def generate(
self,
src: batchers.Batch,
search_strategy: search_strategies.SearchStrategy,
forced_trg_ids: batchers.Batch = None) -> Sequence[sent.Sentence]:
"""
Takes in a batch of source sentences and outputs a list of search outputs.
Args:
src: The source sentences
search_strategy: The strategy with which to perform the search
forced_trg_ids: The target IDs to generate if performing forced decoding
Returns:
A list of search outputs including scores, etc.
"""
assert src.batch_size() == 1
search_outputs = self.generate_search_output(src, search_strategy,
forced_trg_ids)
if isinstance(src, batchers.CompoundBatch): src = src.batches[0]
sorted_outputs = sorted(search_outputs,
key=lambda x: x.score[0],
reverse=True)
assert len(sorted_outputs) >= 1
outputs = []
for curr_output in sorted_outputs:
output_actions = [x for x in curr_output.word_ids[0]]
attentions = [x for x in curr_output.attentions[0]]
score = curr_output.score[0]
out_sent = sent.SimpleSentence(
idx=src[0].idx,
words=output_actions,
vocab=getattr(self.trg_reader, "vocab", None),
output_procs=self.trg_reader.output_procs,
score=score)
if len(sorted_outputs) == 1:
outputs.append(out_sent)
else:
outputs.append(
sent.NbestSentence(base_sent=out_sent,
nbest_id=src[0].idx))
if self.is_reporting():
attentions = np.concatenate([x.npvalue() for x in attentions],
axis=1)
self.report_sent_info({
"attentions": attentions,
"src": src[0],
"output": outputs[0]
})
return outputs
def generate(
self, src: batchers.Batch,
search_strategy: search_strategies.SearchStrategy
) -> Sequence[sent.Sentence]:
"""
Takes in a batch of source sentences and outputs a list of search outputs.
Args:
src: The source sentences
search_strategy: The strategy with which to perform the search
Returns:
A list of search outputs including scores, etc.
"""
assert src.batch_size() == 1
event_trigger.start_sent(src)
search_outputs = self.generate_search_output(src, search_strategy)
if isinstance(src, batchers.CompoundBatch): src = src.batches[0]
sorted_outputs = sorted(search_outputs,
key=lambda x: x.score[0],
reverse=True)
assert len(sorted_outputs) >= 1
outputs = []
for curr_output in sorted_outputs:
output_actions = [x for x in curr_output.word_ids[0]]
attentions = [x for x in curr_output.attentions[0]]
score = curr_output.score[0]
out_sent = self._emit_translation(src, output_actions, score)
if len(sorted_outputs) == 1:
outputs.append(out_sent)
else:
outputs.append(
sent.NbestSentence(base_sent=out_sent,
nbest_id=src[0].idx))
if self.is_reporting():
attentions = np.concatenate([x.npvalue() for x in attentions],
axis=1)
self.report_sent_info({
"attentions": attentions,
"src": src[0],
"output": outputs[0]
})
return outputs