import torch
import torch.nn as nn
class StackedLSTM(nn.Module):
"""
Our own implementation of stacked LSTM.
Needed for the decoder, because we do input feeding.
"""
def __init__(self, num_layers, input_size, rnn_size, dropout):
super(StackedLSTM, self).__init__()
self.dropout = nn.Dropout(dropout)
self.num_layers = num_layers
self.layers = nn.ModuleList()
for i in range(num_layers):
self.layers.append(nn.LSTMCell(input_size, rnn_size))
input_size = rnn_size
def forward(self, input, hidden):
h_0, c_0 = hidden
h_1, c_1 = [], []
for i, layer in enumerate(self.layers):
h_1_i, c_1_i = layer(input, (h_0[i], c_0[i]))
input = h_1_i
if i + 1 != self.num_layers:
input = self.dropout(input)
h_1 += [h_1_i]
c_1 += [c_1_i]
h_1 = torch.stack(h_1)
c_1 = torch.stack(c_1)
return input, (h_1, c_1)
class StackedGRU(nn.Module):
def __init__(self, num_layers, input_size, rnn_size, dropout):
super(StackedGRU, self).__init__()
self.dropout = nn.Dropout(dropout)
self.num_layers = num_layers
self.layers = nn.ModuleList()
for i in range(num_layers):
self.layers.append(nn.GRUCell(input_size, rnn_size))
input_size = rnn_size
def forward(self, input, hidden):
h_1 = []
for i, layer in enumerate(self.layers):
h_1_i = layer(input, hidden[0][i])
input = h_1_i
if i + 1 != self.num_layers:
input = self.dropout(input)
h_1 += [h_1_i]
h_1 = torch.stack(h_1)
return input, (h_1,)
