Euclidean.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._gradOutput = self._gradOutput or input.new()
        self._expand3 = self._expand3 or input.new()

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

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

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

        # to prevent div by zero (NaN) bugs
        self._output.resize_as_(self.output).copy_(self.output).add_(0.0000001)
        self._view(self._gradOutput, gradOutput, gradOutput.size())
        torch.div(self._div, gradOutput, self._output)
        assert input.dim() == 2
        batchSize = input.size(0)

        self._div.resize_(batchSize, 1, outputSize)
        self._expand3 = self._div.expand(batchSize, inputSize, outputSize)

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


        torch.sum(self.gradInput, self._repeat2, 2)
        self.gradInput.resize_as_(input)

        return self.gradInput