def __len__(self):
if self.expr_list or self.expr_tensor:
return super(LazyNumpyExpressionSequence, self).__len__()
else:
if Batcher.is_batched(self.lazy_data):
return self.lazy_data[0].shape[0]
else:
return self.lazy_data.shape[0]
def __getitem__(self, key):
if self.expr_list or self.expr_tensor:
return super(LazyNumpyExpressionSequence, self).__getitem__(key)
else:
if Batcher.is_batched(self.lazy_data):
return dy.inputTensor([
self.lazy_data[batch][key]
for batch in range(len(self.lazy_data))
],
batched=True)
else:
return dy.inputTensor(self.lazy_data[key], batched=False)
def as_tensor(self):
if not (self.expr_list or self.expr_tensor):
self.expr_tensor = dy.inputTensor(self.lazy_data,
batched=Batcher.is_batched(
self.lazy_data))
return super(LazyNumpyExpressionSequence, self).as_tensor()
