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