mirror of
https://github.com/xiph/opus.git
synced 2025-05-16 00:18:29 +00:00
44 lines
1.9 KiB
Python
44 lines
1.9 KiB
Python
"""
|
|
/* Copyright (c) 2023 Amazon
|
|
Written by Jan Buethe */
|
|
/*
|
|
Redistribution and use in source and binary forms, with or without
|
|
modification, are permitted provided that the following conditions
|
|
are met:
|
|
|
|
- Redistributions of source code must retain the above copyright
|
|
notice, this list of conditions and the following disclaimer.
|
|
|
|
- Redistributions in binary form must reproduce the above copyright
|
|
notice, this list of conditions and the following disclaimer in the
|
|
documentation and/or other materials provided with the distribution.
|
|
|
|
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
|
|
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
|
|
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
|
|
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
|
|
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
|
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
|
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
|
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
|
|
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
|
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
|
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*/
|
|
"""
|
|
|
|
import torch
|
|
from torch import nn
|
|
|
|
class DualFC(nn.Module):
|
|
def __init__(self, input_dim, output_dim):
|
|
super(DualFC, self).__init__()
|
|
|
|
self.dense1 = nn.Linear(input_dim, output_dim)
|
|
self.dense2 = nn.Linear(input_dim, output_dim)
|
|
|
|
self.alpha = nn.Parameter(torch.tensor([0.5]), requires_grad=True)
|
|
self.beta = nn.Parameter(torch.tensor([0.5]), requires_grad=True)
|
|
|
|
def forward(self, x):
|
|
return self.alpha * torch.tanh(self.dense1(x)) + self.beta * torch.tanh(self.dense2(x))
|