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| # Written by Shigeki Karita, 2019 | |
| # Published under Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) | |
| # Adapted by Florian Lux, 2021 | |
| import torch | |
| class LayerNorm(torch.nn.LayerNorm): | |
| """ | |
| Layer normalization module. | |
| Args: | |
| nout (int): Output dim size. | |
| dim (int): Dimension to be normalized. | |
| """ | |
| def __init__(self, nout, dim=-1): | |
| """ | |
| Construct an LayerNorm object. | |
| """ | |
| super(LayerNorm, self).__init__(nout, eps=1e-12) | |
| self.dim = dim | |
| def forward(self, x): | |
| """ | |
| Apply layer normalization. | |
| Args: | |
| x (torch.Tensor): Input tensor. | |
| Returns: | |
| torch.Tensor: Normalized tensor. | |
| """ | |
| if self.dim == -1: | |
| return super(LayerNorm, self).forward(x) | |
| return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1) | |