Add THNN conversion for Spatial* modules

Add THNN conversion of SpatialBatchNormalization, SpatialFractionalMaxPooling and SpatialSubSampling

Add THNN convertion of SpatialConvolutionLocal, SpatialFullConvolution and SpatialUpSamplingNearest

THNN conversion of SpatialMaxUnpooling

Remove unfold from generic

Add functional conversion of SpatialCrossMapLRN

Plus fix in the init.c

Fix

1 files changed, 20 insertions, 19 deletions

diff --git a/SpatialBatchNormalization.lua b/SpatialBatchNormalization.lua
index 2fb92b6..5ea9f1b 100644
--- a/SpatialBatchNormalization.lua
+++ b/SpatialBatchNormalization.lua
@@ -29,6 +29,7 @@
 
 ]]--
 local BN,parent = torch.class('nn.SpatialBatchNormalization', 'nn.Module')
+local THNN = require 'nn.THNN'
 
 BN.__version = 2
 
@@ -80,18 +81,18 @@ function BN:updateOutput(input)
    self.save_std = self.save_std or input.new()
    self.save_std:resizeAs(self.running_var)
 
-   input.nn.SpatialBatchNormalization_updateOutput(
-      input,
-      self.output,
-      self.weight,
-      self.bias,
+   input.THNN.SpatialBatchNormalization_updateOutput(
+      input:cdata(),
+      self.output:cdata(),
+      THNN.optionalTensor(self.weight),
+      THNN.optionalTensor(self.bias),
+      self.running_mean:cdata(),
+      self.running_var:cdata(),
+      self.save_mean:cdata(),
+      self.save_std:cdata(),
       self.train,
-      self.eps,
       self.momentum,
-      self.running_mean,
-      self.running_var,
-      self.save_mean,
-      self.save_std)
+      self.eps)
 
    return self.output
 end
@@ -107,15 +108,15 @@ local function backward(self, input, gradOutput, scale, gradInput, gradWeight, g
       gradInput:resizeAs(gradOutput)
    end
 
-   input.nn.SpatialBatchNormalization_backward(
-      input,
-      gradOutput,
-      gradInput,
-      gradWeight,
-      gradBias,
-      self.weight,
-      self.save_mean,
-      self.save_std,
+   input.THNN.SpatialBatchNormalization_backward(
+      input:cdata(),
+      gradOutput:cdata(),
+      THNN.optionalTensor(gradInput),
+      THNN.optionalTensor(gradWeight),
+      THNN.optionalTensor(gradBias),
+      THNN.optionalTensor(self.weight),
+      self.save_mean:cdata(),
+      self.save_std:cdata(),
       scale)
 
    return self.gradInput