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| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| """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) | |
| # def forward(self, x): | |
| # x = x.permute(2, 0, 1) | |
| # y, _ = self.lstm(x) | |
| # if self.skip: | |
| # y = y + x | |
| # y = y.permute(1, 2, 0) | |
| # return y | |
| # 修改transpose顺序 | |
| def forward(self, x): | |
| # # 插入reshape | |
| # x = x.reshape(x.shape) | |
| x1 = x.permute(2, 0, 1) | |
| y, _ = self.lstm(x1) | |
| y = y.permute(1, 2, 0) | |
| if self.skip: | |
| y = y + x | |
| return y | |