torchsig.transforms.transforms.PatchShuffle

class torchsig.transforms.transforms.PatchShuffle(patch_size=(3, 10), shuffle_ratio=(0.01, 0.05), **kwargs)

Bases: SignalTransform

Randomly shuffle multiple local regions of samples.

Transform is loosely based on “PatchShuffle Regularization”.

patch_size

patch_size sets the size of each patch to shuffle * If int or float, patch_size is fixed at the value provided. * If list, patch_size is any element in the list. * If tuple, patch_size is in range of (tuple[0], tuple[1]).

patch_size_distribution: Random draw from patch_size distribution. shuffle_ratio: shuffle_ratio sets the ratio of the patches to shuffle

• If int or float, shuffle_ratio is fixed at the value provided.

• If list, shuffle_ratio is any element in the list.

• If tuple, shuffle_ratio is in range of (tuple[0], tuple[1]).

shuffle_ratio_distribution: Random draw from shuffle_ratio distribution.

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__(patch_size=(3, 10), shuffle_ratio=(0.01, 0.05), **kwargs) → None

Initialize the PatchShuffle transform.

Parameters:

• patch_size – patch_size sets the size of each patch to shuffle. Defaults to (3, 10).

• shuffle_ratio – shuffle_ratio sets the ratio of the patches to shuffle. Defaults to (0.01, 0.05).

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

__call__(signal: Signal) → Signal

Validates signal, performs transform, updates bookeeping, (optionally) enforces data type.

Parameters:

signal – Signal to be transformed.

Returns:

Transformed signal.

__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.