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dlcf_dca_bert.py
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# -*- coding: utf-8 -*-
# file: apc_utils.py
# time: 2021/5/23 0023
# author: xumayi <xumayi@m.scnu.edu.cn>
# github: https://github.com/XuMayi
# Copyright (C) 2021. All Rights Reserved.
import torch
import torch.nn as nn
from transformers.models.bert.modeling_bert import BertPooler
from pyabsa.network.sa_encoder import Encoder
def dependency_hidden(bert_local_out, depend, depended):
depend_out = bert_local_out.clone()
depended_out = bert_local_out.clone()
for i in range(bert_local_out.size()[0]):
for j in range(1, bert_local_out.size()[1]):
if j - 1 not in depend[i]:
depend_out[i][j] = depend_out[i][j] * 0
for i in range(bert_local_out.size()[0]):
for j in range(1, bert_local_out.size()[1]):
if j - 1 not in depended[i]:
depended_out[i][j] = depended_out[i][j] * 0
return depend_out, depended_out
def weight_distrubute_local(bert_local_out, depend_weight, depended_weight, depend, depended, opt, no_connect):
bert_local_out2 = torch.zeros_like(bert_local_out)
for j in range(depend.size()[0]):
bert_local_out2[j][0] = bert_local_out[j][0]
for j in range(depend.size()[0]):
for i in range(depend.size()[1]):
if depend[j][i] != -1 and (depend[j][i] + 1) < opt.max_seq_len:
bert_local_out2[j][depend[j][i] + 1] = depend_weight[j].item() * bert_local_out[j][depend[j][i] + 1]
for j in range(depended.size()[0]):
for i in range(depended.size()[1]):
if depended[j][i] != -1 and (depended[j][i] + 1) < opt.max_seq_len:
bert_local_out2[j][depended[j][i] + 1] = depended_weight[j].item() * bert_local_out[j][
depended[j][i] + 1]
for j in range(no_connect.size()[0]):
for i in range(no_connect.size()[1]):
if no_connect[j][i] != -1 and (no_connect[j][i] + 1) < opt.max_seq_len:
bert_local_out2[j][no_connect[j][i] + 1] = 0
return bert_local_out2
class PointwiseFeedForward(nn.Module):
''' A two-feed-forward-layer module '''
def __init__(self, d_hid, d_inner_hid=None, d_out=None, dropout=0):
super(PointwiseFeedForward, self).__init__()
if d_inner_hid is None:
d_inner_hid = d_hid
if d_out is None:
d_out = d_inner_hid
self.w_1 = nn.Conv1d(d_hid, d_inner_hid, 1) # position-wise
self.w_2 = nn.Conv1d(d_inner_hid, d_out, 1) # position-wise
self.dropout = nn.Dropout(dropout)
self.relu = nn.ReLU()
def forward(self, x):
output = self.relu(self.w_1(x.transpose(1, 2)))
output = self.w_2(output).transpose(2, 1)
output = self.dropout(output)
return output
class DLCF_DCA_BERT(nn.Module):
inputs = ['text_bert_indices', 'text_raw_bert_indices', 'dlcf_vec', 'depend_ids', 'depended_ids', 'no_connect']
def __init__(self, bert, opt):
super(DLCF_DCA_BERT, self).__init__()
self.bert4global = bert
self.bert4local = self.bert4global
self.hidden = opt.embed_dim
self.opt = opt
self.opt.bert_dim = opt.embed_dim
self.dropout = nn.Dropout(opt.dropout)
self.bert_SA_ = Encoder(bert.config, opt)
self.mean_pooling_double = PointwiseFeedForward(self.hidden * 2, self.hidden, self.hidden)
self.bert_pooler = BertPooler(bert.config)
self.dense = nn.Linear(self.hidden, opt.polarities_dim)
self.dca_sa = nn.ModuleList()
self.dca_pool = nn.ModuleList()
self.dca_lin = nn.ModuleList()
for i in range(opt.dca_layer):
self.dca_sa.append(Encoder(bert.config, opt))
self.dca_pool.append(BertPooler(bert.config))
self.dca_lin.append(nn.Sequential(
nn.Linear(opt.bert_dim, opt.bert_dim * 2),
nn.GELU(),
nn.Linear(opt.bert_dim * 2, 1),
nn.Sigmoid())
)
def weight_calculate(self, sa, pool, lin, d_w, ded_w, depend_out, depended_out):
depend_sa_out = sa(depend_out)
depend_sa_out = self.dropout(depend_sa_out)
depended_sa_out = sa(depended_out)
depended_sa_out = self.dropout(depended_sa_out)
depend_pool_out = pool(depend_sa_out)
depend_pool_out = self.dropout(depend_pool_out)
depended_pool_out = pool(depended_sa_out)
depended_pool_out = self.dropout(depended_pool_out)
depend_weight = lin(depend_pool_out)
depend_weight = self.dropout(depend_weight)
depended_weight = lin(depended_pool_out)
depended_weight = self.dropout(depended_weight)
for i in range(depend_weight.size()[0]):
depend_weight[i] = depend_weight[i].item() * d_w[i].item()
depended_weight[i] = depended_weight[i].item() * ded_w[i].item()
weight_sum = depend_weight[i].item() + depended_weight[i].item()
if weight_sum != 0:
depend_weight[i] = (2 * depend_weight[i] / weight_sum) ** self.opt.dca_p
if depend_weight[i] > 2:
depend_weight[i] = 2
depended_weight[i] = (2 * depended_weight[i] / weight_sum) ** self.opt.dca_p
if depended_weight[i] > 2:
depended_weight[i] = 2
else:
depend_weight[i] = 1
depended_weight[i] = 1
return depend_weight, depended_weight
def forward(self, inputs):
if self.opt.use_bert_spc:
text_bert_indices = inputs[0]
else:
text_bert_indices = inputs[1]
text_local_indices = inputs[1]
lcf_matrix = inputs[2]
depend = inputs[3]
depended = inputs[4]
no_connect = inputs[5]
global_context_features = self.bert4global(text_bert_indices)['last_hidden_state']
local_context_features = self.bert4local(text_local_indices)['last_hidden_state']
bert_local_out = torch.mul(local_context_features, lcf_matrix)
depend_weight = torch.ones(bert_local_out.size()[0])
depended_weight = torch.ones(bert_local_out.size()[0])
for i in range(self.opt.dca_layer):
depend_out, depended_out = dependency_hidden(bert_local_out, depend, depended)
depend_weight, depended_weight = self.weight_calculate(self.dca_sa[i], self.dca_pool[i], self.dca_lin[i],
depend_weight, depended_weight, depend_out,
depended_out)
bert_local_out = weight_distrubute_local(bert_local_out, depend_weight, depended_weight, depend, depended,
self.opt, no_connect)
out_cat = torch.cat((bert_local_out, global_context_features), dim=-1)
out_cat = self.mean_pooling_double(out_cat)
out_cat = self.bert_SA_(out_cat)
out_cat = self.bert_pooler(out_cat)
dense_out = self.dense(out_cat)
return dense_out