forked from XuMayi/PyABSA
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathlcfs_atepc_large.py
131 lines (115 loc) · 6.06 KB
/
lcfs_atepc_large.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
# -*- coding: utf-8 -*-
# @FileName: lcf_atepc_large.py
# @Time : 2021/6/20 10:07
# @Author : yangheng@m.scnu.edu.cn
# @github : https://github.com/yangheng95
# Copyright (C) 2021. All Rights Reserved.
import copy
import numpy as np
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
from transformers.models.bert.modeling_bert import BertForTokenClassification, BertPooler
from pyabsa.network.sa_encoder import Encoder
from pyabsa.core.atepc.dataset_utils.data_utils_for_training import SENTIMENT_PADDING
class LCFS_ATEPC_LARGE(nn.Module):
def __init__(self, bert_base_model, opt):
super(LCFS_ATEPC_LARGE, self).__init__()
config = bert_base_model.config
self.bert4global = bert_base_model
self.opt = opt
self.bert4local = copy.deepcopy(self.bert4global)
self.dropout = nn.Dropout(self.opt.dropout)
self.SA1 = Encoder(config, opt)
self.SA2 = Encoder(config, opt)
self.linear_double = nn.Linear(opt.hidden_dim * 2, opt.hidden_dim)
self.linear_triple = nn.Linear(opt.hidden_dim * 3, opt.hidden_dim)
self.pooler = BertPooler(config)
self.dense = torch.nn.Linear(opt.hidden_dim, opt.polarities_dim)
self.num_labels = opt.num_labels
self.classifier = nn.Linear(opt.hidden_dim, opt.num_labels)
def get_batch_token_labels_bert_base_indices(self, labels):
if labels is None:
return
# convert tags of BERT-SPC input to BERT-BASE format
labels = labels.detach().cpu().numpy()
for text_i in range(len(labels)):
sep_index = np.argmax((labels[text_i] == 5))
labels[text_i][sep_index + 1:] = 0
return torch.tensor(labels).to(self.bert4global.device)
def get_ids_for_local_context_extractor(self, text_indices):
# convert BERT-SPC input to BERT-BASE format
text_ids = text_indices.detach().cpu().numpy()
for text_i in range(len(text_ids)):
sep_index = np.argmax((text_ids[text_i] == self.opt.sep_indices))
text_ids[text_i][sep_index + 1:] = 0
return torch.tensor(text_ids).to(self.bert4global.device)
def forward(self, input_ids_spc,
token_type_ids=None,
attention_mask=None,
labels=None,
polarity=None,
valid_ids=None,
attention_mask_label=None,
lcf_cdm_vec=None,
lcf_cdw_vec=None
):
lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None
lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None
if not self.opt.use_bert_spc:
input_ids = self.get_ids_for_local_context_extractor(input_ids_spc)
labels = self.get_batch_token_labels_bert_base_indices(labels)
global_context_out = self.bert4global(input_ids=input_ids, attention_mask=attention_mask)['last_hidden_state']
else:
global_context_out = self.bert4global(input_ids=input_ids_spc, attention_mask=attention_mask)['last_hidden_state']
batch_size, max_len, feat_dim = global_context_out.shape
global_valid_output = torch.zeros(batch_size, max_len, feat_dim, dtype=torch.float32).to(self.bert4global.device)
for i in range(batch_size):
jj = -1
for j in range(max_len):
if valid_ids[i][j].item() == 1:
jj += 1
global_valid_output[i][jj] = global_context_out[i][j]
global_context_out = self.dropout(global_valid_output)
ate_logits = self.classifier(global_context_out)
if lcf_cdm_vec is not None or lcf_cdw_vec is not None:
local_context_ids = self.get_ids_for_local_context_extractor(input_ids_spc)
local_context_out = self.bert4local(input_ids=local_context_ids)['last_hidden_state']
batch_size, max_len, feat_dim = local_context_out.shape
local_valid_output = torch.zeros(batch_size, max_len, feat_dim, dtype=torch.float32).to(self.bert4global.device)
for i in range(batch_size):
jj = -1
for j in range(max_len):
if valid_ids[i][j].item() == 1:
jj += 1
local_valid_output[i][jj] = local_context_out[i][j]
local_context_out = self.dropout(local_valid_output)
if 'cdm' in self.opt.lcf:
cdm_context_out = torch.mul(local_context_out, lcf_cdm_vec)
cdm_context_out = self.SA1(cdm_context_out)
cat_out = torch.cat((global_context_out, cdm_context_out), dim=-1)
cat_out = self.linear_double(cat_out)
elif 'cdw' in self.opt.lcf:
cdw_context_out = torch.mul(local_context_out, lcf_cdw_vec)
cdw_context_out = self.SA1(cdw_context_out)
cat_out = torch.cat((global_context_out, cdw_context_out), dim=-1)
cat_out = self.linear_double(cat_out)
elif 'fusion' in self.opt.lcf:
cdm_context_out = torch.mul(local_context_out, lcf_cdm_vec)
cdw_context_out = torch.mul(local_context_out, lcf_cdw_vec)
cat_out = torch.cat((global_context_out, cdw_context_out, cdm_context_out), dim=-1)
cat_out = self.linear_triple(cat_out)
sa_out = self.SA2(cat_out)
pooled_out = self.pooler(sa_out)
pooled_out = self.dropout(pooled_out)
apc_logits = self.dense(pooled_out)
else:
apc_logits = None
if labels is not None:
criterion_ate = CrossEntropyLoss(ignore_index=0)
criterion_apc = CrossEntropyLoss(ignore_index=SENTIMENT_PADDING)
loss_ate = criterion_ate(ate_logits.view(-1, self.num_labels), labels.view(-1))
loss_apc = criterion_apc(apc_logits, polarity)
return loss_ate, loss_apc
else:
return ate_logits, apc_logits