# 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
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# furnished to do so, subject to the following conditions:
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# SOFTWARE.
import torch.nn as nn
import torch.nn.init as init
from torch import Tensor
[docs]class Linear(nn.Module):
r"""
Wrapper class of torch.nn.Linear
Weight initialize by xavier initialization and bias initialize to zeros.
"""
def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
super(Linear, self).__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
init.xavier_uniform_(self.linear.weight)
if bias:
init.zeros_(self.linear.bias)
def forward(self, x: Tensor) -> Tensor:
return self.linear(x)
[docs]class View(nn.Module):
r""" Wrapper class of torch.view() for Sequential module. """
def __init__(self, shape: tuple, contiguous: bool = False):
super(View, self).__init__()
self.shape = shape
self.contiguous = contiguous
def forward(self, inputs):
if self.contiguous:
inputs = inputs.contiguous()
return inputs.view(*self.shape)
[docs]class Transpose(nn.Module):
r""" Wrapper class of torch.transpose() for Sequential module. """
def __init__(self, shape: tuple):
super(Transpose, self).__init__()
self.shape = shape
def forward(self, inputs: Tensor):
return inputs.transpose(*self.shape)