WeightedEuclidean.py 文件源码

def updateGradInput(self, input, gradOutput):
        if not self.gradInput:
           return

        self._div = self._div or input.new()
        self._output = self._output or self.output.new()
        self._expand4 = self._expand4 or input.new()
        self._gradOutput = self._gradOutput or input.new()

        if not self.fastBackward:
           self.updateOutput(input)

        inputSize, outputSize = self.weight.size(0), self.weight.size(1)

        """
        dy_j   -2 * c_j * c_j * (w_j - x)   c_j * c_j * (x - w_j)
        ---- = -------------------------- = ---------------------
         dx     2 || c_j * (w_j - x) ||              y_j
        """

        # to prevent div by zero (NaN) bugs
        self._output.resize_as_(self.output).copy_(self.output).add_(1e-7)
        self._view(self._gradOutput, gradOutput, gradOutput.size())
        torch.div(self._div, gradOutput, self._output)
        if input.dim() == 1:
            self._div.resize_(1, outputSize)
            self._expand4 = self._div.expand_as(self.weight)

            if torch.type(input) == 'torch.cuda.FloatTensor':
                self._repeat2.resize_as_(self._expand4).copy_(self._expand4)
                self._repeat2.mul_(self._repeat)
            else:
                self._repeat2.mul_(self._repeat, self._expand4)

            self._repeat2.mul_(self.diagCov)
            torch.sum(self.gradInput, self._repeat2, 1)
            self.gradInput.resize_as_(input)
        elif input.dim() == 2:
            batchSize = input.size(0)

            self._div.resize_(batchSize, 1, outputSize)
            self._expand4 = self._div.expand(batchSize, inputSize, outputSize)

            if input.type() == 'torch.cuda.FloatTensor':
                self._repeat2.resize_as_(self._expand4).copy_(self._expand4)
                self._repeat2.mul_(self._repeat)
                self._repeat2.mul_(self._repeat3)
            else:
                torch.mul(self._repeat2, self._repeat, self._expand4)
                self._repeat2.mul_(self._expand3)


            torch.sum(self.gradInput, self._repeat2, 2)
            self.gradInput.resize_as_(input)
        else:
            raise RuntimeError("1D or 2D input expected")

        return self.gradInput