Source code for tensorlayerx.dataflow.dataloader

#! /usr/bin/python
# -*- coding: utf-8 -*-
from .dataset import Dataset, IterableDataset
from .sampler import Sampler, SequentialSampler, RandomSampler, BatchSampler, SubsetRandomSampler, WeightedRandomSampler
from .utils import _DatasetKind, _InfiniteIterableSampler
from . import utils
import math
__all__ = [

[docs]class DataLoader(object): """ Data loader. Combines a dataset and a sampler, and provides an iterable over the given dataset. The :class:`tensorlayerx.dataflow.DataLoader` supports both map-style and iterable-style datasets with single- or multi-process loading, customizing loading order and optional automatic batching Parameters ----------- dataset : Dataset dataset from which to load the data. batch_size : int how many samples per batch to load, default is 1. shuffle : bool set to ``True`` to have the data reshuffled at every epoch, default is ``False``. drop_last : bool set to ``True`` to drop the last incomplete batch, if the dataset size is not divisible by the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then the last batch will be smaller. default is ``False``. sampler : Sampler defines the strategy to draw samples from the dataset. If specified, `shuffle` must not be specified. batch_sampler : Sampler returns a batch of indices at a time. If specified, `shuffle`, `batch_size`, `drop_last`, `sampler` must not be specified. num_workers : int how many subprocesses to use for data loading. ``0`` means that the data will be loaded in single process. default is ``0``. collate_fn : callable merges a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a map-style dataset. time_out : numeric if positive, the timeout value for collecting a batch from workers. Should always be non-negative. default is ``0``. worker_init_fn : callable If not ``None``, this will be called on each worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as input, after seeding and before data loading. default is ``None``. prefetch_factor : int Number of samples loaded in advance by each worker. ``2`` means there will be a total of 2 * num_workers samples prefetched across all workers. default is ``2`` persistent_workers : bool If ``True``, the data loader will not shutdown the worker processes after a dataset has been consumed once. This allows to maintain the workers `Dataset` instances alive. default is ``False``. """ def __init__( self, dataset, batch_size=1, shuffle=False, drop_last=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None, time_out=0, worker_init_fn=None, prefetch_factor=2, persistent_workers=False, ): self.dataset = dataset assert num_workers >= 0, "num_workers should be a non_negative integer" # if num_workers == 0 and prefetch_factor != 2: # raise ValueError("prefetch_factor option should not be specified, when num_workers is 0.") if persistent_workers and num_workers == 0: raise ValueError('persistent_workers option needs num_workers > 0') self.num_workers = 0 # TODO optimizer multiprocess in multi backends self.prefetch_factor = 2 self.time_out = time_out self.worker_init_fn = worker_init_fn if isinstance(dataset, IterableDataset): self._dataset_kind = _DatasetKind.Iter if shuffle is not False: raise ValueError("IterableDataset only support 'shuffle=False', but got shuffle={}.".format(shuffle)) elif sampler is not None: raise ValueError("IterableDataset only support 'sampler=None', but got sampler={}.".format(sampler)) elif batch_sampler is not None: raise ValueError( "IterableDataset only support 'batch_sampler=None', " "but got batch_sampler={}.".format(batch_sampler) ) else: self._dataset_kind = _DatasetKind.Map if sampler is not None and shuffle: raise ValueError("sampler option is mutually exclusive with shuffle option.") if batch_sampler is not None: if batch_size != 1 or shuffle or sampler is not None or drop_last: raise ValueError( "batch_size, shuffle, sampler, drop_last should not be set, when batch_sampler is specified." ) batch_size = None drop_last = False elif batch_size is None: if drop_last: raise ValueError("drop_last should be False, when batch_size is None.") if sampler is None: if self._dataset_kind == _DatasetKind.Iter: sampler = _InfiniteIterableSampler() else: if shuffle: sampler = RandomSampler(dataset) else: sampler = SequentialSampler(dataset) if batch_size is not None and batch_sampler is None: batch_sampler = BatchSampler(sampler, batch_size, drop_last) self.batch_size = batch_size self.drop_last = drop_last self.sampler = sampler self.batch_sampler = batch_sampler self._iterator = None if collate_fn is None: if self._is_batch: collate_fn = utils.default_collate else: collate_fn = utils.default_convert self.collate_fn = collate_fn self.persistent_workers = persistent_workers @property def _is_batch(self): return self.batch_sampler is not None @property def _index_sampler(self): if self._is_batch: return self.batch_sampler else: return self.sampler def _get_iterator(self): if self.num_workers == 0: return utils._SingleProcessDataLoaderIter(self) else: return utils._MultiProcessingDataLoaderIter(self) def __iter__(self): if self.persistent_workers and self.num_workers > 0: if self._iterator is None: self._iterator = self._get_iterator() else: self._iterator._reset(self) return self._iterator else: return self._get_iterator() def __len__(self): if self._dataset_kind == _DatasetKind.Iter: length = len(self.dataset) if self.batch_size is not None: if self.drop_last: length = length // self.batch_size else: length = math.ceil(length / self.batch_size) return length else: return len(self._index_sampler)