112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import six
|
|
from .sampler import SequentialSampler, RandomSampler, BatchSampler
|
|
|
|
|
|
class DataCargo(object):
|
|
def __init__(self,
|
|
dataset,
|
|
batch_fn=None,
|
|
batch_size=1,
|
|
sampler=None,
|
|
shuffle=False,
|
|
batch_sampler=None,
|
|
drop_last=False):
|
|
self.dataset = dataset
|
|
self.batch_fn = batch_fn or self.dataset._batch_examples
|
|
|
|
if batch_sampler is not None:
|
|
# auto_collation with custom batch_sampler
|
|
if batch_size != 1 or shuffle or sampler is not None or drop_last:
|
|
raise ValueError('batch_sampler option is mutually exclusive '
|
|
'with batch_size, shuffle, sampler, and '
|
|
'drop_last')
|
|
batch_size = None
|
|
drop_last = False
|
|
shuffle = False
|
|
elif batch_size is None:
|
|
raise ValueError(
|
|
'batch sampler is none. then batch size must not be none.')
|
|
elif sampler is None:
|
|
if shuffle:
|
|
sampler = RandomSampler(dataset)
|
|
else:
|
|
sampler = SequentialSampler(dataset)
|
|
batch_sampler = BatchSampler(sampler, batch_size, drop_last)
|
|
else:
|
|
batch_sampler = BatchSampler(sampler, batch_size, drop_last)
|
|
|
|
self.batch_size = batch_size
|
|
self.drop_last = drop_last
|
|
self.sampler = sampler
|
|
|
|
self.batch_sampler = batch_sampler
|
|
|
|
def __iter__(self):
|
|
return DataIterator(self)
|
|
|
|
def __call__(self):
|
|
return DataIterator(self)
|
|
|
|
@property
|
|
def _auto_collation(self):
|
|
# we will auto batching
|
|
return self.batch_sampler is not None
|
|
|
|
@property
|
|
def _index_sampler(self):
|
|
if self._auto_collation:
|
|
return self.batch_sampler
|
|
else:
|
|
return self.sampler
|
|
|
|
def __len__(self):
|
|
return len(self._index_sampler)
|
|
|
|
|
|
class DataIterator(object):
|
|
def __init__(self, loader):
|
|
self.loader = loader
|
|
self._dataset = loader.dataset
|
|
|
|
self._batch_fn = loader.batch_fn
|
|
self._index_sampler = loader._index_sampler
|
|
self._sampler_iter = iter(self._index_sampler)
|
|
|
|
def __iter__(self):
|
|
return self
|
|
|
|
def __next__(self):
|
|
|
|
index = self._next_index(
|
|
) # may raise StopIteration, TODO(chenfeiyu): use dynamic batch size
|
|
minibatch = [self._dataset[i] for i in index
|
|
] # we can abstract it, too to use dynamic batch size
|
|
minibatch = self._batch_fn(minibatch) # list[Example] -> Batch
|
|
return minibatch
|
|
|
|
next = __next__ # Python 2 compatibility
|
|
|
|
def _next_index(self):
|
|
if six.PY3:
|
|
return next(self._sampler_iter)
|
|
else:
|
|
# six.PY2
|
|
return self._sampler_iter.next()
|
|
|
|
def __len__(self):
|
|
return len(self._index_sampler)
|