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

function MultiplicativeFilter:__init(input_size, forbid_randomize)

parent.__init(self)

--self.gradInput:resize(input_size)

--self.output:resize(input_size)

-- the filter cannot be named 'weight' or 'bias', or the neural network modules will attempt to train it

self.bias_filter = torch.Tensor(1, input_size) -- Cmul only depends upon the number of arguments being the same, so no minibatches is the same as one minibatch

self.forbid_randomize = forbid_randomize

self.filter_active = torch.Tensor(1):fill(1)

self:randomize()

end

function MultiplicativeFilter:randomize()

local perturbation_type = 'dropout' -- 'continuous_uniform'

if not(forbid_randomize) then

if perturbation_type == 'continuous_uniform' then

self.bias_filter:copy(torch.rand(self.bias_filter:size(2))):mul(0.1):add(0.95)

elseif perturbation_type == 'dropout' then

self.bias_filter:copy(torch.rand(self.bias_filter:size(2))) -- the multiplicative bias should be mean-1

self.bias_filter:add(-0.1):sign():add(1):mul(0.5)

end

--print(self.bias_filter)

end

end

function MultiplicativeFilter:activate()

self.filter_active[1] = 1

end

function MultiplicativeFilter:inactivate()

self.filter_active[1] = 0

end

function MultiplicativeFilter:updateOutput(input)

self.output:resizeAs(input)

if self.filter_active[1] == 1 then

--print(self.bias_filter)

local expanded_bias = self.bias_filter

if input:dim() == 2 then

expanded_bias = torch.expandAs(self.bias_filter, input)

end

self.output:cmul(input, expanded_bias)

else

self.output:copy(input)

end

return self.output

end

function MultiplicativeFilter:updateGradInput(input, gradOutput)

self.gradInput:resizeAs(gradOutput)

if self.filter_active[1] == 1 then

local expanded_bias = self.bias_filter

if input:dim() == 2 then

expanded_bias = torch.expandAs(self.bias_filter, input)

end

self.gradInput:cmul(gradOutput, expanded_bias)

else

self.gradInput:copy(gradOutput)

end

return self.gradInput

end