TianYuan
3 years ago
parent
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# 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 paddle.io import Dataset
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__all__ = [
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"split",
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"TransformDataset",
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"CacheDataset",
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"TupleDataset",
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"DictDataset",
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"SliceDataset",
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"SubsetDataset",
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"FilterDataset",
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"ChainDataset",
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]
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def split(dataset, first_size):
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"""A utility function to split a dataset into two datasets."""
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first = SliceDataset(dataset, 0, first_size)
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second = SliceDataset(dataset, first_size, len(dataset))
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return first, second
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class TransformDataset(Dataset):
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def __init__(self, dataset, transform):
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"""Dataset which is transformed from another with a transform.
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Args:
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dataset (Dataset): the base dataset.
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transform (callable): the transform which takes an example of the base dataset as parameter and return a new example.
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"""
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self._dataset = dataset
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self._transform = transform
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def __len__(self):
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return len(self._dataset)
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def __getitem__(self, i):
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in_data = self._dataset[i]
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return self._transform(in_data)
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class CacheDataset(Dataset):
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def __init__(self, dataset):
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"""A lazy cache of the base dataset.
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Args:
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dataset (Dataset): the base dataset to cache.
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"""
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self._dataset = dataset
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self._cache = dict()
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def __len__(self):
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return len(self._dataset)
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def __getitem__(self, i):
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if i not in self._cache:
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self._cache[i] = self._dataset[i]
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return self._cache[i]
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class TupleDataset(Dataset):
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def __init__(self, *datasets):
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"""A compound dataset made from several datasets of the same length. An example of the `TupleDataset` is a tuple of examples from the constituent datasets.
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Args:
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datasets: tuple[Dataset], the constituent datasets.
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"""
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if not datasets:
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raise ValueError("no datasets are given")
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length = len(datasets[0])
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for i, dataset in enumerate(datasets):
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if len(dataset) != length:
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raise ValueError("all the datasets should have the same length."
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"dataset {} has a different length".format(i))
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self._datasets = datasets
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self._length = length
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def __getitem__(self, index):
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# SOA
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batches = [dataset[index] for dataset in self._datasets]
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if isinstance(index, slice):
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length = len(batches[0])
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# AOS
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return [
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tuple([batch[i] for batch in batches])
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for i in six.moves.range(length)
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]
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else:
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return tuple(batches)
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def __len__(self):
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return self._length
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class DictDataset(Dataset):
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def __init__(self, **datasets):
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"""
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A compound dataset made from several datasets of the same length. An
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example of the `DictDataset` is a dict of examples from the constituent
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datasets.
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WARNING: paddle does not have a good support for DictDataset, because
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every batch yield from a DataLoader is a list, but it cannot be a dict.
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So you have to provide a collate function because you cannot use the
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default one.
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Args:
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datasets: Dict[Dataset], the constituent datasets.
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"""
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if not datasets:
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raise ValueError("no datasets are given")
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length = None
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for key, dataset in six.iteritems(datasets):
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if length is None:
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length = len(dataset)
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elif len(dataset) != length:
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raise ValueError(
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"all the datasets should have the same length."
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"dataset {} has a different length".format(key))
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self._datasets = datasets
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self._length = length
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def __getitem__(self, index):
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batches = {
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key: dataset[index]
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for key, dataset in six.iteritems(self._datasets)
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}
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if isinstance(index, slice):
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length = len(six.next(six.itervalues(batches)))
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return [{key: batch[i]
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for key, batch in six.iteritems(batches)}
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for i in six.moves.range(length)]
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else:
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return batches
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def __len__(self):
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return self._length
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class SliceDataset(Dataset):
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def __init__(self, dataset, start, finish, order=None):
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"""A Dataset which is a slice of the base dataset.
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Args:
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dataset (Dataset): the base dataset.
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start (int): the start of the slice.
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finish (int): the end of the slice, not inclusive.
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order (List[int], optional): the order, it is a permutation of the valid example ids of the base dataset. If `order` is provided, the slice is taken in `order`. Defaults to None.
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"""
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if start < 0 or finish > len(dataset):
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raise ValueError("subset overruns the dataset.")
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self._dataset = dataset
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self._start = start
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self._finish = finish
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self._size = finish - start
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if order is not None and len(order) != len(dataset):
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raise ValueError(
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"order should have the same length as the dataset"
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"len(order) = {} which does not euqals len(dataset) = {} ".
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format(len(order), len(dataset)))
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self._order = order
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def __len__(self):
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return self._size
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def __getitem__(self, i):
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if i >= 0:
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if i >= self._size:
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raise IndexError('dataset index out of range')
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index = self._start + i
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else:
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if i < -self._size:
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raise IndexError('dataset index out of range')
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index = self._finish + i
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if self._order is not None:
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index = self._order[index]
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return self._dataset[index]
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class SubsetDataset(Dataset):
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def __init__(self, dataset, indices):
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"""A Dataset which is a subset of the base dataset.
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Args:
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dataset (Dataset): the base dataset.
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indices (Iterable[int]): the indices of the examples to pick.
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"""
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self._dataset = dataset
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if len(indices) > len(dataset):
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raise ValueError("subset's size larger that dataset's size!")
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self._indices = indices
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self._size = len(indices)
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def __len__(self):
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return self._size
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def __getitem__(self, i):
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index = self._indices[i]
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return self._dataset[index]
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class FilterDataset(Dataset):
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def __init__(self, dataset, filter_fn):
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"""A filtered dataset.
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Args:
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dataset (Dataset): the base dataset.
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filter_fn (callable): a callable which takes an example of the base dataset and return a boolean.
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"""
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self._dataset = dataset
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self._indices = [
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i for i in range(len(dataset)) if filter_fn(dataset[i])
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]
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self._size = len(self._indices)
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def __len__(self):
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return self._size
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def __getitem__(self, i):
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index = self._indices[i]
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return self._dataset[index]
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class ChainDataset(Dataset):
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def __init__(self, *datasets):
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"""A concatenation of the several datasets which the same structure.
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Args:
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datasets (Iterable[Dataset]): datasets to concat.
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"""
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self._datasets = datasets
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def __len__(self):
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return sum(len(dataset) for dataset in self._datasets)
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def __getitem__(self, i):
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if i < 0:
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raise IndexError("ChainDataset doesnot support negative indexing.")
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for dataset in self._datasets:
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if i < len(dataset):
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return dataset[i]
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i -= len(dataset)
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raise IndexError("dataset index out of range")
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