(options, "num", 1);
+    int side = option_find_int(options, "side", 7);
+    region_layer layer = make_region_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
+    return layer;
+}
+
 cost_layer parse_cost(list *options, size_params params)
 {
     char *type_s = option_find_str(options, "type", "sse");
@@ -236,6 +249,9 @@
 {
     float probability = option_find_float(options, "probability", .5);
     dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability);
+    layer.out_w = params.w;
+    layer.out_h = params.h;
+    layer.out_c = params.c;
     return layer;
 }
 
@@ -327,6 +343,7 @@
 
     n = n->next;
     int count = 0;
+    free_section(s);
     while(n){
         fprintf(stderr, "%d: ", count);
         s = (section *)n->val;
@@ -344,6 +361,8 @@
             l = parse_cost(options, params);
         }else if(is_detection(s)){
             l = parse_detection(options, params);
+        }else if(is_region(s)){
+            l = parse_region(options, params);
         }else if(is_softmax(s)){
             l = parse_softmax(options, params);
         }else if(is_normalization(s)){
@@ -396,6 +415,10 @@
 {
     return (strcmp(s->type, "[detection]")==0);
 }
+int is_region(section *s)
+{
+    return (strcmp(s->type, "[region]")==0);
+}
 int is_deconvolutional(section *s)
 {
     return (strcmp(s->type, "[deconv]")==0
@@ -500,6 +523,45 @@
     return sections;
 }
 
+void save_weights_double(network net, char *filename)
+{
+    fprintf(stderr, "Saving doubled 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,j,k;
+    for(i = 0; i < net.n; ++i){
+        layer l = net.layers[i];
+        if(l.type == CONVOLUTIONAL){
+#ifdef GPU
+            if(gpu_index >= 0){
+                pull_convolutional_layer(l);
+            }
+#endif
+            float zero = 0;
+            fwrite(l.biases, sizeof(float), l.n, fp);
+            fwrite(l.biases, sizeof(float), l.n, fp);
+
+            for (j = 0; j < l.n; ++j){
+                int index = j*l.c*l.size*l.size;
+                fwrite(l.filters+index, sizeof(float), l.c*l.size*l.size, fp);
+                for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
+            }
+            for (j = 0; j < l.n; ++j){
+                int index = j*l.c*l.size*l.size;
+                for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
+                fwrite(l.filters+index, sizeof(float), l.c*l.size*l.size, fp);
+            }
+        }
+    }
+    fclose(fp);
+}
+
 void save_weights_upto(network net, char *filename, int cutoff)
 {
     fprintf(stderr, "Saving weights to %s\n", filename);

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