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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.
from torch import nn
class StreamableLSTM(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
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