loss.py 文件源码

def forward(self, input1, input2, y):
        self.w1  = input1.new()
        self.w22 = input1.new()
        self.w  = input1.new()
        self.w32 = input1.new()
        self._outputs = input1.new()

        buffer = input1.new()
        _idx = self._new_idx(input1)

        torch.mul(buffer, input1, input2)
        torch.sum(self.w1, buffer, 1)

        epsilon = 1e-12
        torch.mul(buffer, input1, input1)
        torch.sum(self.w22, buffer, 1).add_(epsilon)

        self._outputs.resize_as_(self.w22).fill_(1)
        torch.div(self.w22, self._outputs, self.w22)
        self.w.resize_as_(self.w22).copy_(self.w22)

        torch.mul(buffer, input2, input2)
        torch.sum(self.w32, buffer, 1).add_(epsilon)
        torch.div(self.w32, self._outputs, self.w32)
        self.w.mul_(self.w32)
        self.w.sqrt_()

        torch.mul(self._outputs, self.w1, self.w)
        self._outputs = self._outputs.select(1, 0)

        torch.eq(_idx, y, -1)
        self._outputs[_idx] = self._outputs[_idx].add_(-self.margin).cmax_(0)
        torch.eq(_idx, y, 1)
        self._outputs[_idx] = self._outputs[_idx].mul_(-1).add_(1)

        output = self._outputs.sum()

        if self.size_average:
            output = output / y.size(0)

        self.save_for_backward(input1, input2, y)
        return input1.new((output,))