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+-- ref: https://en.wikipedia.org/wiki/Huber_loss
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+local WeightedHuberCriterion, parent = torch.class('w2nn.WeightedHuberCriterion','nn.Criterion')
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+
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+function WeightedHuberCriterion:__init(w, gamma)
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+ parent.__init(self)
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+ self.gamma = gamma or 1.0
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+ self.weight = w:clone()
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+ self.diff = torch.Tensor()
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+ self.diff_abs = torch.Tensor()
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+ --self.outlier_rate = 0.0
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+ self.square_loss_buff = torch.Tensor()
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+ self.linear_loss_buff = torch.Tensor()
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+end
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+function WeightedHuberCriterion:updateOutput(input, target)
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+ self.diff:resizeAs(input):copy(input)
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+ for i = 1, input:size(1) do
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+ self.diff[i]:add(-1, target[i]):cmul(self.weight)
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+ end
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+ self.diff_abs:resizeAs(self.diff):copy(self.diff):abs()
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+
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+ local square_targets = self.diff[torch.lt(self.diff_abs, self.gamma)]
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+ local linear_targets = self.diff[torch.ge(self.diff_abs, self.gamma)]
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+ local square_loss = self.square_loss_buff:resizeAs(square_targets):copy(square_targets):pow(2.0):mul(0.5):sum()
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+ local linear_loss = self.linear_loss_buff:resizeAs(linear_targets):copy(linear_targets):abs():add(-0.5 * self.gamma):mul(self.gamma):sum()
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+
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+ --self.outlier_rate = linear_targets:nElement() / input:nElement()
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+ self.output = (square_loss + linear_loss) / input:nElement()
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+ return self.output
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+end
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+function WeightedHuberCriterion:updateGradInput(input, target)
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+ local norm = 1.0 / input:nElement()
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+ self.gradInput:resizeAs(self.diff):copy(self.diff):mul(norm)
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+ local outlier = torch.ge(self.diff_abs, self.gamma)
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+ self.gradInput[outlier] = torch.sign(self.diff[outlier]) * self.gamma * norm
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+ return self.gradInput
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+end
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