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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.
#################### Norm2D for Discriminators ####################
import torch
import torch.nn as nn
import einops
from torch.nn.utils import spectral_norm, weight_norm
CONV_NORMALIZATIONS = frozenset(
[
"none",
"weight_norm",
"spectral_norm",
"time_layer_norm",
"layer_norm",
"time_group_norm",
]
)
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, **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
def apply_parametrization_norm(module: nn.Module, norm: str = "none") -> nn.Module:
assert norm in CONV_NORMALIZATIONS
if norm == "weight_norm":
return weight_norm(module)
elif norm == "spectral_norm":
return spectral_norm(module)
else:
# We already check was in CONV_NORMALIZATION, so any other choice
# doesn't need reparametrization.
return module
def get_norm_module(
module: nn.Module, causal: bool = False, norm: str = "none", **norm_kwargs
) -> nn.Module:
"""Return the proper normalization module. If causal is True, this will ensure the returned
module is causal, or return an error if the normalization doesn't support causal evaluation.
"""
assert norm in CONV_NORMALIZATIONS
if norm == "layer_norm":
assert isinstance(module, nn.modules.conv._ConvNd)
return ConvLayerNorm(module.out_channels, **norm_kwargs)
elif norm == "time_group_norm":
if causal:
raise ValueError("GroupNorm doesn't support causal evaluation.")
assert isinstance(module, nn.modules.conv._ConvNd)
return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
else:
return nn.Identity()
class NormConv2d(nn.Module):
"""Wrapper around Conv2d and normalization applied to this conv
to provide a uniform interface across normalization approaches.
"""
def __init__(
self,
*args,
norm: str = "none",
norm_kwargs={},
**kwargs,
):
super().__init__()
self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
self.norm_type = norm
def forward(self, x):
x = self.conv(x)
x = self.norm(x)
return x
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