Source code for pyabsa.tasks.AspectPolarityClassification.models.__classic__.tc_lstm

# -*- coding: utf-8 -*-
# file: tc_lstm.py
# author: songyouwei <youwei0314@gmail.com>, ZhangYikai <zykhelloha@gmail.com>
# Copyright (C) 2018. All Rights Reserved.

import torch
import torch.nn as nn

from pyabsa.networks.dynamic_rnn import DynamicLSTM




class TC_LSTM(nn.Module):


    inputs = ["left_with_aspect_indices", "right_with_aspect_indices", "aspect_indices"]


    def __init__(self, embedding_matrix, config):
        super(TC_LSTM, self).__init__()
        self.embed = nn.Embedding.from_pretrained(
            torch.tensor(embedding_matrix, dtype=torch.float)
        )
        self.lstm_l = DynamicLSTM(
            config.embed_dim * 2, config.hidden_dim, num_layers=1, batch_first=True
        )
        self.lstm_r = DynamicLSTM(
            config.embed_dim * 2, config.hidden_dim, num_layers=1, batch_first=True
        )
        self.dense = nn.Linear(config.hidden_dim * 2, config.output_dim)



    def forward(self, inputs):
        # Get the target and its length(target_len)
        x_l, x_r, target = (
            inputs["left_with_aspect_indices"],
            inputs["right_with_aspect_indices"],
            inputs["aspect_indices"],
        )
        x_l_len, x_r_len = torch.sum(x_l != 0, dim=-1), torch.sum(x_r != 0, dim=-1)
        target_len = torch.sum(target != 0, dim=-1, dtype=torch.float)[:, None, None]
        x_l, x_r, target = self.embed(x_l), self.embed(x_r), self.embed(target)
        v_target = torch.div(
            target.sum(dim=1, keepdim=True), target_len
        )  # v_{target} in paper: average the target words

        # the concatenation of word embedding and target vector v_{target}:
        x_l = torch.cat((x_l, torch.cat(([v_target] * x_l.shape[1]), 1)), 2)
        x_r = torch.cat((x_r, torch.cat(([v_target] * x_r.shape[1]), 1)), 2)

        _, (h_n_l, _) = self.lstm_l(x_l, x_l_len)
        _, (h_n_r, _) = self.lstm_r(x_r, x_r_len)
        h_n = torch.cat((h_n_l[0], h_n_r[0]), dim=-1)
        out = self.dense(h_n)
        return {"logits": out}