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local DistNLLCriterion, parent = torch.class('nn.DistNLLCriterion', 'nn.Criterion')
function DistNLLCriterion:__init(opts)
parent.__init(self)
-- user options
opts = opts or {}
self.inputIsADistance = opts.inputIsADistance or false
self.inputIsProbability = opts.inputIsProbability or false
self.inputIsLogProbability = opts.inputIsLogProbability or false
self.targetIsProbability = opts.targetIsProbability
if self.targetIsProbability == nil then self.targetIsProbability = true end
-- internal
self.targetSoftMax = nn.SoftMax()
self.inputLogSoftMax = nn.LogSoftMax()
self.inputLog = nn.Log()
self.gradLogInput = torch.Tensor()
self.input = torch.Tensor()
end
function DistNLLCriterion:normalize(input, target)
-- normalize target
if not self.targetIsProbability then
self.probTarget = self.targetSoftMax:updateOutput(target)
else
self.probTarget = target
end
-- flip input if a distance
if self.inputIsADistance then
self.input:resizeAs(input):copy(input):mul(-1)
else
self.input = input
end
-- normalize input
if not self.inputIsLogProbability and not self.inputIsProbability then
self.logProbInput = self.inputLogSoftMax:updateOutput(self.input)
elseif not self.inputIsLogProbability then
self.logProbInput = self.inputLog:updateOutput(self.input)
else
self.logProbInput = self.input
end
end
function DistNLLCriterion:denormalize()
-- denormalize gradients
if not self.inputIsLogProbability and not self.inputIsProbability then
self.gradInput = self.inputLogSoftMax:updateGradInput(self.input, self.gradLogInput)
elseif not self.inputIsLogProbability then
self.gradInput = self.inputLog:updateGradInput(self.input, self.gradLogInput)
else
self.gradInput = self.gradLogInput
end
-- if input is a distance, then flip gradients back
if self.inputIsADistance then
self.gradInput:mul(-1)
end
end
function DistNLLCriterion:updateOutput(input, target)
self:normalize(input, target)
self.output = 0
for i = 1,input:size(1) do
self.output = self.output - self.logProbInput[i] * self.probTarget[i]
end
return self.output
end
function DistNLLCriterion:updateGradInput(input, target)
self:normalize(input, target)
self.gradLogInput:resizeAs(input)
for i = 1,input:size(1) do
self.gradLogInput[i] = -self.probTarget[i]
end
self:denormalize()
return self.gradInput
end
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