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local MarginRankingCriterion, parent = torch.class('nn.MarginRankingCriterion', 'nn.Criterion')
function MarginRankingCriterion:__init(margin)
parent.__init(self)
margin=margin or 1
self.margin = margin
self.gradInput = {torch.Tensor(1), torch.Tensor(1)}
self.sizeAverage = true
end
function MarginRankingCriterion:updateOutput(input, y)
if torch.type(y) == 'number' then -- non-batch mode
self.output = math.max(0, -y * (input[1][1] - input[2][1]) + self.margin)
else
self._output = self._output or input[1]:clone()
self._output:resizeAs(input[1])
self._output:copy(input[1])
self._output:add(-1, input[2])
self._output:mul(-1):cmul(y)
self._output:add(self.margin)
self._output:cmax(0)
self.output = self._output:sum()
if self.sizeAverage then
self.output = self.output/y:size(1)
end
end
return self.output
end
function MarginRankingCriterion:updateGradInput(input, y)
if torch.type(y) == 'number' then -- non-batch mode
local dist = -y * (input[1][1] - input[2][1]) + self.margin
if dist < 0 then
self.gradInput[1][1] = 0;
self.gradInput[2][1] = 0;
else
self.gradInput[1][1] = -y
self.gradInput[2][1] = y
end
else
self.dist = self.dist or input[1].new()
self.dist = self.dist:resizeAs(input[1]):copy(input[1])
local dist = self.dist
dist:add(-1, input[2])
dist:mul(-1):cmul(y)
dist:add(self.margin)
self.mask = self.mask or input[1].new()
self.mask = self.mask:resizeAs(input[1]):copy(dist)
local mask = self.mask
mask:ge(dist, 0)
self.gradInput[1]:resize(dist:size())
self.gradInput[2]:resize(dist:size())
self.gradInput[1]:copy(mask)
self.gradInput[1]:mul(-1):cmul(y)
self.gradInput[2]:copy(mask)
self.gradInput[2]:cmul(y)
if self.sizeAverage then
self.gradInput[1]:div(y:size(1))
self.gradInput[2]:div(y:size(1))
end
end
return self.gradInput
end
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