StreamingCIFAR10#

class streaming.vision.StreamingCIFAR10(local, remote=None, split=None, shuffle=False, transform=None, target_transform=None, predownload=100000, keep_zip=None, download_retry=2, download_timeout=60, validate_hash=None, shuffle_seed=None, num_canonical_nodes=None, batch_size=None)[source]#

Implementation of the CIFAR-10 dataset using StreamingDataset.

Parameters

  • local (str) – Local dataset directory where shards are cached by split.

  • remote (str, optional) – Download shards from this remote path or directory. If None, this rank and worker’s partition of the dataset must all exist locally. Defaults to None.

  • split (str, optional) – Which dataset split to use, if any. Defaults to None.

  • shuffle (bool) – Whether to iterate over the samples in randomized order. Defaults to False.

  • transform (callable, optional) – A function/transform that takes in an image and returns a transformed version. Defaults to None.

  • target_transform (callable, optional) – A function/transform that takes in the target and transforms it. Defaults to None.

  • predownload (int, optional) – Target number of samples ahead to download the shards of while iterating. Defaults to 100_000.

  • keep_zip (bool, optional) – Whether to keep or delete the compressed file when decompressing downloaded shards. If set to None, keep iff remote is local. Defaults to None.

  • download_retry (int) – Number of download re-attempts before giving up. Defaults to 2.

  • download_timeout (float) – Number of seconds to wait for a shard to download before raising an exception. Defaults to 60.

  • validate_hash (str, optional) – Optional hash or checksum algorithm to use to validate shards. Defaults to None.

  • shuffle_seed (int, optional) – Seed for shuffling, or None for random seed. Defaults to None.

  • num_canonical_nodes (int, optional) – Canonical number of nodes for shuffling with resumption. Defaults to None, which is interpreted as the number of nodes of the initial run.

  • batch_size (int, optional) – Batch size of its DataLoader, which affects how the dataset is partitioned over the workers. Defaults to None.