def get_hidden(self, examples):
args = self.args
if not isinstance(examples, list):
examples = [examples]
input_dict = to_input_dict(
examples=examples,
vocab=self.vocab,
max_tgt_len=-1,
cuda=args.cuda,
training=self.training,
src_append=False,
tgt_append=True,
use_tgt=True,
use_tag=False,
use_dst=False,
)
src_var = input_dict['src']
tgt_var = input_dict['tgt']
src_length = input_dict['src_len']
encoder_outputs, encoder_hidden = self.encode(src_var=src_var,
src_length=src_length)
encoder_hidden = self.bridge(encoder_hidden)
return encoder_hidden
def score(self, examples, to_word=False, **kwargs):
args = self.args
if not isinstance(examples, list):
examples = [examples]
input_dict = to_input_dict(examples=examples,
vocab=self.vocab,
max_tgt_len=self.max_len,
cuda=args.cuda,
training=self.training,
use_tgt=True,
use_tag=args.use_arc,
use_dst=args.use_dst)
enc_out = self.encode(seqs_x=input_dict['src'],
seqs_length=input_dict['src_len'])
ret = self.decode(enc_out, ret_syn=True)
pos_word_score = self.decoder.scoring(raw_score=ret['word'],
tgt_var=input_dict['tgt'])
pos_syn_score = self.syntax_score(ret,
input_dict,
use_arc=args.use_arc,
use_dst=args.use_dst)
sum_score = pos_word_score + pos_syn_score
return sum_score
def score(self, examples, return_enc_state=False, **kwargs):
args = self.args
if not isinstance(examples, list):
examples = [examples]
input_dict = to_input_dict(
examples=examples,
vocab=self.vocab,
max_tgt_len=-1,
cuda=args.cuda,
training=self.training,
src_append=False,
tgt_append=True,
use_tgt=True,
use_tag=False,
use_dst=False,
)
src_var = input_dict['src']
tgt_var = input_dict['tgt']
src_length = input_dict['src_len']
encoder_outputs, encoder_hidden = self.encode(src_var=src_var,
src_length=src_length)
encoder_hidden = self.bridge(encoder_hidden)
scores = self.decoder.score(inputs=tgt_var,
encoder_hidden=encoder_hidden,
encoder_outputs=encoder_outputs)
if return_enc_state:
return scores, encoder_hidden
else:
return scores
def predict(self, examples, to_word=True):
args = self.args
if not isinstance(examples, list):
examples = [examples]
input_dict = to_input_dict(examples=examples,
vocab=self.vocab,
max_tgt_len=self.max_len,
cuda=args.cuda,
training=self.training,
use_tgt=False,
use_tag=False,
use_dst=False)
predict = self.forward(seqs_x=input_dict['src'],
x_length=input_dict['src_len'],
to_word=to_word)
return predict['pred']
def predict(self, examples, to_word=True):
args = self.args
if not isinstance(examples, list):
examples = [examples]
input_dict = to_input_dict(
examples=examples,
vocab=self.vocab,
max_tgt_len=-1,
cuda=args.cuda,
training=self.training,
src_append=False,
tgt_append=True,
use_tgt=False,
use_tag=False,
use_dst=False,
)
src_var = input_dict['src']
src_length = input_dict['src_len']
encoder_outputs, encoder_hidden = self.encode(src_var=src_var,
src_length=src_length)
encoder_hidden = self.bridge(encoder_hidden)
decoder_output, decoder_hidden, ret_dict, _ = self.decoder.forward(
encoder_hidden=encoder_hidden,
encoder_outputs=encoder_outputs,
teacher_forcing_ratio=0.0)
result = torch.stack(ret_dict['sequence']).squeeze()
final_result = []
if len(result.size()) < 2:
result = result.view(-1, 1)
example_nums = result.size(-1)
if to_word:
for i in range(example_nums):
hyp = result[:, i].data.tolist()
res = id2word(hyp, self.vocab.tgt)
seems = [[res], [len(res)]]
final_result.append(seems)
return final_result