SparseNN/SparseBlockFlattenDim3.lua at master · IBPA/SparseNN · GitHub

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local SparseBlockFlattenDim3, parent = torch.class('nn.SparseBlockFlattenDim3', 'nn.Module')

function SparseBlockFlattenDim3:__init()
end

function SparseBlockFlattenDim3:pri_ensureOutput(input)
   if self.output ~= nil then
      return
   end

   self.output = { nBatchSize = input.nBatchSize, taData = {} }

   local nColumns = table.getn(input.taData)
   for i=1, nColumns do
      local taInputCurr = input.taData[i]

      taOutputCurr = { teValue = torch.Tensor(),
                       teRowIdx = taInputCurr.teRowIdx }

      table.insert(self.output.taData, taOutputCurr)
   end
end

function SparseBlockFlattenDim3:pri_updateOutput_column(input, output)
   local nRows = input.teValue:size(1)
   torch.view(output.teValue, input.teValue, nRows, -1)
end

function SparseBlockFlattenDim3:updateOutput(input)
   self:pri_ensureOutput(input)

   local nColumns = table.getn(self.output.taData)
   for i=1, nColumns do
      self:pri_updateOutput_column(input.taData[i],
                                   self.output.taData[i])
   end

   return self.output
end

function SparseBlockFlattenDim3:pri_ensureGradInput(input)
   if self.gradInput ~= nil then
      return
   end

   self.gradInput = { nBatchSize = input.nBatchSize, taData = {} }

   local nColumns = table.getn(input.taData)
   for i=1, nColumns do
      local taInputCurr = input.taData[i]

      taGradInputCurr = { teValue = torch.Tensor(),
                          teRowIdx = taInputCurr.teRowIdx }
      table.insert(self.gradInput.taData, taGradInputCurr)
   end
end

function SparseBlockFlattenDim3:pri_updateGradInput_column(taInput, taGradOutput, taGradInput)
   local input = taInput.teValue
   local gradOutput = taGradOutput.teValue
   local gradInput = taGradInput.teValue
   torch.viewAs(gradInput, gradOutput, input)
end

function SparseBlockFlattenDim3:updateGradInput(input, gradOutput)
   self:pri_ensureGradInput(input)

   local nColumns = table.getn(self.gradInput.taData)
   for i=1, nColumns do
      self:pri_updateGradInput_column(input.taData[i],
                                      gradOutput.taData[i],
                                      self.gradInput.taData[i])

   end

   return self.gradInput
end