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144 lines
5.3 KiB
144 lines
5.3 KiB
# 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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from __future__ import print_function
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from __future__ import division
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import numpy as np
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from .dataset import Dataset
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__all__ = ["BatchSampler"]
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class BatchSampler(object):
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"""
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A base implement of batch sampler used by `paddle.io.DataLoader`
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which yield mini-batch indices(a list/tuple with length as
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mini-batch size and holds sample indices) iterably.
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Batch sampler used by :code:`paddle.io.DataLoader` should be a subclass
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of :code:`paddle.io.BatchSampler`, BatchSampler subclasses should
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implement following methods:
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:code:`__iter__`: return mini-batch indices iterably.
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:code:`__len__`: get mini-batch number in an epoch.
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Args:
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dataset(Dataset): this could be a :code:`paddle.io.Dataset`
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implement or other python object which implemented
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:code:`__len__` for BatchSampler to get indices as the
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range of :attr:`dataset` length. Default None.
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indices (list|tuple): a substitution parameter for
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:attr:`dataset` either :attr:`dataset` or
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:attr:`indices` should be set, give the whole
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indices to sampler from directly. Default None.
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shuffle(bool): whether to shuffle indices order before genrating
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batch indices. Default False.
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batch_size(int): sample indice number in a mini-batch indices.
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drop_last(bool): whether drop the last incomplete batch dataset size
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is not divisible by the batch size. Default False
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Returns:
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BatchSampler: an iterable object for indices iterating
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Examples:
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.. code-block:: python
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from paddle.io import BatchSampler, Dataset
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# init with indices
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bs = BatchSampler(indices=list(range(100)),
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shuffle=True,
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batch_size=8,
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drop_last=True)
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for batch_indices in bs:
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print(batch_indices)
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# init with dataset
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class RandomDataset(Dataset):
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def __init__(self, num_samples):
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self.num_samples = num_samples
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def __getitem__(self, idx):
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image = np.random.random([784]).astype('float32')
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label = np.random.randint(0, 9, (1, )).astype('int64')
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return image, label
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def __len__(self):
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return self.num_samples
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bs = BatchSampler(dataset=RandomDataset(100),
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shuffle=False,
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batch_size=16,
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drop_last=False)
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for batch_indices in bs:
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print(batch_indices)
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see `paddle.io.DataLoader`
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"""
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def __init__(self,
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dataset=None,
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indices=None,
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shuffle=False,
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batch_size=1,
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drop_last=False):
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if dataset is None:
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assert indices is not None, \
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"either dataset or indices should be set"
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assert isinstance(indices, list) or isinstance(indices, tuple), \
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"indices should be a list or tuple, but got {}".format(type(indices))
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self.indices = indices
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else:
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assert isinstance(dataset, Dataset), \
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"dataset should be an instance of paddle.io.Dataset"
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assert indices is None, \
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"should not set both dataset and indices"
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self.indices = list(range(len(dataset)))
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assert isinstance(batch_size, int) and batch_size > 0, \
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"batch_size should be a positive integer, but got {}".format(batch_size)
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self.batch_size = batch_size
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assert isinstance(shuffle, bool), \
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"shuffle should be a boolean value, but got {}".format(type(shuffle))
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self.shuffle = shuffle
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assert isinstance(drop_last, bool), \
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"drop_last should be a boolean value, but got {}".format(type(drop_last))
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self.drop_last = drop_last
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def __iter__(self):
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if self.shuffle:
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np.random.shuffle(self.indices)
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_iter = iter(self.indices)
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batch_indices = []
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for idx in _iter:
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batch_indices.append(idx)
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if len(batch_indices) == self.batch_size:
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yield batch_indices
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batch_indices = []
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if not self.drop_last and len(batch_indices) > 0:
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yield batch_indices
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def __len__(self):
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num_samples = len(self.indices)
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num_samples += int(not self.drop_last) * (self.batch_size - 1)
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return num_samples // self.batch_size
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