mindspore.dataset.SubsetSampler

class mindspore.dataset.SubsetSampler(indices, num_samples=None)

Samples the elements from a sequence of indices.

Parameters

• indices (Iterable) – A sequence of indices (Any iterable Python object but string).

• num_samples (int, optional) – Number of elements to sample. Default: None , which means sample all elements.

Raises

• TypeError – If elements of indices are not of type number.

• TypeError – If num_samples is not of type int.

• ValueError – If num_samples is a negative value.

Examples

>>> import mindspore.dataset as ds
>>> indices = [0, 1, 2, 3, 4, 5]
>>>
>>> # creates a SubsetSampler, will sample from the provided indices
>>> sampler = ds.SubsetSampler(indices)
>>> dataset = ds.ImageFolderDataset(image_folder_dataset_dir,
...                                 num_parallel_workers=8,
...                                 sampler=sampler)

add_child(sampler)

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

Note

• If a child sampler is added and it has a shuffle option, its value cannot be Shuffle.PARTIAL . Additionally, the parent sampler's shuffle value must be Shuffle.GLOBAL .

Parameters

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

Examples

>>> import mindspore.dataset as ds
>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> sampler.add_child(ds.RandomSampler(num_samples=4))
>>> dataset = ds.Cifar10Dataset(cifar10_dataset_dir, sampler=sampler)

get_child()

Get the child sampler of given sampler.

Returns

Sampler, The child sampler of given sampler.

Examples

>>> import mindspore.dataset as ds
>>> sampler = ds.SequentialSampler(start_index=0, num_samples=3)
>>> sampler.add_child(ds.RandomSampler(num_samples=2))
>>> child_sampler = sampler.get_child()