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# MIT License | |
# Copyright (c) Meta Platforms, Inc. and affiliates. | |
# 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 | |
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
# SOFTWARE. | |
# Copyright (c) [2023] [Meta Platforms, Inc. and affiliates.] | |
# Copyright (c) [2025] [Ziyue Jiang] | |
# SPDX-License-Identifier: MIT | |
# This file has been modified by Ziyue Jiang on 2025/03/19 | |
# Original file was released under MIT, with the full license text # available at https://github.com/facebookresearch/encodec/blob/gh-pages/LICENSE. | |
# This modified file is released under the same license. | |
"""LSTM layers module.""" | |
from torch import nn | |
class SLSTM(nn.Module): | |
""" | |
LSTM without worrying about the hidden state, nor the layout of the data. | |
Expects input as convolutional layout. | |
""" | |
def __init__(self, dimension: int, num_layers: int = 2, skip: bool = True): | |
super().__init__() | |
self.skip = skip | |
self.lstm = nn.LSTM(dimension, dimension, num_layers) | |
# 修改transpose顺序 | |
def forward(self, x): | |
x1 = x.permute(2, 0, 1) | |
y, _ = self.lstm(x1) | |
y = y.permute(1, 2, 0) | |
if self.skip: | |
y = y + x | |
return y | |