SpatialCrossResponseNormalization.lua

local C = inn.C

local SpatialCrossResponseNormalization, parent = torch.class('inn.SpatialCrossResponseNormalization', 'nn.Module')

function SpatialCrossResponseNormalization:__init(size, alpha, beta, k)
  parent.__init(self)
  
  self.size = size
  self.alpha = alpha or 0.0001
  self.beta = beta or 0.75
  self.k = k or 1

  self.output = torch.Tensor()
  self.gradInput = torch.Tensor()
  self.scale = torch.Tensor()

  self:cuda()
end

function SpatialCrossResponseNormalization:updateOutput(input)
  assert(torch.isTypeOf(input, 'torch.CudaTensor'))
  C.LRNforward(cutorch.getState(), input:cdata(), self.output:cdata(), 
  	self.scale:cdata(), self.size, self.alpha, self.beta, self.k)
  return self.output
end


function SpatialCrossResponseNormalization:updateGradInput(input, gradOutput)
  assert(torch.isTypeOf(input, 'torch.CudaTensor'))
  assert(torch.isTypeOf(gradOutput, 'torch.CudaTensor'))
  C.LRNbackward(cutorch.getState(), input:cdata(), self.output:cdata(),
  	gradOutput:cdata(), self.gradInput:cdata(), self.scale:cdata(), 
  	self.size, self.alpha, self.beta, self.k) 
  return self.gradInput
end