# @Author : bamtercelboo
# @Datetime : 2018/8/17 16:06
# @File : BiLSTM.py
# @Last Modify Time : 2018/8/17 16:06
# @Contact : bamtercelboo@{gmail.com, 163.com}
"""
FILE : BiLSTM.py
FUNCTION : None
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import random
from DataUtils.Common import *
from models.initialize import *
from models.modelHelp import prepare_pack_padded_sequence
torch.manual_seed(seed_num)
random.seed(seed_num)
class BiLSTM(nn.Module):
"""
BiLSTM
"""
def __init__(self, **kwargs):
super(BiLSTM, self).__init__()
for k in kwargs:
self.__setattr__(k, kwargs[k])
V = self.embed_num
D = self.embed_dim
C = self.label_num
paddingId = self.paddingId
self.embed = nn.Embedding(V, D, padding_idx=paddingId)
if self.pretrained_embed:
self.embed.weight.data.copy_(self.pretrained_weight)
else:
init_embedding(self.embed.weight)
self.dropout_embed = nn.Dropout(self.dropout_emb)
self.dropout = nn.Dropout(self.dropout)
self.bilstm = nn.LSTM(input_size=D, hidden_size=self.lstm_hiddens, num_layers=self.lstm_layers,
bidirectional=True, batch_first=True, bias=True)
self.linear = nn.Linear(in_features=self.lstm_hiddens * 2, out_features=C, bias=True)
init_linear(self.linear)
def forward(self, word, sentence_length):
"""
:param word:
:param sentence_length:
:param desorted_indices:
:return:
"""
word, sentence_length, desorted_indices = prepare_pack_padded_sequence(word, sentence_length, device=self.device)
x = self.embed(word) # (N,W,D)
x = self.dropout_embed(x)
packed_embed = pack_padded_sequence(x, sentence_length, batch_first=True)
x, _ = self.bilstm(packed_embed)
x, _ = pad_packed_sequence(x, batch_first=True)
x = x[desorted_indices]
x = self.dropout(x)
x = torch.tanh(x)
logit = self.linear(x)
return logit
