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local PairwiseDistance, parent = torch.class('nn.PairwiseDistance', 'nn.Module')
function PairwiseDistance:__init(p)
parent.__init(self)
-- state
self.gradInput = {torch.Tensor(), torch.Tensor()}
self.output = torch.Tensor()
self.norm=p
end
function PairwiseDistance:updateOutput(input)
if input[1]:dim() == 1 then
self.output[1]=input[1]:dist(input[2],self.norm)
elseif input[1]:dim() == 2 then
self.diff = self.diff or input[1].new()
self.diff:resizeAs(input[1])
local diff = self.diff:zero()
--local diff = torch.add(input[1], -1, input[2])
diff:add(input[1], -1, input[2])
self.output:resize(input[1]:size(1))
self.output:zero()
self.output:add(diff:pow(self.norm):sum(2))
self.output:pow(1./self.norm)
else
error('input must be vector or matrix')
end
return self.output
end
local function mathsign(x)
if x==0 then return 2*torch.random(2)-3; end
if x>0 then return 1; else return -1; end
end
function PairwiseDistance:updateGradInput(input, gradOutput)
self.gradInput[1]:resize(input[1]:size())
self.gradInput[2]:resize(input[2]:size())
self.gradInput[1]:copy(input[1])
self.gradInput[1]:add(-1, input[2])
if self.norm==1 then
self.gradInput[1]:apply(mathsign)
end
if input[1]:dim() == 1 then
self.gradInput[1]:mul(gradOutput[1])
elseif input[1]:dim() == 2 then
self.grad = self.grad or gradOutput.new()
self.ones = self.ones or gradOutput.new()
self.grad:resizeAs(input[1]):zero()
self.ones:resize(input[1]:size(2)):fill(1)
self.grad:addr(gradOutput, self.ones)
self.gradInput[1]:cmul(self.grad)
else
error('input must be vector or matrix')
end
self.gradInput[2]:zero():add(-1, self.gradInput[1])
return self.gradInput
end
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