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local WeightedMSECriterion, parent = torch.class('nn.WeightedMSECriterion','nn.MSECriterion')
function WeightedMSECriterion:__init(w)
parent.__init(self)
self.weight = w:clone()
end
function WeightedMSECriterion:updateOutput(input,target)
self.buffer = self.buffer or input.new()
self.buffer:resizeAs(input):copy(target)
if input:dim() - 1 == self.weight:dim() then
for i=1,input:size(1) do
self.buffer[i]:cmul(self.weight)
end
else
self.buffer:cmul(self.weight)
end
self.output_tensor = self.output_tensor or input.new(1)
input.THNN.MSECriterion_updateOutput(
input:cdata(),
self.buffer:cdata(),
self.output_tensor:cdata(),
self.sizeAverage
)
self.output = self.output_tensor[1]
return self.output
end
function WeightedMSECriterion:updateGradInput(input, target)
self.buffer:resizeAs(input):copy(target)
if input:dim() - 1 == self.weight:dim() then
for i=1,input:size(1) do
self.buffer[i]:cmul(self.weight)
end
else
self.buffer:cmul(self.weight)
end
input.THNN.MSECriterion_updateGradInput(
input:cdata(),
self.buffer:cdata(),
self.gradInput:cdata(),
self.sizeAverage
)
return self.gradInput
end
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