From 9802287b5890d9b2cc250adba1b9810657a95c9c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 18 Dec 2015 23:55:58 +0000
Subject: [PATCH] some fixes

---
 src/convolutional_layer.c |   39 +++++++++++++++++++++++++--------------
 1 files changed, 25 insertions(+), 14 deletions(-)

diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index b9fd3c9..e97b00d 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -86,9 +86,8 @@
         }
 
         l.mean = calloc(n, sizeof(float));
-        l.spatial_mean = calloc(n*l.batch, sizeof(float));
-
         l.variance = calloc(n, sizeof(float));
+
         l.rolling_mean = calloc(n, sizeof(float));
         l.rolling_variance = calloc(n, sizeof(float));
     }
@@ -114,12 +113,6 @@
         l.rolling_mean_gpu = cuda_make_array(l.mean, n);
         l.rolling_variance_gpu = cuda_make_array(l.variance, n);
 
-        l.spatial_mean_gpu = cuda_make_array(l.spatial_mean, n*l.batch);
-        l.spatial_variance_gpu = cuda_make_array(l.spatial_mean, n*l.batch);
-
-        l.spatial_mean_delta_gpu = cuda_make_array(l.spatial_mean, n*l.batch);
-        l.spatial_variance_delta_gpu = cuda_make_array(l.spatial_mean, n*l.batch);
-
         l.mean_delta_gpu = cuda_make_array(l.mean, n);
         l.variance_delta_gpu = cuda_make_array(l.variance, n);
 
@@ -201,13 +194,25 @@
 #endif
 }
 
-void bias_output(float *output, float *biases, int batch, int n, int size)
+void add_bias(float *output, float *biases, int batch, int n, int size)
 {
     int i,j,b;
     for(b = 0; b < batch; ++b){
         for(i = 0; i < n; ++i){
             for(j = 0; j < size; ++j){
-                output[(b*n + i)*size + j] = biases[i];
+                output[(b*n + i)*size + j] += biases[i];
+            }
+        }
+    }
+}
+
+void scale_bias(float *output, float *scales, int batch, int n, int size)
+{
+    int i,j,b;
+    for(b = 0; b < batch; ++b){
+        for(i = 0; i < n; ++i){
+            for(j = 0; j < size; ++j){
+                output[(b*n + i)*size + j] *= scales[i];
             }
         }
     }
@@ -229,7 +234,7 @@
     int out_w = convolutional_out_width(l);
     int i;
 
-    bias_output(l.output, l.biases, l.batch, l.n, out_h*out_w);
+    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
 
     int m = l.n;
     int k = l.size*l.size*l.c;
@@ -248,10 +253,16 @@
     }
 
     if(l.batch_normalize){
-        mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean);   
-        variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance);   
-        normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w);   
+        if(state.train){
+            mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean);   
+            variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance);   
+            normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w);   
+        } else {
+            normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.n, l.out_h*l.out_w);
+        }
+        scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
     }
+    add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
 
     activate_array(l.output, m*n*l.batch, l.activation);
 }

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