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local SuperCriterion, parent = torch.class('nn.SuperCriterion', 'nn.Criterion')
function SuperCriterion:__init()
parent.__init(self)
self.criterions = {}
self.weights = {}
self.gradInput = {}
end
function SuperCriterion:add(criterion, weight)
weight = weight or 1
table.insert(self.criterions, criterion)
table.insert(self.weights, weight)
end
function SuperCriterion:updateOutput(input, target)
self.output = 0
if type(target) == 'table' then
for i,criterion in ipairs(self.criterions) do
self.output = self.output + self.weights[i]*criterion:updateOutput(input[i],target[i])
end
else
for i,criterion in ipairs(self.criterions) do
self.output = self.output + self.weights[i]*criterion:updateOutput(input[i],target)
end
end
return self.output
end
function SuperCriterion:updateGradInput(input, target)
if type(target) == 'table' then
for i,criterion in ipairs(self.criterions) do
self.gradInput[i] = torch.Tensor() or self.gradInput[i]
self.gradInput[i]:resizeAs(input[i]):zero()
self.gradInput[i]:add(self.weights[i], criterion:updateGradInput(input[i],target[i]))
end
else
for i,criterion in ipairs(self.criterions) do
self.gradInput[i] = torch.Tensor() or self.gradInput[i]
self.gradInput[i]:resizeAs(input[i]):zero()
self.gradInput[i]:add(self.weights[i], criterion:updateGradInput(input[i],target))
end
end
return self.gradInput
end
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