(self, sum_, ssum, size)
return mean, bias_var.clamp(self.eps) ** -0.5
Compute the mean and standard-deviation with sum and square-sum. This method
        also maintains the moving average on the master device.
Compute the mean and standard-deviation with sum and square-sum. This method
        also maintains the moving average on the master device.
Compute the mean and standard - deviation with sum and square - sum . This method also maintains the moving average on the master device .
def _compute_mean_std(self, sum_, ssum, size):
        """Compute the mean and standard-deviation with sum and square-sum. This method
        also maintains the moving average on the master device."""
        assert (
            size > 1
        ), "BatchNorm computes unbiased standard-deviation, which requires size > 1."
        mean = sum_ / size
        sumvar = ssum - sum_ * mean
        unbias_var = sumvar / (size - 1)
        bias_var = sumvar / size

        if hasattr(torch, "no_grad"):
            with torch.no_grad():
                self.running_mean = (
                    1 - self.momentum
                ) * self.running_mean + self.momentum * mean.data
                self.running_var = (
                    1 - self.momentum
                ) * self.running_var + self.momentum * unbias_var.data
        else:
            self.running_mean = (
                1 - self.momentum
            ) * self.running_mean + self.momentum * mean.data
            self.running_var = (
                1 - self.momentum
            ) * self.running_var + self.momentum * unbias_var.data

        return mean, bias_var.clamp(self.eps) ** -0.5
def _compute_mean_std ( self , sum_ , ssum , size ) : assert ( size > 1 ) , "BatchNorm computes unbiased standard-deviation, which requires size > 1." mean = sum_ / size sumvar = ssum - sum_ * mean unbias_var = sumvar / ( size - 1 ) bias_var = sumvar / size if hasattr ( torch , "no_grad" ) : with torch . no_grad ( ) : self . running_mean = ( 1 - self . momentum ) * self . running_mean + self . momentum * mean . data self . running_var = ( 1 - self . momentum ) * self . running_var + self . momentum * unbias_var . data else : self . running_mean = ( 1 - self . momentum ) * self . running_mean + self . momentum * mean . data self . running_var = ( 1 - self . momentum ) * self . running_var + self . momentum * unbias_var . data return mean , bias_var . clamp ( self . eps ) ** - 0.5