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# -*- coding: utf-8 -*-
# file: td_lstm.py
# author: songyouwei <youwei0314@gmail.com>
# Copyright (C) 2018. All Rights Reserved.
from layers.dynamic_rnn import DynamicLSTM
import torch
import torch.nn as nn
class TD_LSTM(nn.Module):
def __init__(self, embedding_matrix, opt):
super(TD_LSTM, self).__init__()
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.lstm_l = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
self.lstm_r = DynamicLSTM(opt.embed_dim, opt.hidden_dim, num_layers=1, batch_first=True)
self.dense = nn.Linear(opt.hidden_dim*2, opt.polarities_dim)
def forward(self, inputs):
x_l, x_r = inputs[0], inputs[1]
x_l_len, x_r_len = torch.sum(x_l != 0, dim=-1), torch.sum(x_r != 0, dim=-1)
x_l, x_r = self.embed(x_l), self.embed(x_r)
_, (h_n_l, _) = self.lstm_l(x_l, x_l_len)
_, (h_n_r, _) = self.lstm_r(x_r, x_r_len)
h_n = torch.cat((h_n_l[0], h_n_r[0]), dim=-1)
out = self.dense(h_n)
return out