Fixed renaming issue

3 files changed, 3 insertions, 2 deletions

diff --git a/CMakeLists.txt b/CMakeLists.txt
index a7abd1a..efc52fc 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -19,6 +19,7 @@ IF(NOT CUDNN_FOUND)
   FIND_PACKAGE(CUDNN 6 EXACT REQUIRED)
 ENDIF()
 
+
 FILE(GLOB luasrc *.lua)
 SET(src "")
 ADD_TORCH_PACKAGE(cudnn "${src}" "${luasrc}" "NVIDIA CuDNN Bindings")
diff --git a/README.md b/README.md
index 1271bbe..c356988 100644
--- a/README.md
+++ b/README.md
@@ -91,7 +91,7 @@ If you don't want to convert all modules you can pass a function as the third ar
 It will be called at each step, with a module that is currently converted.  It is meant to exclude
 modules i.e. if it returns `true`, they will be left untouched, otherwise they will be subject to conversion.
 
-`Note that you cannot do backward pass when using cuDNN and when your model has batch normaliation layers and is in evaluate mode.`
+`Note that you cannot do backward pass when using cuDNN and when your model has batch normalization layers and is in evaluate mode.`
 
 ```lua
 net = nn.Sequential()
diff --git a/init.lua b/init.lua
index 35575f2..13a77fd 100644
--- a/init.lua
+++ b/init.lua
@@ -219,7 +219,7 @@ function cudnn.setConvolutionDescriptor(data, desc)
    -- make sure we have references to these tensors so gc doesn't clean them up
    local padATensor = torch.IntTensor(data.padA)
    local filterStrideATensor = torch.IntTensor(data.filterStrideA)
-   local upscaleATensor = torch.IntTensor(data.upscaleA)
+   local upscaleATensor = torch.IntTensor(data.dilationA)
    errcheck('cudnnSetConvolutionNdDescriptor', myDesc[0],
             data.arrayLength,
             padATensor:data(),