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# Copyright (c) 2023 Amphion. | |
# | |
# This source code is licensed under the MIT license found in the | |
# LICENSE file in the root directory of this source tree. | |
# This source file is copied from https://github.com/facebookresearch/encodec | |
# 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 einops | |
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): | |
x = einops.rearrange(x, "b ... t -> b t ...") | |
x = super().forward(x) | |
x = einops.rearrange(x, "b t ... -> b ... t") | |
return | |