tensorplay.utils.data.sampler

Classes

class BatchSampler [source]

BatchSampler(sampler, batch_size, drop_last)

Bases: Sampler

Wraps another sampler to yield a mini-batch of indices.

Args

• sampler (Sampler): Base sampler.
• batch_size (int): Size of mini-batch.
• drop_last (bool): If True, the sampler will drop the last batch if its size would be less than batch_size

Example

list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=False))
    [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
list(BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True))
    [[0, 1, 2], [3, 4, 5], [6, 7, 8]]

Methods

__init__(self, sampler, batch_size, drop_last) [source]

Initialize self. See help(type(self)) for accurate signature.

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class RandomSampler [source]

RandomSampler(data_source, replacement=False, num_samples=None, generator=None)

Bases: Sampler

Samples elements randomly. If without replacement, then sample from a shuffled dataset. If with replacement, then user can specify num_samples to draw.

Arguments

• data_source (Dataset): dataset to sample from
• replacement (bool): samples are drawn on-demand with replacement if True, default=False
• num_samples (int): number of samples to draw, default=len(dataset). This argument is supposed to be specified only when replacement is True.
• generator (Generator): Generator used in sampling.

Methods

__init__(self, data_source, replacement=False, num_samples=None, generator=None) [source]

Initialize self. See help(type(self)) for accurate signature.

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class Sampler [source]

Sampler(data_source: Optional[Sized])

Bases: Generic

Base class for all Samplers.

Every Sampler subclass has to provide an __iter__ method, providing a way to iterate over indices of dataset elements, and a __len__ method that returns the length of the returned iterators.

.. note:: The __len__ method isn't strictly required by ~torch.utils.data.DataLoader, but is expected in any calculation involving the length of a ~torch.utils.data.DataLoader.

Methods

__init__(self, data_source: Optional[Sized]) [source]

Initialize self. See help(type(self)) for accurate signature.

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class SequentialSampler [source]

SequentialSampler(data_source)

Bases: Sampler

Samples elements sequentially, always in the same order.

Arguments

• data_source (Dataset): dataset to sample from

Methods

__init__(self, data_source) [source]

Initialize self. See help(type(self)) for accurate signature.

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