def generate(self,
src: Union[batchers.Batch, sent.Sentence],
forced_trg_ids: Optional[Sequence[numbers.Integral]] = None,
normalize_scores: bool = False):
if not batchers.is_batched(src):
src = batchers.mark_as_batch([src])
if forced_trg_ids:
forced_trg_ids = batchers.mark_as_batch([forced_trg_ids])
h = self._encode_src(src)
scores = self.scorer.calc_log_probs(
h) if normalize_scores else self.scorer.calc_scores(h)
np_scores = scores.npvalue()
if forced_trg_ids:
output_action = forced_trg_ids
else:
output_action = np.argmax(np_scores, axis=0)
outputs = []
for batch_i in range(src.batch_size()):
if src.batch_size() > 1:
my_action = output_action[batch_i]
score = np_scores[:, batch_i][my_action]
else:
my_action = output_action
score = np_scores[my_action]
outputs.append(sent.ScalarSentence(value=my_action, score=score))
return outputs
def read_sent(self, line: str, idx: numbers.Integral) -> sent.Sentence:
if self.vocab:
convert_fct = self.vocab.convert
else:
convert_fct = convert_int
if self.read_sent_len:
return sent.ScalarSentence(idx=idx, value=len(line.strip().split()))
else:
return sent.SimpleSentence(idx=idx,
words=[convert_fct(word) for word in line.strip().split()] + [vocabs.Vocab.ES],
vocab=self.vocab,
output_procs=self.output_procs)
def generate(self,
src: Union[batchers.Batch, sent.Sentence],
normalize_scores: bool = False):
if not batchers.is_batched(src):
src = batchers.mark_as_batch([src])
h = self._encode_src(src)
best_words, best_scores = self.scorer.best_k(h, k=1, normalize_scores=normalize_scores)
assert best_words.shape == (1, src.batch_size())
assert best_scores.shape == (1, src.batch_size())
outputs = []
for batch_i in range(src.batch_size()):
if src.batch_size() > 1:
word = best_words[0, batch_i]
score = best_scores[0, batch_i]
else:
word = best_words[0]
score = best_scores[0]
outputs.append(sent.ScalarSentence(value=word, score=score))
return outputs
def read_sent(self, line: str,
idx: numbers.Integral) -> sent.ScalarSentence:
return sent.ScalarSentence(idx=idx, value=int(line.strip()))
