127 lines
4.9 KiB
Python
127 lines
4.9 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import six
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from .sampler import SequentialSampler, RandomSampler, BatchSampler
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class DataCargo(object):
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def __init__(self,
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dataset,
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batch_fn=None,
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batch_size=1,
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sampler=None,
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shuffle=False,
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batch_sampler=None,
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drop_last=False):
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"""An Iterable object of batches. It requires a dataset, a batch function and a sampler. The sampler yields the example ids, then the corresponding examples in the dataset are collected and transformed into a batch with the batch function.
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Args:
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dataset (Dataset): the dataset used to build a data cargo.
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batch_fn (callable, optional): a callable that takes a list of examples of `dataset` and return a batch, it can be None if the dataset has a `_batch_examples` method which satisfy the requirement. Defaults to None.
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batch_size (int, optional): number of examples in a batch. Defaults to 1.
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sampler (Sampler, optional): an iterable of example ids(intergers), the example ids are used to pick examples. Defaults to None.
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shuffle (bool, optional): when sampler is not provided, shuffle = True creates a RandomSampler and shuffle=False creates a SequentialSampler internally. Defaults to False.
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batch_sampler (BatchSampler, optional): an iterable of lists of example ids(intergers), the list is used to pick examples, `batch_sampler` option is mutually exclusive with `batch_size`, `shuffle`, `sampler`, and `drop_last`. Defaults to None.
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drop_last (bool, optional): whether to drop the last minibatch. Defaults to False.
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"""
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self.dataset = dataset
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self.batch_fn = batch_fn or self.dataset._batch_examples
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if batch_sampler is not None:
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# auto_collation with custom batch_sampler
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if batch_size != 1 or shuffle or sampler is not None or drop_last:
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raise ValueError('batch_sampler option is mutually exclusive '
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'with batch_size, shuffle, sampler, and '
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'drop_last')
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batch_size = None
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drop_last = False
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shuffle = False
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elif batch_size is None:
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raise ValueError(
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'batch sampler is none. then batch size must not be none.')
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elif sampler is None:
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if shuffle:
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sampler = RandomSampler(dataset)
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else:
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sampler = SequentialSampler(dataset)
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batch_sampler = BatchSampler(sampler, batch_size, drop_last)
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else:
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batch_sampler = BatchSampler(sampler, batch_size, drop_last)
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self.batch_size = batch_size
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self.drop_last = drop_last
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self.sampler = sampler
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self.batch_sampler = batch_sampler
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def __iter__(self):
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return DataIterator(self)
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def __call__(self):
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# protocol for paddle's DataLoader
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return DataIterator(self)
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@property
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def _auto_collation(self):
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# use auto batching
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return self.batch_sampler is not None
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@property
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def _index_sampler(self):
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if self._auto_collation:
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return self.batch_sampler
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else:
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return self.sampler
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def __len__(self):
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return len(self._index_sampler)
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class DataIterator(object):
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def __init__(self, loader):
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"""Iterator object of DataCargo.
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Args:
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loader (DataCargo): the data cargo to iterate.
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"""
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self.loader = loader
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self._dataset = loader.dataset
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self._batch_fn = loader.batch_fn
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self._index_sampler = loader._index_sampler
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self._sampler_iter = iter(self._index_sampler)
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def __iter__(self):
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return self
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def __next__(self):
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# TODO(chenfeiyu): use dynamic batch size
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index = self._next_index()
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minibatch = [self._dataset[i] for i in index]
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minibatch = self._batch_fn(minibatch) # list[Example] -> Batch
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return minibatch
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next = __next__ # Python 2 compatibility
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def _next_index(self):
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if six.PY3:
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return next(self._sampler_iter)
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else:
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# six.PY2
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return self._sampler_iter.next()
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def __len__(self):
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return len(self._index_sampler)
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