loss.py 文件源码

def backward(self, grad_output):
        v1, v2, y = self.saved_tensors

        buffer = v1.new()
        _idx = self._new_idx(v1)

        gw1 = grad_output.new()
        gw2 = grad_output.new()
        gw1.resize_as_(v1).copy_(v2)
        gw2.resize_as_(v1).copy_(v1)

        torch.mul(buffer, self.w1, self.w22)
        gw1.addcmul_(-1, buffer.expand_as(v1), v1)
        gw1.mul_(self.w.expand_as(v1))

        torch.mul(buffer, self.w1, self.w32)
        gw2.addcmul_(-1, buffer.expand_as(v1), v2)
        gw2.mul_(self.w.expand_as(v1))

        torch.le(_idx, self._outputs, 0)
        _idx = _idx.view(-1, 1).expand(gw1.size())
        gw1[_idx] = 0
        gw2[_idx] = 0

        torch.eq(_idx, y, 1)
        _idx = _idx.view(-1, 1).expand(gw2.size())
        gw1[_idx] = gw1[_idx].mul_(-1)
        gw2[_idx] = gw2[_idx].mul_(-1)

        if self.size_average:
            gw1.div_(y.size(0))
            gw2.div_(y.size(0))

        if grad_output[0] != 1:
            gw1.mul_(grad_output)
            gw2.mul_(grad_output)

        return gw1, gw2, None