torchsig.transforms.dataset_transforms.PatchShuffle¶

class torchsig.transforms.dataset_transforms.PatchShuffle(patch_size=(3, 10), shuffle_ratio=(0.01, 0.05), **kwargs)[source]¶

Bases: DatasetTransform

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]).

Type:

int, float, list, tuple

patch_size_distribution (Callable[[], int]): Random draw from patch_size distribution. shuffle_ratio (int, float, list, tuple):

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 (Callable[[], float]): Random draw from shuffle_ratio distribution.

Methods

`add_parent`	Add parent Seedable object and set up RNGs accordingly
`get_distribution`
`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`	Updates bookkeeping to transforms in DatasetSignal's SignalMetadata and checks signal valididty.
`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[source]¶: Transform initialization as Seedable.

__call__(signal: DatasetSignal) → DatasetSignal[source]¶

Performs transforms.

Parameters:: signal (DatasetSignal) – DatasetSignal to be transformed.
Raises:: NotImplementedError – Inherited classes must override this method.
Returns:: Transformed DatasetSignal.
Return type:: DatasetSignal

__repr__() → str¶

Transform string representation. Should be able to recreate class from this string.

Returns:: Transform representation.
Return type:: str

__str__() → str¶: Return str(self).

add_parent(parent) → None¶: Add parent Seedable object and set up RNGs accordingly

get_second_seed(seed: int) → int¶

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

Parameters:: seed (int) – Seed to use.
Returns:: New seed.
Return type:: int

seed(seed: int) → None¶

Seed number generators with given seed.

Parameters:: seed (int) – Seed to use.

setup_rngs() → None¶: Initialize torch and numpy number generators, and update its children.

update(signal: DatasetSignal) → None¶

Updates bookkeeping to transforms in DatasetSignal’s SignalMetadata and checks signal valididty. Inherited classes should always call self.update() after performing transform operation (inside __call__).

Parameters:: signal (DatasetSignal) – transformed DatasetSignal.

update_from_parent() → None¶: Update numpy and torch number generators with parent seed