85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
# Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe)
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# 2020 Northwestern Polytechnical University (Pengcheng Guo)
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# 2020 Mobvoi Inc (Binbin Zhang)
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# 2024 Alibaba Inc (Xiang Lyu)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Swish() activation function for Conformer."""
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import torch
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from torch import nn, sin, pow
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from torch.nn import Parameter
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class Swish(torch.nn.Module):
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"""Construct an Swish object."""
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Return Swish activation function."""
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return x * torch.sigmoid(x)
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# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
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# LICENSE is in incl_licenses directory.
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class Snake(nn.Module):
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'''
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Implementation of a sine-based periodic activation function
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Shape:
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- Input: (B, C, T)
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- Output: (B, C, T), same shape as the input
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Parameters:
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- alpha - trainable parameter
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References:
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- This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
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https://arxiv.org/abs/2006.08195
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Examples:
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>>> a1 = snake(256)
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>>> x = torch.randn(256)
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>>> x = a1(x)
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'''
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def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
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'''
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Initialization.
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INPUT:
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- in_features: shape of the input
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- alpha: trainable parameter
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alpha is initialized to 1 by default, higher values = higher-frequency.
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alpha will be trained along with the rest of your model.
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'''
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super(Snake, self).__init__()
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self.in_features = in_features
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# initialize alpha
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self.alpha_logscale = alpha_logscale
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if self.alpha_logscale: # log scale alphas initialized to zeros
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self.alpha = Parameter(torch.zeros(in_features) * alpha)
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else: # linear scale alphas initialized to ones
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self.alpha = Parameter(torch.ones(in_features) * alpha)
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self.alpha.requires_grad = alpha_trainable
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self.no_div_by_zero = 0.000000001
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def forward(self, x):
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'''
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Forward pass of the function.
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Applies the function to the input elementwise.
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Snake ∶= x + 1/a * sin^2 (xa)
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'''
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alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
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if self.alpha_logscale:
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alpha = torch.exp(alpha)
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x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
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return x
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