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local PairwiseDistance, parent = torch.class('nn.PairwiseDistance', 'nn.Module')
function PairwiseDistance:__init(p)
parent.__init(self)
-- state
self.gradInput = {}
self.diff = torch.Tensor()
self.norm = p or 2 -- Default using Euclidean distance
end
function PairwiseDistance:updateOutput(input)
self.output:resize(1)
if input[1]:dim() == 1 then
self.output:resize(1)
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()
diff:add(input[1], -1, input[2])
diff:abs()
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)
if input[1]:dim() > 2 then
error('input must be vector or matrix')
end
self.gradInput[1] = (self.gradInput[1] or input[1].new()):resize(input[1]:size())
self.gradInput[2] = (self.gradInput[2] or input[2].new()):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)
else
-- Note: derivative of p-norm:
-- d/dx_k(||x||_p) = (x_k * abs(x_k)^(p-2)) / (||x||_p)^(p-1)
if (self.norm > 2) then
self.gradInput[1]:cmul(self.gradInput[1]:clone():abs():pow(self.norm-2))
end
if (input[1]:dim() > 1) then
self.outExpand = self.outExpand or self.output.new()
self.outExpand:resize(self.output:size(1), 1)
self.outExpand:copy(self.output)
self.outExpand:add(1.0e-6) -- Prevent divide by zero errors
self.outExpand:pow(-(self.norm-1))
self.gradInput[1]:cmul(self.outExpand:expand(self.gradInput[1]:size(1),
self.gradInput[1]:size(2)))
else
self.gradInput[1]:mul(math.pow(self.output[1] + 1e-6, -(self.norm-1)))
end
end
if input[1]:dim() == 1 then
self.gradInput[1]:mul(gradOutput[1])
else
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)
end
self.gradInput[2]:zero():add(-1, self.gradInput[1])
return self.gradInput
end
function PairwiseDistance:clearState()
nn.utils.clear(self, 'diff', 'outExpand', 'grad', 'ones')
return parent.clearState(self)
end
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