torchsig.transforms.base_transforms.RandAugment

class torchsig.transforms.base_transforms.RandAugment(transforms: list[Transform], choose: int = 2, replace: bool = False, **kwargs)

Bases: Transform

RandAugment transform loosely based on: `”RandAugment: Practical automated data augmentation with a reduced search space”

<https://arxiv.org/pdf/1909.13719.pdf>`_.

This transform randomly selects and applies a subset of transforms from a list.

transforms

List of Transforms to choose from.

choose

Number of Transforms to randomly choose. Defaults to 2.

replace

Allow replacement in random choose. Defaults to False.

Methods

add_parent	Add parent Seedable object and set up RNGs accordingly.
get_distribution	Create distribution function with proper seeding.
get_second_seed	Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds.
seed	Seed number generators with given seed.
setup_rngs	Initialize torch and numpy number generators, and update its children.
update_from_parent	Update numpy and torch number generators with parent seed.

__init__(transforms: list[Transform], choose: int = 2, replace: bool = False, **kwargs)

Initialize the RandAugment transform.

Parameters:

• transforms – List of Transforms to choose from.

• choose – Number of Transforms to randomly choose. Defaults to 2.

• replace – Allow replacement in random choose. Defaults to False.

• **kwargs – Additional keyword arguments passed to the parent class.

__call__(signal: Signal) → Signal

Apply a random subset of transforms to the signal.

Parameters:

signal – Signal to be transformed.

Returns:

Transformed signal after applying the randomly chosen transforms.

__repr__() → str

Transform string representation.

Should be able to recreate class from this string.

Returns:

Transform representation.

__str__() → str

String representation of the transform.

Returns:

String representation of the transform.

add_parent(parent: Seedable, register: bool = True) → None

Add parent Seedable object and set up RNGs accordingly.

Parameters:

• parent – Parent Seedable object to add.

• register – If True (default), add self to parent.children so that future seed propagation reaches this object. Pass False for transient objects (e.g. per-sample Signal instances) that only need the parent link for metadata/RNG access during their lifetime but must not accumulate in the parent’s child list, which would otherwise cause unbounded memory growth.

get_distribution(params: list | tuple | float, scaling: str = 'linear') → Distribution

Create distribution function with proper seeding.

Parameters:

• params – Parameters for distribution.

• scaling – Scaling param for distribution. Defaults to ‘linear’.

Returns:

Distribution function, seeded.

Return type:

Distribution

get_second_seed(seed: int) → int

Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds.

Parameters:

seed – Seed to use.

Returns:

New seed.

seed(seed: int) → None

Seed number generators with given seed.

Parameters:

seed – Seed to use.

setup_rngs() → None

Initialize torch and numpy number generators, and update its children.

update_from_parent() → None

Update numpy and torch number generators with parent seed.