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from torch import nn
from torch.nn.utils.rnn import PackedSequence
class LstmFlatteningResult(nn.LSTM):
def forward(self, input, *fargs, **fkwargs):
output, (hidden, cell) = nn.LSTM.forward(self, input, *fargs, **fkwargs)
return output, hidden, cell
class LstmFlatteningResultWithSeqLength(nn.Module):
def __init__(self, input_size, hidden_size, layers, bidirect, dropout, batch_first):
super().__init__()
self.batch_first = batch_first
self.inner_model = nn.LSTM(
input_size=input_size,
hidden_size=hidden_size,
num_layers=layers,
bidirectional=bidirect,
dropout=dropout,
batch_first=batch_first,
)
def forward(self, input: PackedSequence, hx=None):
output, (hidden, cell) = self.inner_model.forward(input, hx)
return output, hidden, cell
class LstmFlatteningResultWithoutSeqLength(nn.Module):
def __init__(self, input_size, hidden_size, layers, bidirect, dropout, batch_first):
super().__init__()
self.batch_first = batch_first
self.inner_model = nn.LSTM(
input_size=input_size,
hidden_size=hidden_size,
num_layers=layers,
bidirectional=bidirect,
dropout=dropout,
batch_first=batch_first,
)
def forward(self, input, hx=None):
output, (hidden, cell) = self.inner_model.forward(input, hx)
return output, hidden, cell
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