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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.
import math
import torch
import torch.nn as nn
from modules.activation_functions import GaU
from modules.general.utils import Conv1d
class ResidualBlock(nn.Module):
r"""Residual block with dilated convolution, main portion of ``BiDilConv``.
Args:
channels: The number of channels of input and output.
kernel_size: The kernel size of dilated convolution.
dilation: The dilation rate of dilated convolution.
d_context: The dimension of content encoder output, None if don't use context.
"""
def __init__(
self,
channels: int = 256,
kernel_size: int = 3,
dilation: int = 1,
d_context: int = None,
):
super().__init__()
self.context = d_context
self.gau = GaU(
channels,
kernel_size,
dilation,
d_context,
)
self.out_proj = Conv1d(
channels,
channels * 2,
1,
)
def forward(
self,
x: torch.Tensor,
y_emb: torch.Tensor,
context: torch.Tensor = None,
):
"""
Args:
x: Latent representation inherited from previous residual block
with the shape of [B x C x T].
y_emb: Embeddings with the shape of [B x C], which will be FILM on the x.
context: Context with the shape of [B x ``d_context`` x T], default to None.
"""
h = x + y_emb[..., None]
if self.context:
h = self.gau(h, context)
else:
h = self.gau(h)
h = self.out_proj(h)
res, skip = h.chunk(2, 1)
return (res + x) / math.sqrt(2.0), skip
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