def load_indexed_dataset(path,
dictionary=None,
dataset_impl=None,
combine=False,
default="cached"):
"""A helper function for loading indexed datasets.
Args:
path (str): path to indexed dataset (e.g., 'data-bin/train')
dictionary (~fairseq.data.Dictionary): data dictionary
dataset_impl (str, optional): which dataset implementation to use. If
not provided, it will be inferred automatically. For legacy indexed
data we use the 'cached' implementation by default.
combine (bool, optional): automatically load and combine multiple
datasets. For example, if *path* is 'data-bin/train', then we will
combine 'data-bin/train', 'data-bin/train1', ... and return a
single ConcatDataset instance.
"""
import fairseq.data.indexed_dataset as indexed_dataset
from fairseq.data.concat_dataset import ConcatDataset
datasets = []
for k in itertools.count():
path_k = path + (str(k) if k > 0 else "")
try:
path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
except Exception as e:
if "StorageException: [404] Path not found" in str(e):
logger.warning(f"path_k: {e} not found")
else:
raise e
dataset_impl_k = dataset_impl
if dataset_impl_k is None:
dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
dataset = indexed_dataset.make_dataset(
path_k,
impl=dataset_impl_k or default,
fix_lua_indexing=True,
dictionary=dictionary,
)
if dataset is None:
break
logger.info("loaded {:,} examples from: {}".format(
len(dataset), path_k))
datasets.append(dataset)
if not combine:
break
if len(datasets) == 0:
return None
elif len(datasets) == 1:
return datasets[0]
else:
return ConcatDataset(datasets)
def load_indexed_dataset(path,
dictionary,
dataset_impl=None,
combine=False,
default='cached'):
"""A helper function for loading indexed datasets.
Args:
path (str): path to indexed dataset (e.g., 'data-bin/train')
dictionary (~fairseq.data.Dictionary): data dictionary
dataset_impl (str, optional): which dataset implementation to use. If
not provided, it will be inferred automatically. For legacy indexed
data we use the 'cached' implementation by default.
combine (bool, optional): automatically load and combine multiple
datasets. For example, if *path* is 'data-bin/train', then we will
combine 'data-bin/train', 'data-bin/train1', ... and return a
single ConcatDataset instance.
"""
from fairseq.data.concat_dataset import ConcatDataset
import fairseq.data.indexed_dataset as indexed_dataset
datasets = []
for k in itertools.count(): ## 从0开始，无限加1遍历，用于存在多个训练集的情况
path_k = path + (str(k) if k > 0 else '') ##k=0时，名字不加入id，用于存在多个训练集的情况
dataset_impl_k = dataset_impl
if dataset_impl_k is None:
dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
#dataset_impl_k==lazy -->IndexedDataset类，
#调用make_dataset函数构建IndexedDataset对象，并读入数据索引相关信息，即读入.idx后缀的文件。
#之后可以如同list按id索引数据，每次索引都是直接从二进制文件读取
dataset = indexed_dataset.make_dataset(
path_k,
impl=dataset_impl_k or default,
fix_lua_indexing=True,
dictionary=dictionary,
)
if dataset is None:
break
print('| loaded {} examples from: {}'.format(len(dataset), path_k))
datasets.append(dataset)
if not combine:
break
if len(datasets) == 0:
return None
elif len(datasets) == 1:
return datasets[0]
else:
return ConcatDataset(datasets)
def load_indexed_dataset(path,
dictionary,
dataset_impl=None,
combine=False,
default='cached',
path_xml=None):
"""A helper function for loading indexed datasets.
Args:
path (str): path to indexed dataset (e.g., 'data-bin/train')
dictionary (~fairseq.data.Dictionary): data dictionary
dataset_impl (str, optional): which dataset implementation to use. If
not provided, it will be inferred automatically. For legacy indexed
data we use the 'cached' implementation by default.
combine (bool, optional): automatically load and combine multiple
datasets. For example, if *path* is 'data-bin/train', then we will
combine 'data-bin/train', 'data-bin/train1', ... and return a
single ConcatDataset instance.
"""
from fairseq.data.concat_dataset import ConcatDataset
import fairseq.data.indexed_dataset as indexed_dataset
datasets = []
for k in itertools.count():
path_k = path + (str(k) if k > 0 else '')
dataset_impl_k = dataset_impl
if dataset_impl_k is None:
dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
dataset = indexed_dataset.make_dataset(
path_k,
impl=dataset_impl_k or default,
fix_lua_indexing=True,
dictionary=dictionary,
path_xml=path_xml,
)
if dataset is None:
break
print('| loaded {} examples from: {}'.format(len(dataset), path_k))
datasets.append(dataset)
if not combine:
break
if len(datasets) == 0:
return None
elif len(datasets) == 1:
return datasets[0]
else:
return ConcatDataset(datasets)
