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# -*- coding: utf-8 -*-
# file: lcf_bert.py
# author: yangheng <yangheng@m.scnu.edu.cn>
# Copyright (C) 2019. All Rights Reserved.
# The code is based on repository: https://github.com/yangheng95/LCF-ABSA
import torch
import torch.nn as nn
import copy
import numpy as np
from pytorch_transformers.modeling_bert import BertPooler, BertSelfAttention
class SelfAttention(nn.Module):
def __init__(self, config, opt):
super(SelfAttention, self).__init__()
self.opt = opt
self.config = config
self.SA = BertSelfAttention(config)
self.tanh = torch.nn.Tanh()
def forward(self, inputs):
zero_tensor = torch.tensor(np.zeros((inputs.size(0), 1, 1, self.opt.max_seq_len),
dtype=np.float32), dtype=torch.float32).to(self.opt.device)
SA_out = self.SA(inputs, zero_tensor)
return self.tanh(SA_out[0])
class LCF_BERT(nn.Module):
def __init__(self, bert, opt):
super(LCF_BERT, self).__init__()
self.bert_spc = bert
self.opt = opt
# self.bert_local = copy.deepcopy(bert) # Uncomment the line to use dual Bert
self.bert_local = bert # Default to use single Bert and reduce memory requirements
self.dropout = nn.Dropout(opt.dropout)
self.bert_SA = SelfAttention(bert.config, opt)
self.linear_double = nn.Linear(opt.bert_dim * 2, opt.bert_dim)
self.linear_single = nn.Linear(opt.bert_dim, opt.bert_dim)
self.bert_pooler = BertPooler(bert.config)
self.dense = nn.Linear(opt.bert_dim, opt.polarities_dim)
def feature_dynamic_mask(self, text_local_indices, aspect_indices):
texts = text_local_indices.cpu().numpy()
asps = aspect_indices.cpu().numpy()
mask_len = self.opt.SRD
masked_text_raw_indices = np.ones((text_local_indices.size(0), self.opt.max_seq_len, self.opt.bert_dim),
dtype=np.float32)
for text_i, asp_i in zip(range(len(texts)), range(len(asps))):
asp_len = np.count_nonzero(asps[asp_i]) - 2
try:
asp_begin = np.argwhere(texts[text_i] == asps[asp_i][1])[0][0]
except:
continue
if asp_begin >= mask_len:
mask_begin = asp_begin - mask_len
else:
mask_begin = 0
for i in range(mask_begin):
masked_text_raw_indices[text_i][i] = np.zeros((self.opt.bert_dim), dtype=np.float)
for j in range(asp_begin + asp_len + mask_len, self.opt.max_seq_len):
masked_text_raw_indices[text_i][j] = np.zeros((self.opt.bert_dim), dtype=np.float)
masked_text_raw_indices = torch.from_numpy(masked_text_raw_indices)
return masked_text_raw_indices.to(self.opt.device)
def feature_dynamic_weighted(self, text_local_indices, aspect_indices):
texts = text_local_indices.cpu().numpy()
asps = aspect_indices.cpu().numpy()
masked_text_raw_indices = np.ones((text_local_indices.size(0), self.opt.max_seq_len, self.opt.bert_dim),
dtype=np.float32)
for text_i, asp_i in zip(range(len(texts)), range(len(asps))):
asp_len = np.count_nonzero(asps[asp_i]) - 2
try:
asp_begin = np.argwhere(texts[text_i] == asps[asp_i][1])[0][0]
asp_avg_index = (asp_begin * 2 + asp_len) / 2
except:
continue
distances = np.zeros(np.count_nonzero(texts[text_i]), dtype=np.float32)
for i in range(1, np.count_nonzero(texts[text_i])-1):
if abs(i - asp_avg_index) + asp_len / 2 > self.opt.SRD:
distances[i] = 1 - (abs(i - asp_avg_index)+asp_len/2
- self.opt.SRD)/np.count_nonzero(texts[text_i])
else:
distances[i] = 1
for i in range(len(distances)):
masked_text_raw_indices[text_i][i] = masked_text_raw_indices[text_i][i] * distances[i]
masked_text_raw_indices = torch.from_numpy(masked_text_raw_indices)
return masked_text_raw_indices.to(self.opt.device)
def forward(self, inputs):
text_bert_indices = inputs[0]
bert_segments_ids = inputs[1]
text_local_indices = inputs[2]
aspect_indices = inputs[3]
bert_spc_out, _ = self.bert_spc(text_bert_indices, bert_segments_ids)
bert_spc_out = self.dropout(bert_spc_out)
bert_local_out, _ = self.bert_local(text_local_indices)
bert_local_out = self.dropout(bert_local_out)
if self.opt.local_context_focus == 'cdm':
masked_local_text_vec = self.feature_dynamic_mask(text_local_indices, aspect_indices)
bert_local_out = torch.mul(bert_local_out, masked_local_text_vec)
elif self.opt.local_context_focus == 'cdw':
weighted_text_local_features = self.feature_dynamic_weighted(text_local_indices, aspect_indices)
bert_local_out = torch.mul(bert_local_out, weighted_text_local_features)
out_cat = torch.cat((bert_local_out, bert_spc_out), dim=-1)
mean_pool = self.linear_double(out_cat)
self_attention_out = self.bert_SA(mean_pool)
pooled_out = self.bert_pooler(self_attention_out)
dense_out = self.dense(pooled_out)
return dense_out
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