# -*- coding: utf-8 -*-
# file: local_sentiment_aggregation.py
# time: 06/06/2022
# author: YANG, HENG <hy345@exeter.ac.uk> (杨恒)
# github: https://github.com/yangheng95
# Copyright (C) 2021. All Rights Reserved.
import torch
from pyabsa.networks.sa_encoder import Encoder
from torch import nn
from pyabsa.utils.pyabsa_utils import fprint
[docs]
class LSA(nn.Module):
def __init__(self, bert, config):
super(LSA, self).__init__()
self.config = config
self.encoder = Encoder(bert.config, config)
self.encoder_left = Encoder(bert.config, config)
self.encoder_right = Encoder(bert.config, config)
self.linear_window_3h = nn.Linear(config.embed_dim * 3, config.embed_dim)
self.linear_window_2h = nn.Linear(config.embed_dim * 2, config.embed_dim)
self.eta1 = nn.Parameter(torch.tensor(self.config.eta, dtype=torch.float))
self.eta2 = nn.Parameter(torch.tensor(self.config.eta, dtype=torch.float))
[docs]
def forward(
self,
global_context_features,
spc_mask_vec,
lcf_matrix,
left_lcf_matrix,
right_lcf_matrix,
):
masked_global_context_features = torch.mul(
spc_mask_vec, global_context_features
)
# # --------------------------------------------------- #
lcf_features = torch.mul(global_context_features, lcf_matrix)
lcf_features = self.encoder(lcf_features)
# # --------------------------------------------------- #
left_lcf_features = torch.mul(masked_global_context_features, left_lcf_matrix)
left_lcf_features = self.encoder_left(left_lcf_features)
# # --------------------------------------------------- #
right_lcf_features = torch.mul(masked_global_context_features, right_lcf_matrix)
right_lcf_features = self.encoder_right(right_lcf_features)
# # --------------------------------------------------- #
if "lr" == self.config.window or "rl" == self.config.window:
if self.eta1 <= 0 and self.config.eta != -1:
torch.nn.init.uniform_(self.eta1)
fprint("reset eta1 to: {}".format(self.eta1.item()))
if self.eta2 <= 0 and self.config.eta != -1:
torch.nn.init.uniform_(self.eta2)
fprint("reset eta2 to: {}".format(self.eta2.item()))
if self.config.eta >= 0:
cat_features = torch.cat(
(
lcf_features,
self.eta1 * left_lcf_features,
self.eta2 * right_lcf_features,
),
-1,
)
else:
cat_features = torch.cat(
(lcf_features, left_lcf_features, right_lcf_features), -1
)
sent_out = self.linear_window_3h(cat_features)
elif "l" == self.config.window:
sent_out = self.linear_window_2h(
torch.cat((lcf_features, self.eta1 * left_lcf_features), -1)
)
elif "r" == self.config.window:
sent_out = self.linear_window_2h(
torch.cat((lcf_features, self.eta2 * right_lcf_features), -1)
)
else:
raise KeyError("Invalid parameter:", self.config.window)
return sent_out