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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
class Net(nn.Module):
def __init__(self,
statement_vocab_dim,
subject_vocab_dim,
speaker_vocab_dim,
speaker_pos_vocab_dim,
state_vocab_dim,
party_vocab_dim,
context_vocab_dim,
statement_embed_dim = 100,
statement_kernel_num = 14,
statement_kernel_size = [3, 4, 5],
subject_embed_dim = 5,
subject_lstm_nlayers = 2,
subject_lstm_bidirectional = True,
subject_hidden_dim = 5,
speaker_embed_dim = 5,
speaker_pos_embed_dim = 10,
speaker_pos_lstm_nlayers = 2,
speaker_pos_lstm_bidirectional = True,
speaker_pos_hidden_dim = 5,
state_embed_dim = 5,
party_embed_dim = 5,
context_embed_dim = 20,
context_lstm_nlayers = 2,
context_lstm_bidirectional = True,
context_hidden_dim = 6,
dropout = 0.5):
# Statement CNN
super(Net, self).__init__()
self.statement_vocab_dim = statement_vocab_dim
self.statement_embed_dim = statement_embed_dim
self.statement_kernel_num = statement_kernel_num
self.statement_kernel_size = statement_kernel_size
self.statement_embedding = nn.Embedding(self.statement_vocab_dim, self.statement_embed_dim)
self.statement_convs = [nn.Conv2d(1, self.statement_kernel_num, (kernel_, self.statement_embed_dim)) for kernel_ in self.statement_kernel_size]
# Subject
self.subject_vocab_dim = subject_vocab_dim
self.subject_embed_dim = subject_embed_dim
self.subject_lstm_nlayers = subject_lstm_nlayers
self.subject_lstm_num_direction = 2 if subject_lstm_bidirectional else 1
self.subject_hidden_dim = subject_hidden_dim
self.subject_embedding = nn.Embedding(self.subject_vocab_dim, self.subject_embed_dim)
self.subject_lstm = nn.LSTM(
input_size = self.subject_embed_dim,
hidden_size = self.subject_hidden_dim,
num_layers = self.subject_lstm_nlayers,
batch_first = True,
bidirectional = subject_lstm_bidirectional
)
# Speaker
self.speaker_vocab_dim = speaker_vocab_dim
self.speaker_embed_dim = speaker_embed_dim
self.speaker_embedding = nn.Embedding(self.speaker_vocab_dim, self.speaker_embed_dim)
# Speaker Position
self.speaker_pos_vocab_dim = speaker_pos_vocab_dim
self.speaker_pos_embed_dim = speaker_pos_embed_dim
self.speaker_pos_lstm_nlayers = speaker_pos_lstm_nlayers
self.speaker_pos_lstm_num_direction = 2 if speaker_pos_lstm_bidirectional else 1
self.speaker_pos_hidden_dim = speaker_pos_hidden_dim
self.speaker_pos_embedding = nn.Embedding(self.speaker_pos_vocab_dim, self.speaker_pos_embed_dim)
self.speaker_pos_lstm = nn.LSTM(
input_size = self.speaker_pos_embed_dim,
hidden_size = self.speaker_pos_hidden_dim,
num_layers = self.speaker_pos_lstm_nlayers,
batch_first = True,
bidirectional = speaker_pos_lstm_bidirectional
)
# State
self.state_vocab_dim = state_vocab_dim
self.state_embed_dim = state_embed_dim
self.state_embedding = nn.Embedding(self.state_vocab_dim, self.state_embed_dim)
# Party
self.party_vocab_dim = party_vocab_dim
self.party_embed_dim = party_embed_dim
self.party_embedding = nn.Embedding(self.party_vocab_dim, self.party_embed_dim)
# Context
self.context_vocab_dim = context_vocab_dim
self.context_embed_dim = context_embed_dim
self.context_lstm_nlayers = context_lstm_nlayers
self.context_lstm_num_direction = 2 if context_lstm_bidirectional else 1
self.context_hidden_dim = context_hidden_dim
self.context_embedding = nn.Embedding(self.context_vocab_dim, self.context_embed_dim)
self.context_lstm = nn.LSTM(
input_size = self.context_embed_dim,
hidden_size = self.context_hidden_dim,
num_layers = self.context_lstm_nlayers,
batch_first = True,
bidirectional = context_lstm_bidirectional
)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(len(self.statement_kernel_size) * self.statement_kernel_num
+ self.subject_lstm_nlayers * self.subject_lstm_num_direction
+ self.speaker_embed_dim
+ self.speaker_pos_lstm_nlayers * self.speaker_pos_lstm_num_direction
+ self.state_embed_dim
+ self.party_embed_dim
+ self.context_lstm_nlayers * self.context_lstm_num_direction,
6)
def forward(self,
sample):
statement = Variable(sample.statement).unsqueeze(0)
subject = Variable(sample.subject).unsqueeze(0)
speaker = Variable(sample.speaker).unsqueeze(0)
speaker_pos = Variable(sample.speaker_pos).unsqueeze(0)
state = Variable(sample.state).unsqueeze(0)
party = Variable(sample.party).unsqueeze(0)
context = Variable(sample.context).unsqueeze(0)
batch = 1 # Current support one sample per time
# TODO: Increase batch number
# Statement
statement_ = self.statement_embedding(statement).unsqueeze(0) # 1*W*D -> 1*1*W*D
statement_ = [F.relu(conv(statement_)).squeeze(3) for conv in self.statement_convs] # 1*1*W*1 -> 1*Co*W x [len(convs)]
statement_ = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in statement_] # 1*Co*1 -> 1*Co x len(convs)
statement_ = torch.cat(statement_, 1) # 1*len(convs)
# Subject
subject_ = self.subject_embedding(subject) # 1*W*D
_, (subject_, _) = self.subject_lstm(subject_) # 1*(layer x dir)*hidden
subject_ = F.max_pool1d(subject_, self.subject_hidden_dim).view(1, -1) # 1*(layer x dir)*1 -> 1*(layer x dir)
# Speaker
speaker_ = self.speaker_embedding(speaker).squeeze(0) # 1*1*D -> 1*D
# Speaker Position
speaker_pos_ = self.speaker_pos_embedding(speaker_pos)
_, (speaker_pos_, _) = self.speaker_pos_lstm(speaker_pos_)
speaker_pos_ = F.max_pool1d(speaker_pos_, self.speaker_pos_hidden_dim).view(1, -1)
# State
state_ = self.state_embedding(state).squeeze(0)
# Party
party_ = self.party_embedding(party).squeeze(0)
# Context
context_ = self.context_embedding(context)
_, (context_, _) = self.context_lstm(context_)
context_ = F.max_pool1d(context_, self.context_hidden_dim).view(1, -1)
# Concatenate
features = torch.cat((statement_, subject_, speaker_, speaker_pos_, state_, party_, context_), 1)
features = self.dropout(features)
features = self.fc(features)
return features