`tlc.client.utils`¶

Module Contents¶

Classes¶

Class	Description
`SubsetSequentialSampler`	Samples elements sequentially from a given list of indices.
`RangeSampler`	Samples elements sequentially from a range
`RepeatByWeightSampler`	Repeats elements based on their weight.
`StandardizedTransforms`	A callable class that wraps transforms to take the whole sample as its only argument, rather than destructuring it.

Functions¶

Function	Description
`bytes2str`
`str2bytes`
`take`
`batched_iterator`
`without_transforms`	Ensures that, if the dataset is a Torchvision dataset, its transforms are temporarily removed.
`relativize_with_max_depth`	Relativize the given URL with respect to the given owner URL, up to a maximum depth.
`standardized_transforms`	Create a new transforms function which takes the whole sample as its only argument, rather than destructuring it.
`get_column_from_pyarrow_table`	Return a the specified column of the table as a pyarrow table.

API¶

tlc.client.utils.bytes2str(obj: bytes) → str¶

tlc.client.utils.str2bytes(s: str) → bytes¶

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])¶

Bases: torch.utils.data.sampler.Sampler[int]

Samples elements sequentially from a given list of indices.

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

Bases: torch.utils.data.sampler.Sampler[int]

Samples elements sequentially from a range

class tlc.client.utils.RepeatByWeightSampler(weights: list[float], shuffle: bool = True)¶

Bases: torch.utils.data.sampler.Sampler[int]

Repeats elements based on their weight.

tlc.client.utils.without_transforms(dataset: torch.utils.data.Dataset) → collections.abc.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.relativize_with_max_depth(url: tlc.core.url.Url, owner: tlc.core.url.Url, max_depth: int) → tlc.core.url.Url¶

Relativize the given URL with respect to the given owner URL, up to a maximum depth.

Deprecated: Use Url.to_relative_with_max_depth instead.

class tlc.client.utils.StandardizedTransforms(transforms: Callable[..., Any])¶: A callable class that wraps transforms to take the whole sample as its only argument, rather than destructuring it.

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.

Parameters:: transforms – The transforms function to standardize.
Returns:: The standardized transforms function.

tlc.client.utils.get_column_from_pyarrow_table(table: pyarrow.Table, name: str, combine_chunks: bool = True) → pyarrow.Array | pyarrow.ChunkedArray¶

Return a the specified column of the table as a pyarrow table.

To get nested sub-columns, use dot notation. E.g. ‘column.sub_column’. The values in the column will be the row-view of the table. A column which is a PIL image in its sample-view, for instance, will be returned as a column of strings.

Parameters:

name – The name of the column to get.
combine_chunks – Whether to combine the chunks of the returned column in the case that it is a ChunkedArray. Defaults to True.

Returns:

A pyarrow array containing the specified column.

Raises:

KeyError – If the column does not exist in the table.

tlc.client.utils¶

Module Contents¶

Classes¶

Functions¶

API¶

`tlc.client.utils`¶