lint

diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index 2686ba6..30235b3 100644 (file)
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -48,7 +48,8 @@ Dtype MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
         sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-        sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
+        sum_multiplier_.cpu_data(), 0.,
+        variance_.mutable_cpu_data());  // E(X^2)
     caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
         temp_.mutable_cpu_data());  // (EX)^2
     caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
@@ -132,7 +133,8 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
         sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-        sum_multiplier_.cpu_data(), 0., variance_.mutable_cpu_data());  // E(X^2)
+        sum_multiplier_.cpu_data(), 0.,
+        variance_.mutable_cpu_data());  // E(X^2)
     caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
         temp_.mutable_cpu_data());  // (EX)^2
     caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),

diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 6d1d715..dd82398 100644 (file)
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -24,47 +24,48 @@ Dtype MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     // put the squares of bottom into temp_
     caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
         temp_.mutable_gpu_data());
-  
+
     // computes variance using var(X) = E(X^2) - (EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
         sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-        sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
+        sum_multiplier_.gpu_data(), 0.,
+        variance_.mutable_gpu_data());  // E(X^2)
     caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
         temp_.mutable_gpu_data());  // (EX)^2
     caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
         variance_.mutable_gpu_data());  // variance
-  
+
     Dtype eps = 1e-10;
-  
+
     // do mean and variance normalization
     // subtract mean
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
             mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
             temp_.mutable_gpu_data());
-  
+
     caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
-  
+
     // normalize variance
     caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
           variance_.mutable_gpu_data());
-  
+
     caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-  
+
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
           variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
           temp_.mutable_gpu_data());
-  
+
     caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
   } else {
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
             sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-    
+
     // subtract mean
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
             mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
             temp_.mutable_gpu_data());
-  
+
     caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
   }
 
@@ -98,40 +99,41 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
           bottom_diff);
     caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
-  
+
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_diff,
             sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
             mean_.gpu_data(), sum_multiplier_.gpu_data(), 1.,
             bottom_diff);
-  
+
     caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff, Dtype(-1. / dim),
         bottom_diff);
-  
+
     // put the squares of bottom into temp_
     caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
         temp_.mutable_gpu_data());
-  
+
     // computes variance using var(X) = E(X^2) - (EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
         sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-        sum_multiplier_.gpu_data(), 0., variance_.mutable_gpu_data());  // E(X^2)
+        sum_multiplier_.gpu_data(), 0.,
+        variance_.mutable_gpu_data());  // E(X^2)
     caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
         temp_.mutable_gpu_data());  // (EX)^2
     caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
         variance_.mutable_gpu_data());  // variance
-  
+
     // normalize variance
     caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
           variance_.mutable_gpu_data());
-  
+
     caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-  
+
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
         variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
         temp_.mutable_gpu_data());
-  
+
     caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
   } else {
     caffe_copy(temp_.count(), top_diff, bottom_diff);