ParallelWaveGAN/parallel_wavegan/layers/layer_norm.py

"""LayerNorm for specific dimensions.

Adapted from ESPnet Transformer LayerNorm.

"""

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(self.dim, -1))
            .transpose(self.dim, -1)
        )