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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 torch
from torch import nn, pow, sin
from torch.nn import Parameter
class Snake(nn.Module):
r"""Implementation of a sine-based periodic activation function.
Alpha is initialized to 1 by default, higher values means higher frequency.
It will be trained along with the rest of your model.
Args:
in_features: shape of the input
alpha: trainable parameter
Shape:
- Input: (B, C, T)
- Output: (B, C, T), same shape as the input
References:
This activation function is from this paper by Liu Ziyin, Tilman Hartwig,
Masahito Ueda: https://arxiv.org/abs/2006.08195
Examples:
>>> a1 = Snake(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(
self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False
):
super(Snake, self).__init__()
self.in_features = in_features
# initialize alpha
self.alpha_logscale = alpha_logscale
if self.alpha_logscale: # log scale alphas initialized to zeros
self.alpha = Parameter(torch.zeros(in_features) * alpha)
else: # linear scale alphas initialized to ones
self.alpha = Parameter(torch.ones(in_features) * alpha)
self.alpha.requires_grad = alpha_trainable
self.no_div_by_zero = 0.000000001
def forward(self, x):
r"""Forward pass of the function. Applies the function to the input elementwise.
Snake ∶= x + 1/a * sin^2 (ax)
"""
alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
if self.alpha_logscale:
alpha = torch.exp(alpha)
x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
return x
class SnakeBeta(nn.Module):
r"""A modified Snake function which uses separate parameters for the magnitude
of the periodic components. Alpha is initialized to 1 by default,
higher values means higher frequency. Beta is initialized to 1 by default,
higher values means higher magnitude. Both will be trained along with the
rest of your model.
Args:
in_features: shape of the input
alpha: trainable parameter that controls frequency
beta: trainable parameter that controls magnitude
Shape:
- Input: (B, C, T)
- Output: (B, C, T), same shape as the input
References:
This activation function is a modified version based on this paper by Liu Ziyin,
Tilman Hartwig, Masahito Ueda: https://arxiv.org/abs/2006.08195
Examples:
>>> a1 = SnakeBeta(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(
self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False
):
super(SnakeBeta, self).__init__()
self.in_features = in_features
# initialize alpha
self.alpha_logscale = alpha_logscale
if self.alpha_logscale: # log scale alphas initialized to zeros
self.alpha = Parameter(torch.zeros(in_features) * alpha)
self.beta = Parameter(torch.zeros(in_features) * alpha)
else: # linear scale alphas initialized to ones
self.alpha = Parameter(torch.ones(in_features) * alpha)
self.beta = Parameter(torch.ones(in_features) * alpha)
self.alpha.requires_grad = alpha_trainable
self.beta.requires_grad = alpha_trainable
self.no_div_by_zero = 0.000000001
def forward(self, x):
r"""Forward pass of the function. Applies the function to the input elementwise.
SnakeBeta ∶= x + 1/b * sin^2 (xa)
"""
alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
beta = self.beta.unsqueeze(0).unsqueeze(-1)
if self.alpha_logscale:
alpha = torch.exp(alpha)
beta = torch.exp(beta)
x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
return x
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