def load_dataset(self, split, epoch=1, combine=False):
"""Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
loaded_datasets = []
paths = utils.split_paths(self.args.data)
assert len(paths) > 0
data_path = paths[(epoch - 1) % len(paths)]
logger.info("data_path", data_path)
for k in itertools.count():
split_k = split + (str(k) if k > 0 else '')
path = os.path.join(data_path, split_k)
ds = indexed_dataset.make_dataset(
path,
impl=self.args.dataset_impl,
fix_lua_indexing=True,
dictionary=self.dictionary,
)
if ds is None:
if k > 0:
break
else:
raise FileNotFoundError(
'Dataset not found: {} ({})'.format(split, data_path))
with data_utils.numpy_seed(self.seed + k):
loaded_datasets.append(
BlockPairDataset(
ds,
self.dictionary,
ds.sizes,
self.args.tokens_per_sample,
break_mode=self.args.break_mode,
doc_break_size=1,
))
logger.info('{} {} {} examples都是非常重要的例子'.format(
data_path, split_k, len(loaded_datasets[-1])))
if not combine:
break
if len(loaded_datasets) == 1:
dataset = loaded_datasets[0]
sizes = dataset.sizes
else:
dataset = ConcatDataset(loaded_datasets)
sizes = np.concatenate([ds.sizes for ds in loaded_datasets])
self.datasets[split] = MaskedLMDataset(
dataset=dataset,
sizes=sizes,
vocab=self.dictionary,
pad_idx=self.dictionary.pad(),
mask_idx=self.dictionary.mask(),
classif_token_idx=self.dictionary.cls(),
sep_token_idx=self.dictionary.sep(),
shuffle=self.args.shuffle_dataset,
seed=self.seed,
)
def load_dataset(self, split, combine=False):
"""
Load a given dataset split.
Args:
split (str): name of the split (e.g., train, valid, test)
"""
loaded_datasets = []
for k in itertools.count():
split_k = split + (str(k) if k > 0 else '')
path = os.path.join(self.args.data, split_k)
if self.args.raw_text and IndexedRawTextDataset.exists(path):
ds = IndexedRawTextDataset(path, self.dictionary)
elif not self.args.raw_text and IndexedDataset.exists(path):
if self.args.lazy_load:
ds = IndexedDataset(path, fix_lua_indexing=True)
else:
ds = IndexedCachedDataset(path, fix_lua_indexing=True)
else:
if k > 0:
break
else:
raise FileNotFoundError(
'Dataset not found: {} ({})'.format(
split, self.args.data))
with data_utils.numpy_seed(self.seed + k):
loaded_datasets.append(
BlockPairDataset(
ds,
self.dictionary,
ds.sizes,
self.args.tokens_per_sample,
break_mode=self.args.break_mode,
))
logger.info('{} {} {} examples'.format(self.args.data, split_k,
len(loaded_datasets[-1])))
if not combine:
break
if len(loaded_datasets) == 1:
dataset = loaded_datasets[0]
sizes = dataset.sizes
else:
dataset = ConcatDataset(loaded_datasets)
sizes = np.concatenate([ds.sizes for ds in loaded_datasets])
self.datasets[split] = MaskedLMDataset(
dataset=dataset,
sizes=sizes,
vocab=self.dictionary,
pad_idx=self.dictionary.pad(),
mask_idx=self.dictionary.mask(),
classif_token_idx=self.dictionary.cls(),
sep_token_idx=self.dictionary.sep(),
shuffle=False,
seed=self.seed,
)