| import torch |
| import torch.nn as nn |
|
|
| class RevIN(nn.Module): |
| def __init__(self, num_features: int, eps=1e-5, affine=True, subtract_last=False): |
| """ |
| :param num_features: the number of features or channels |
| :param eps: a value added for numerical stability |
| :param affine: if True, RevIN has learnable affine parameters |
| """ |
| super(RevIN, self).__init__() |
| self.num_features = num_features |
| self.eps = eps |
| self.affine = affine |
| self.subtract_last = subtract_last |
| if self.affine: |
| self._init_params() |
|
|
| def forward(self, x, mode:str): |
| if mode == 'norm': |
| self._get_statistics(x) |
| x = self._normalize(x) |
| elif mode == 'denorm': |
| x = self._denormalize(x) |
| else: raise NotImplementedError |
| return x |
|
|
| def _init_params(self): |
| |
| self.affine_weight = nn.Parameter(torch.ones(self.num_features)) |
| self.affine_bias = nn.Parameter(torch.zeros(self.num_features)) |
|
|
| def _get_statistics(self, x): |
| dim2reduce = tuple(range(1, x.ndim-1)) |
| if self.subtract_last: |
| self.last = x[:,-1,:].unsqueeze(1) |
| else: |
| self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach() |
| self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach() |
|
|
| def _normalize(self, x): |
| if self.subtract_last: |
| x = x - self.last |
| else: |
| x = x - self.mean |
| x = x / self.stdev |
| if self.affine: |
| x = x * self.affine_weight |
| x = x + self.affine_bias |
| return x |
|
|
| def _denormalize(self, x): |
| if self.affine: |
| x = x - self.affine_bias |
| x = x / (self.affine_weight + self.eps*self.eps) |
| x = x * self.stdev |
| if self.subtract_last: |
| x = x + self.last |
| else: |
| x = x + self.mean |
| return x |
