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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 |