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# coding: utf-8
# pylint: disable=
"""Dataset sampler."""
__all__ = ['Sampler', 'SequentialSampler', 'RandomSampler', 'BatchSampler']

import numpy as np

[docs]class Sampler(object): """Base class for samplers. All samplers should subclass `Sampler` and define `__iter__` and `__len__` methods. """ def __iter__(self): raise NotImplementedError def __len__(self): raise NotImplementedError
[docs]class SequentialSampler(Sampler): """Samples elements from [0, length) sequentially. Parameters ---------- length : int Length of the sequence. """ def __init__(self, length): self._length = length def __iter__(self): return iter(range(self._length)) def __len__(self): return self._length
[docs]class RandomSampler(Sampler): """Samples elements from [0, length) randomly without replacement. Parameters ---------- length : int Length of the sequence. """ def __init__(self, length): self._length = length def __iter__(self): indices = np.arange(self._length) np.random.shuffle(indices) return iter(indices) def __len__(self): return self._length
[docs]class BatchSampler(Sampler): """Wraps over another `Sampler` and return mini-batches of samples. Parameters ---------- sampler : Sampler The source Sampler. batch_size : int Size of mini-batch. last_batch : {'keep', 'discard', 'rollover'} Specifies how the last batch is handled if batch_size does not evenly divide sequence length. If 'keep', the last batch will be returned directly, but will contain less element than `batch_size` requires. If 'discard', the last batch will be discarded. If 'rollover', the remaining elements will be rolled over to the next iteration. Examples -------- >>> sampler = >>> batch_sampler =, 3, 'keep') >>> list(batch_sampler) [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] """ def __init__(self, sampler, batch_size, last_batch='keep'): self._sampler = sampler self._batch_size = batch_size self._last_batch = last_batch self._prev = [] def __iter__(self): batch, self._prev = self._prev, [] for i in self._sampler: batch.append(i) if len(batch) == self._batch_size: yield batch batch = [] if batch: if self._last_batch == 'keep': yield batch elif self._last_batch == 'discard': return elif self._last_batch == 'rollover': self._prev = batch else: raise ValueError( "last_batch must be one of 'keep', 'discard', or 'rollover', " \ "but got %s"%self._last_batch) def __len__(self): if self._last_batch == 'keep': return (len(self._sampler) + self._batch_size - 1) // self._batch_size if self._last_batch == 'discard': return len(self._sampler) // self._batch_size if self._last_batch == 'rollover': return (len(self._prev) + len(self._sampler)) // self._batch_size raise ValueError( "last_batch must be one of 'keep', 'discard', or 'rollover', " \ "but got %s"%self._last_batch)