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


function TVLoss:__init(strength)
  parent.__init(self)
  self.strength = strength
  self.x_diff = torch.Tensor()
  self.y_diff = torch.Tensor()
end


function TVLoss:updateOutput(input)
  self.output = input
  return self.output
end


-- TV loss backward pass inspired by kaishengtai/neuralart
function TVLoss:updateGradInput(input, gradOutput)
  self.gradInput:resizeAs(input):zero()
  local N, C = input:size(1), input:size(2)
  local H, W = input:size(3), input:size(4)
  self.x_diff:resize(N, 3, H - 1, W - 1)
  self.y_diff:resize(N, 3, H - 1, W - 1)
  self.x_diff:copy(input[{{}, {}, {1, -2}, {1, -2}}])
  self.x_diff:add(-1, input[{{}, {}, {1, -2}, {2, -1}}])
  self.y_diff:copy(input[{{}, {}, {1, -2}, {1, -2}}])
  self.y_diff:add(-1, input[{{}, {}, {2, -1}, {1, -2}}])
  self.gradInput[{{}, {}, {1, -2}, {1, -2}}]:add(self.x_diff):add(self.y_diff)
  self.gradInput[{{}, {}, {1, -2}, {2, -1}}]:add(-1, self.x_diff)
  self.gradInput[{{}, {}, {2, -1}, {1, -2}}]:add(-1, self.y_diff)
  self.gradInput:mul(self.strength)
  self.gradInput:add(gradOutput)
  return self.gradInput
end