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# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import pow, sin
from torch.nn import Parameter
from .resample import DownSample1d, UpSample1d
class Activation1d(nn.Module):
def __init__(self,
activation,
up_ratio: int = 2,
down_ratio: int = 2,
up_kernel_size: int = 12,
down_kernel_size: int = 12):
super().__init__()
self.up_ratio = up_ratio
self.down_ratio = down_ratio
self.act = activation
self.upsample = UpSample1d(up_ratio, up_kernel_size)
self.downsample = DownSample1d(down_ratio, down_kernel_size)
# x: [B,C,T]
def forward(self, x):
x = self.upsample(x)
x = self.act(x)
x = self.downsample(x)
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
class Mish(nn.Module):
"""
Mish activation function is proposed in "Mish: A Self
Regularized Non-Monotonic Neural Activation Function"
paper, https://arxiv.org/abs/1908.08681.
"""
def __init__(self):
super().__init__()
def forward(self, x):
return x * torch.tanh(F.softplus(x))
class SnakeAlias(nn.Module):
def __init__(self,
channels,
up_ratio: int = 2,
down_ratio: int = 2,
up_kernel_size: int = 12,
down_kernel_size: int = 12,
C = None):
super().__init__()
self.up_ratio = up_ratio
self.down_ratio = down_ratio
self.act = SnakeBeta(channels, alpha_logscale=True)
self.upsample = UpSample1d(up_ratio, up_kernel_size, C)
self.downsample = DownSample1d(down_ratio, down_kernel_size, C)
# x: [B,C,T]
def forward(self, x, C=None):
x = self.upsample(x, C)
x = self.act(x)
x = self.downsample(x)
return x |