def get_lstm_states(self, gaz_list, reverse_gaz_list, word_ids, word_masks,
lengths):
"""
:param gaz_list:
:param reverse_gaz_list:
:param word_ids:
:param word_masks:
:param lengths:
:return:
"""
batch_size = word_masks.size(0)
seq_len = word_masks.size(1)
## 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_ids)
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), lengths)
return lstm_outs
def forward(self, inputs, lengths):
f_cnn_out, b_cnn_out = self.get_cnn_features(inputs)
rb_cnn_out = reverse_padded_sequence(b_cnn_out, lengths)
cnn_out = torch.cat((f_cnn_out, rb_cnn_out),
dim=-1) # [N, M, 2 * out_size]
return cnn_out, (f_cnn_out, b_cnn_out)
def forward(self, inputs, lengths):
"""
"""
f_lstm_out, b_lstm_out = self.get_lstm_features(inputs)
# lengths = list(map(int, word_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)