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48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
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# -*- coding: utf-8 -*-
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# Copyright 2022 Reo Yoneyama (Nagoya University)
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# MIT License (https://opensource.org/licenses/MIT)
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"""Snake Activation Function Module.
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References:
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- Neural Networks Fail to Learn Periodic Functions and How to Fix It
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https://arxiv.org/pdf/2006.08195.pdf
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- BigVGAN: A Universal Neural Vocoder with Large-Scale Training
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https://arxiv.org/pdf/2206.04658.pdf
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"""
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import torch
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import torch.nn as nn
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class Snake(nn.Module):
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"""Snake activation function module."""
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def __init__(self, channels, init=50):
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"""Initialize Snake module.
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Args:
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channels (int): Number of feature channels.
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init (float): Initial value of the learnable parameter alpha.
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According to the original paper, 5 ~ 50 would be
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suitable for periodic data (i.e. voices).
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"""
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super(Snake, self).__init__()
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alpha = init * torch.ones(1, channels, 1)
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self.alpha = nn.Parameter(alpha)
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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x (Tensor): Input noise signal (B, channels, T).
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Returns:
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Tensor: Output tensor (B, channels, T).
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"""
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return x + torch.sin(self.alpha * x) ** 2 / self.alpha
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