mindspore.dataset.DistributedSampler

class mindspore.dataset.DistributedSampler(num_shards, shard_id, shuffle=True, num_samples=None, offset=- 1)

A sampler that accesses a shard of the dataset.

Parameters

• num_shards (int) – Number of shards to divide the dataset into.

• shard_id (int) – Shard ID of the current shard, which should within the range of [0, num_shards-1].

• shuffle (bool, optional) – If True, the indices are shuffled, otherwise it will not be shuffled(default=True).

• num_samples (int, optional) – The number of samples to draw (default=None, which means sample all elements).

• offset (int, optional) – The starting shard ID where the elements in the dataset are sent to, which should be no more than num_shards. This parameter is only valid when a ConcatDataset takes a DistributedSampler as its sampler. It will affect the number of samples of per shard (default=-1, which means each shard has same number of samples).

Examples

>>> # creates a distributed sampler with 10 shards in total. This shard is shard 5.
>>> sampler = ds.DistributedSampler(10, 5)
>>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir,
...                                 num_parallel_workers=8,
...                                 sampler=sampler)

Raises

• TypeError – If num_shards is not an integer value.

• TypeError – If shard_id is not an integer value.

• TypeError – If shuffle is not a boolean value.

• TypeError – If num_samples is not an integer value.

• TypeError – If offset is not an integer value.

• ValueError – If num_samples is a negative value.

• RuntimeError – If num_shards is not a positive value.

• RuntimeError – If shard_id is smaller than 0 or equal to num_shards or larger than num_shards.

• RuntimeError – If offset is greater than num_shards.

add_child(sampler)

Add a sub-sampler for given sampler. The sub-sampler will receive all data from the output of parent sampler and apply its sample logic to return new samples.

Parameters

sampler (Sampler) – Object used to choose samples from the dataset. Only builtin samplers(DistributedSampler, PKSampler, RandomSampler, SequentialSampler, SubsetRandomSampler, WeightedRandomSampler) are supported.

Examples

>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> sampler.add_child(ds.RandomSampler(num_samples=2))
>>> dataset = ds.Cifar10Dataset(cifar10_dataset_dir, sampler=sampler)

get_child()

Get the child sampler.

get_num_samples()

All samplers can contain a numeric num_samples value (or it can be set to None). A child sampler can exist or be None. If a child sampler exists, then the child sampler count can be a numeric value or None. These conditions impact the resultant sampler count that is used. The following table shows the possible results from calling this function.

child sampler	num_samples	child_samples	result
T	x	y	min(x, y)
T	x	None	x
T	None	y	y
T	None	None	None
None	x	n/a	x
None	None	n/a	None

Returns

int, the number of samples, or None.

parse()

Parse the sampler.

parse_child()

Parse the child sampler.

parse_child_for_minddataset()

Parse the child sampler for MindRecord.

parse_for_minddataset()

Parse the sampler for MindRecord.