def forward(self, inputs, char_seq_length):
f_lstm_out, b_lstm_out = self.get_lstm_features(
inputs, char_seq_length)
lengths = list(map(int, char_seq_length))
rb_lstm_out = reverse_padded_sequence(b_lstm_out, lengths)
lstm_out = torch.cat((f_lstm_out, rb_lstm_out), dim=-1)
return lstm_out, (f_lstm_out, b_lstm_out)
def forward(self, gaz_list, reverse_gaz_list, word_inputs, word_seq_lengths, mask):
"""
Args:
gaz_list: the forward gaz_list
reverse_gaz_list: the backward gaz list
word_inputs: word ids
word_seq_lengths: each sentence length
mask: sentence mask
"""
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
lengths = list(map(int, word_seq_lengths))
## get batch gaz ids
batch_gaz_ids, batch_gaz_length, batch_gaz_mask = get_batch_gaz(reverse_gaz_list, batch_size, seq_len, self.gpu)
reverse_batch_gaz_ids, reverse_batch_gaz_length, reverse_batch_gaz_mask = get_batch_gaz(gaz_list, batch_size, seq_len, self.gpu)
reverse_batch_gaz_ids = reverse_padded_sequence(reverse_batch_gaz_ids, lengths)
reverse_batch_gaz_length = reverse_padded_sequence(reverse_batch_gaz_length, lengths)
reverse_batch_gaz_mask = reverse_padded_sequence(reverse_batch_gaz_mask, lengths)
## word embedding
word_embs = self.word_embedding(word_inputs)
reverse_word_embs = reverse_padded_sequence(word_embs, lengths)
## gaz embedding
gaz_embs = self.gaz_embed((batch_gaz_ids, batch_gaz_length, batch_gaz_mask))
reverse_gaz_embs = self.gaz_embed((reverse_batch_gaz_ids, reverse_batch_gaz_length, reverse_batch_gaz_mask))
## lstm
forward_inputs = torch.cat((word_embs, gaz_embs), dim=-1)
backward_inputs = torch.cat((reverse_word_embs, reverse_gaz_embs), dim=-1)
lstm_outs, _ = self.lstm((forward_inputs, backward_inputs), word_seq_lengths)
## hidden2tag
outs = self.hidden2tag(lstm_outs)
## crf and loss
_, tag_seq = self.crf._viterbi_decode(outs, mask)
return tag_seq
def forward(self, char_inputs, char_seq_lengths, mask):
batch_size = char_inputs.size(0)
sent_len = char_inputs.size(1)
lengths = list(map(int, char_seq_lengths))
reverse_char_inputs = reverse_padded_sequence(char_inputs, lengths)
char_emb = self.char_embedding(char_inputs)
reverse_char_emb = self.char_embedding(reverse_char_inputs)
lstm_out, _ = self.lstm((char_emb, reverse_char_emb), char_seq_lengths)
# softmax
outs = self.hidden2tag(lstm_out)
# CRF
_, tag_seq = self.crf._viterbi_decode(outs, mask)
return tag_seq
def neg_log_likelihood_loss(self, char_inputs, char_seq_lengths, batch_label, mask):
batch_size = char_inputs.size(0)
sent_len = char_inputs.size(1)
lengths = list(map(int, char_seq_lengths))
reverse_char_inputs = reverse_padded_sequence(char_inputs, lengths)
char_emb = self.char_embedding(char_inputs)
reverse_char_emb = self.char_embedding(reverse_char_inputs)
lstm_out, _ = self.lstm((char_emb, reverse_char_emb), char_seq_lengths)
# softmax
outs = self.hidden2tag(lstm_out)
# CRF
## crf and loss
loss = self.crf.neg_log_likelihood_loss(outs, mask, batch_label)
_, tag_seq = self.crf._viterbi_decode(outs, mask)
return loss, tag_seq