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local DotProduct, parent = torch.class('nn.DotProduct', 'nn.Module')
function DotProduct:__init()
parent.__init(self)
self.gradInput = {torch.Tensor(), torch.Tensor()}
end
function DotProduct:updateOutput(input)
local input1, input2 = input[1], input[2]
if input1:dim() == 1 then
-- convert non batch input to batch input
input1 = input1:view(1,-1)
input2 = input2:view(1,-1)
end
if not self.buffer then
self.buffer = input1.new()
end
self.buffer:cmul(input1, input2)
self.output:sum(self.buffer, 2)
self.output:resize(input1:size(1))
return self.output
end
function DotProduct:updateGradInput(input, gradOutput)
local v1 = input[1]
local v2 = input[2]
local not_batch = false
if #self.gradInput ~= 2 then
self.gradInput[1] = self.gradInput[1] or input[1].new()
self.gradInput[2] = self.gradInput[2] or input[2].new()
end
if v1:dim() == 1 then
v1 = v1:view(1,-1)
v2 = v2:view(1,-1)
not_batch = true
end
local gw1 = self.gradInput[1]
local gw2 = self.gradInput[2]
gw1:resizeAs(v1):copy(v2)
gw2:resizeAs(v2):copy(v1)
local go = gradOutput:view(-1,1):expandAs(v1)
gw1:cmul(go)
gw2:cmul(go)
if not_batch then
-- unbatch gradInput
self.gradInput[1]:set(gw1:select(1,1))
self.gradInput[2]:set(gw2:select(1,1))
end
return self.gradInput
end
function DotProduct:clearState()
if self.buffer then self.buffer:set() end
return parent.clearState(self)
end
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