# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# SOFTWARE.
import torch.nn as nn
from torch import Tensor
from typing import Optional
[docs]class ResidualConnectionModule(nn.Module):
r"""
Residual Connection Module.
outputs = (module(inputs) x module_factor + inputs x input_factor)
"""
def __init__(
self,
module: nn.Module,
module_factor: float = 1.0,
input_factor: float = 1.0,
) -> None:
super(ResidualConnectionModule, self).__init__()
self.module = module
self.module_factor = module_factor
self.input_factor = input_factor
def forward(self, inputs: Tensor, mask: Optional[Tensor] = None) -> Tensor:
if mask is None:
return (self.module(inputs) * self.module_factor) + (inputs * self.input_factor)
else:
return (self.module(inputs, mask) * self.module_factor) + (inputs * self.input_factor)