Add fn for using THNN-only modules during tests

1 files changed, 10 insertions, 10 deletions

diff --git a/test/LinearTHNN.lua b/test/LinearTHNN.lua
index 4ac944e..dc690dc 100644
--- a/test/LinearTHNN.lua
+++ b/test/LinearTHNN.lua
@@ -1,6 +1,6 @@
-local Linear, parent = torch.class('nn.Linear', 'nn.Module')
+local LinearTHNN, parent = torch.class('nn.LinearTHNN', 'nn.Module')
 
-function Linear:__init(inputSize, outputSize, bias)
+function LinearTHNN:__init(inputSize, outputSize, bias)
    parent.__init(self)
    local bias = ((bias == nil) and true) or bias
    self.weight = torch.Tensor(outputSize, inputSize)
@@ -13,13 +13,13 @@ function Linear:__init(inputSize, outputSize, bias)
    self:reset()
 end
 
-function Linear:noBias()
+function LinearTHNN:noBias()
    self.bias = nil
    self.gradBias = nil
    return self
 end
 
-function Linear:reset(stdv)
+function LinearTHNN:reset(stdv)
    if stdv then
       stdv = stdv * math.sqrt(3)
    else
@@ -43,7 +43,7 @@ function Linear:reset(stdv)
    return self
 end
 
-function Linear:updateOutput(input)
+function LinearTHNN:updateOutput(input)
    input.THNN.Linear_updateOutput(
       input:cdata(),
       self.output:cdata(),
@@ -54,7 +54,7 @@ function Linear:updateOutput(input)
    return self.output
 end
 
-function Linear:updateGradInput(input, gradOutput)
+function LinearTHNN:updateGradInput(input, gradOutput)
    input.THNN.Linear_updateGradInput(
       input:cdata(),
       gradOutput:cdata(),
@@ -64,7 +64,7 @@ function Linear:updateGradInput(input, gradOutput)
    return self.gradInput
 end
 
-function Linear:accGradParameters(input, gradOutput, scale)
+function LinearTHNN:accGradParameters(input, gradOutput, scale)
    input.THNN.Linear_accGradParameters(
       input:cdata(),
       gradOutput:cdata(),
@@ -80,14 +80,14 @@ function Linear:accGradParameters(input, gradOutput, scale)
 end
 
 -- we do not need to accumulate parameters when sharing
-Linear.sharedAccUpdateGradParameters = Linear.accUpdateGradParameters
+LinearTHNN.sharedAccUpdateGradParameters = LinearTHNN.accUpdateGradParameters
 
-function Linear:clearState()
+function LinearTHNN:clearState()
    if self.addBuffer then self.addBuffer:set() end
    return parent.clearState(self)
 end
 
-function Linear:__tostring__()
+function LinearTHNN:__tostring__()
   return torch.type(self) ..
       string.format('(%d -> %d)', self.weight:size(2), self.weight:size(1)) ..
       (self.bias == nil and ' without bias' or '')