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local ParallelCriterion, parent = torch.class('nn.ParallelCriterion', 'nn.Criterion')
function ParallelCriterion:__init(repeatTarget)
parent.__init(self)
self.criterions = {}
self.weights = {}
self.gradInput = {}
self.repeatTarget = repeatTarget
end
function ParallelCriterion:add(criterion, weight)
assert(criterion, 'no criterion provided')
weight = weight or 1
table.insert(self.criterions, criterion)
table.insert(self.weights, weight)
return self
end
function ParallelCriterion:updateOutput(input, target)
self.output = 0
for i,criterion in ipairs(self.criterions) do
local target = self.repeatTarget and target or target[i]
self.output = self.output + self.weights[i]*criterion:updateOutput(input[i],target)
end
return self.output
end
function ParallelCriterion:updateGradInput(input, target)
self.gradInput = nn.utils.recursiveResizeAs(self.gradInput, input)
nn.utils.recursiveFill(self.gradInput, 0)
for i,criterion in ipairs(self.criterions) do
local target = self.repeatTarget and target or target[i]
nn.utils.recursiveAdd(self.gradInput[i], self.weights[i], criterion:updateGradInput(input[i], target))
end
return self.gradInput
end
function ParallelCriterion:type(type, tensorCache)
self.gradInput = {}
return parent.type(self, type, tensorCache)
end
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