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# -*- coding: utf-8 -*-
# file: atae-lstm
# author: songyouwei <youwei0314@gmail.com>
# Copyright (C) 2018. All Rights Reserved.
from layers.attention import Attention, NoQueryAttention
from layers.dynamic_rnn import DynamicLSTM
import torch
import torch.nn as nn
from layers.squeeze_embedding import SqueezeEmbedding
class ATAE_LSTM(nn.Module):
def __init__(self, embedding_matrix, opt):
super(ATAE_LSTM, self).__init__()
self.opt = opt
self.embed = nn.Embedding.from_pretrained(torch.tensor(embedding_matrix, dtype=torch.float))
self.squeeze_embedding = SqueezeEmbedding()
self.lstm = DynamicLSTM(opt.embed_dim*2, opt.hidden_dim, num_layers=1, batch_first=True)
self.attention = NoQueryAttention(opt.hidden_dim+opt.embed_dim, score_function='bi_linear')
self.dense = nn.Linear(opt.hidden_dim, opt.polarities_dim)
def forward(self, inputs):
text_indices, aspect_indices = inputs[0], inputs[1]
x_len = torch.sum(text_indices != 0, dim=-1)
x_len_max = torch.max(x_len)
aspect_len = torch.sum(aspect_indices != 0, dim=-1).float()
x = self.embed(text_indices)
x = self.squeeze_embedding(x, x_len)
aspect = self.embed(aspect_indices)
aspect_pool = torch.div(torch.sum(aspect, dim=1), aspect_len.unsqueeze(1))
aspect = aspect_pool.unsqueeze(1).expand(-1, x_len_max, -1)
x = torch.cat((aspect, x), dim=-1)
h, (_, _) = self.lstm(x, x_len)
ha = torch.cat((h, aspect), dim=-1)
_, score = self.attention(ha)
output = torch.squeeze(torch.bmm(score, h), dim=1)
out = self.dense(output)
return out