62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
# Copyright (c) 2023 Amphion.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import torch.nn as nn
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from modules.general.utils import Conv1d
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class GaU(nn.Module):
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r"""Gated Activation Unit (GaU) proposed in `Gated Activation Units for Neural
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Networks <https://arxiv.org/pdf/1606.05328.pdf>`_.
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Args:
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channels: number of input channels.
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kernel_size: kernel size of the convolution.
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dilation: dilation rate of the convolution.
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d_context: dimension of context tensor, None if don't use context.
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"""
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def __init__(
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self,
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channels: int,
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kernel_size: int = 3,
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dilation: int = 1,
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d_context: int = None,
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):
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super().__init__()
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self.context = d_context
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self.conv = Conv1d(
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channels,
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channels * 2,
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kernel_size,
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dilation=dilation,
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padding=dilation * (kernel_size - 1) // 2,
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)
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if self.context:
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self.context_proj = Conv1d(d_context, channels * 2, 1)
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def forward(self, x: torch.Tensor, context: torch.Tensor = None):
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r"""Calculate forward propagation.
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Args:
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x: input tensor with shape [B, C, T].
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context: context tensor with shape [B, ``d_context``, T], default to None.
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"""
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h = self.conv(x)
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if self.context:
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h = h + self.context_proj(context)
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h1, h2 = h.chunk(2, 1)
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h = torch.tanh(h1) * torch.sigmoid(h2)
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return h
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