def get_sign_bias_from_bn(prev: nn.Module, bn: nn.BatchNorm2d, scale=None):
"""get equivalent ``(s, b)`` for this bn such that
``bn(prev(x * scale)) >= 0`` iff ``s * (x + b) >= 0``, where
``s in [-1, 1]`` and ``b`` is an integer"""
from .net_bin import PositiveInputCombination
mean = bn.running_mean
if isinstance(prev, PositiveInputCombination):
mean = mean - prev.get_bias()
k, b = bn._get_scale_bias(bn.running_var, mean)
# cond: k * x + b > 0
if scale is not None:
k *= scale
sign = k.sign()
assert torch.all(sign.abs() > 1e-4)
bias = b / k
bias = torch.floor_(bias) + (sign < 0).to(bias.dtype)
return sign, bias
