`tlc.client.utils`#

Module Contents#

Class	Description
`SubsetSequentialSampler`	Samples elements sequentially from a given list of indices.
`RangeSampler`	Samples elements sequentially from a range

Function	Description
`bytes2str`
`str2bytes`
`take`
`batched_iterator`
`without_transforms`	Ensures that, if the dataset is a Torchvision dataset, its transforms are temporarily removed.
`standardized_transforms`	Create a new transforms function which takes the whole sample as its only argument, rather than destructuring it.

tlc.client.utils.take(iterator: collections.abc.Iterator, batch_size: int) → list#

tlc.client.utils.batched_iterator(iterator: collections.abc.Iterable, batch_size: int) → collections.abc.Iterator[list]#

class tlc.client.utils.SubsetSequentialSampler(indices: list[int])#

Samples elements sequentially from a given list of indices.

class tlc.client.utils.RangeSampler(end: int, start: int = 0, step: int = 1)#

Samples elements sequentially from a range

tlc.client.utils.without_transforms(dataset: torch.utils.data.Dataset) → Generator[Callable | None, None, None]#

Ensures that, if the dataset is a Torchvision dataset, its transforms are temporarily removed.

Parameters:: dataset – The dataset to temporarily remove transforms from.

tlc.client.utils.standardized_transforms(transforms: Callable[..., Any]) → Callable[[Any], Any]#

Create a new transforms function which takes the whole sample as its only argument, rather than destructuring it.