如何直接从列表或dict创建torchtext.data.TabularDataset

Question

torchtext.data.TabularDataset可以从TSV/JSON/CSV文件创建，然后可以用于从Glove、FastText或任何其他嵌入构建词汇表。但我的要求是直接从list或dict创建torchtext.data.TabularDataset。

当前通过读取TSV文件实现的代码

self.RAW = data.RawField()
self.TEXT = data.Field(batch_first=True)
self.LABEL = data.Field(sequential=False, unk_token=None)

self.train, self.dev, self.test = data.TabularDataset.splits(
path='.data/quora',
train='train.tsv',
validation='dev.tsv',
test='test.tsv',
format='tsv',
fields=[('label', self.LABEL),
('q1', self.TEXT),
('q2', self.TEXT),
('id', self.RAW)])

self.TEXT.build_vocab(self.train, self.dev, self.test, vectors=GloVe(name='840B', dim=300))
self.LABEL.build_vocab(self.train)

sort_key = lambda x: data.interleave_keys(len(x.q1), len(x.q2))

self.train_iter, self.dev_iter, self.test_iter = 
data.BucketIterator.splits((self.train, self.dev, self.test),
batch_sizes=[args.batch_size] * 3,
device=args.gpu,
sort_key=sort_key)

这是用于从文件中读取数据的当前工作代码。因此，为了直接从List/Dict创建数据集，我尝试了Examples.fromDict或Examples.fromList等内置函数，但在进入最后一个for循环时，它抛出了一个错误AttributeError: 'BucketIterator' object has no attribute 'q1'

Answer 1

它要求我编写一个自己的类，继承Dataset类，并在torchtext.data.TabularDataset类中进行一些修改。

class TabularDataset_From_List(data.Dataset):
def __init__(self, input_list, format, fields, skip_header=False, **kwargs):
make_example = {
'json': Example.fromJSON, 'dict': Example.fromdict,
'tsv': Example.fromTSV, 'csv': Example.fromCSV}[format.lower()]
examples = [make_example(item, fields) for item in input_list]
if make_example in (Example.fromdict, Example.fromJSON):
fields, field_dict = [], fields
for field in field_dict.values():
if isinstance(field, list):
fields.extend(field)
else:
fields.append(field)
super(TabularDataset_From_List, self).__init__(examples, fields, **kwargs)
@classmethod
def splits(cls, path=None, root='.data', train=None, validation=None,
test=None, **kwargs):
if path is None:
path = cls.download(root)
train_data = None if train is None else cls(
train, **kwargs)
val_data = None if validation is None else cls(
validation, **kwargs)
test_data = None if test is None else cls(
test, **kwargs)
return tuple(d for d in (train_data, val_data, test_data)
if d is not None)