[Minor] Move lua contrib libraries to lua- prefix

1 files changed, 0 insertions, 55 deletions

diff --git a/contrib/torch/nn/SpatialDropout.lua b/contrib/torch/nn/SpatialDropout.lua
deleted file mode 100644
index 4320061b7..000000000
--- a/contrib/torch/nn/SpatialDropout.lua
+++ /dev/null
@@ -1,55 +0,0 @@
-local SpatialDropout, Parent = torch.class('nn.SpatialDropout', 'nn.Module')
-
-function SpatialDropout:__init(p,stochasticInference)
-   Parent.__init(self)
-   self.p = p or 0.5
-   self.train = true
-   self.stochastic_inference = stochasticInference or false
-   self.noise = torch.Tensor()
-end
-
-function SpatialDropout:updateOutput(input)
-   self.output:resizeAs(input):copy(input)
-   if self.train or self.stochastic_inference then
-      if input:dim() == 4 then
-        self.noise:resize(input:size(1), input:size(2), 1, 1)
-      elseif input:dim() == 3 then
-        self.noise:resize(input:size(1), 1, 1)
-      else
-        error('Input must be 4D (nbatch, nfeat, h, w) or 3D (nfeat, h, w)')
-      end
-      self.noise:bernoulli(1-self.p)
-      -- We expand the random dropouts to the entire feature map because the
-      -- features are likely correlated across the map and so the dropout
-      -- should also be correlated.
-      self.output:cmul(torch.expandAs(self.noise, input))
-   else
-      self.output:mul(1-self.p)
-   end
-   return self.output
-end
-
-function SpatialDropout:updateGradInput(input, gradOutput)
-   if self.train then
-      self.gradInput:resizeAs(gradOutput):copy(gradOutput)
-      self.gradInput:cmul(torch.expandAs(self.noise, input)) -- simply mask the gradients with the noise vector
-   else
-      error('backprop only defined while training')
-   end
-   return self.gradInput
-end
-
-function SpatialDropout:setp(p)
-   self.p = p
-end
-
-function SpatialDropout:__tostring__()
-  return string.format('%s(%f)', torch.type(self), self.p)
-end
-
-function SpatialDropout:clearState()
-  if self.noise then
-    self.noise:set()
-  end
-  return Parent.clearState(self)
-end