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Running
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Zero
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import torch
from torch import nn, sin, pow
from torch.nn import Parameter
class Snake(nn.Module):
"""
Implementation of a sine-based periodic activation function
Shape:
- Input: (B, C, T)
- Output: (B, C, T), same shape as the input
Parameters:
- alpha - trainable parameter
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
):
"""
Initialization.
INPUT:
- in_features: shape of the input
- alpha: trainable parameter
alpha is initialized to 1 by default, higher values = higher-frequency.
alpha will be trained along with the rest of your model.
"""
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):
"""
Forward pass of the function.
Applies the function to the input elementwise.
Snake ∶= x + 1/a * sin^2 (xa)
"""
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):
"""
A modified Snake function which uses separate parameters for the magnitude of the periodic components
Shape:
- Input: (B, C, T)
- Output: (B, C, T), same shape as the input
Parameters:
- alpha - trainable parameter that controls frequency
- beta - trainable parameter that controls magnitude
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
):
"""
Initialization.
INPUT:
- in_features: shape of the input
- alpha - trainable parameter that controls frequency
- beta - trainable parameter that controls magnitude
alpha is initialized to 1 by default, higher values = higher-frequency.
beta is initialized to 1 by default, higher values = higher-magnitude.
alpha will be trained along with the rest of your model.
"""
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):
"""
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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