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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| """Normalization modules.""" | |
| import typing as tp | |
| import torch | |
| from torch import nn | |
| class ConvLayerNorm(nn.LayerNorm): | |
| """ | |
| Convolution-friendly LayerNorm that moves channels to last dimensions | |
| before running the normalization and moves them back to original position right after. | |
| """ | |
| def __init__( | |
| self, normalized_shape: tp.Union[int, tp.List[int], torch.Size], **kwargs | |
| ): | |
| super().__init__(normalized_shape, **kwargs) | |
| def forward(self, x): | |
| assert x.ndim == 3 # (n_batch, n_channels, n_samples) | |
| x = x.transpose(1, 2) | |
| x = super().forward(x) | |
| x = x.transpose(1, 2) | |
| return x | |