def embed_sent(self,
x: sent.Sentence) -> expression_seqs.ExpressionSequence:
# TODO refactor: seems a bit too many special cases that need to be distinguished
batched = batchers.is_batched(x)
first_sent = x[0] if batched else x
if hasattr(first_sent, "get_array"):
if not batched:
return expression_seqs.LazyNumpyExpressionSequence(
lazy_data=x.get_array())
else:
return expression_seqs.LazyNumpyExpressionSequence(
lazy_data=batchers.mark_as_batch([s for s in x]),
mask=x.mask)
else:
if not batched:
embeddings = [self.embed(word) for word in x]
else:
embeddings = []
for word_i in range(x.sent_len()):
embeddings.append(
self.embed(
batchers.mark_as_batch(
[single_sent[word_i] for single_sent in x])))
return expression_seqs.ExpressionSequence(expr_list=embeddings,
mask=x.mask)
def embed_speech_sent(self, x):
# TODO refactor: seems a bit too many special cases that need to be distinguished
# x = x.batches[0]
batched = batchers.is_batched(x)
first_sent = x[0] if batched else x
if hasattr(first_sent, "get_array"):
if not batched:
return expression_seqs.LazyNumpyExpressionSequence(
lazy_data=x.get_array())
else:
return expression_seqs.LazyNumpyExpressionSequence(
lazy_data=batchers.mark_as_batch([s for s in x]),
mask=x.mask)
else:
raise ValueError("!! Expected to use above")
return expression_seqs.ExpressionSequence(expr_list=embeddings,
mask=x.mask)