(pre, priors, variances)
return landms
Decode landm from predictions using priors to undo
    the encoding we did for offset regression at train time.
    Args:
        pre (tensor): landm predictions for loc layers,
            Shape: [num_priors,10]
        priors (tensor): Prior boxes in center-offset form.
            Shape: [num_priors,4].
        variances: (list[float]) Variances of priorboxes
    Return:
        decoded landm predictions
Decode landm from predictions using priors to undo
    the encoding we did for offset regression at train time.
    Args:
        pre (tensor): landm predictions for loc layers,
            Shape: [num_priors,10]
        priors (tensor): Prior boxes in center-offset form.
            Shape: [num_priors,4].
        variances: (list[float]) Variances of priorboxes
    Return:
        decoded landm predictions
Decode landm from predictions using priors to undo the encoding we did for offset regression at train time . Args : pre ( tensor ) : landm predictions for loc layers Shape : [ num_priors 10 ] priors ( tensor ) : Prior boxes in center - offset form . Shape : [ num_priors 4 ] . variances : ( list [ float ] ) Variances of priorboxes Return : decoded landm predictions
def decode_landm(pre, priors, variances):
    """Decode landm from predictions using priors to undo
    the encoding we did for offset regression at train time.
    Args:
        pre (tensor): landm predictions for loc layers,
            Shape: [num_priors,10]
        priors (tensor): Prior boxes in center-offset form.
            Shape: [num_priors,4].
        variances: (list[float]) Variances of priorboxes
    Return:
        decoded landm predictions
    """
    landms = torch.cat((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
                        priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
                        priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
                        priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
                        priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
                        ), dim=1)
    return landms
def decode_landm ( pre , priors , variances ) : landms = torch . cat ( ( priors [ : , : 2 ] + pre [ : , : 2 ] * variances [ 0 ] * priors [ : , 2 : ] , priors [ : , : 2 ] + pre [ : , 2 : 4 ] * variances [ 0 ] * priors [ : , 2 : ] , priors [ : , : 2 ] + pre [ : , 4 : 6 ] * variances [ 0 ] * priors [ : , 2 : ] , priors [ : , : 2 ] + pre [ : , 6 : 8 ] * variances [ 0 ] * priors [ : , 2 : ] , priors [ : , : 2 ] + pre [ : , 8 : 10 ] * variances [ 0 ] * priors [ : , 2 : ] , ) , dim = 1 ) return landms