OnBothSides

class hybrid_learning.datasets.transforms.tuple_transforms.OnBothSides(trafo)[source]

Bases: _PartialTupleTrafo, TwoTupleTransforms

Apply a given transformation to both input and target of a tuple in parallel. Shortcut for OnAll(trafo) with tuple length enforcement.

Public Data Attributes:

Inherited from : py: class:_PartialTupleTrafo

settings

Settings to reproduce the instance.
Inherited from : py: class:Transform

IDENTITY_CLASS

The identity class or classes for composition / addition.

settings

Settings to reproduce the instance.

Public Methods:

apply_to(inp, target)

Apply transformation in parallel to both inp and target.
Inherited from : py: class:TwoTupleTransforms

apply_to(inp, target)

Apply transformation in parallel to both inp and target.
Inherited from : py: class:TupleTransforms

apply_to(inp, target)

Apply transformation in parallel to both inp and target.
Inherited from : py: class:Transform

apply_to(inp, target)

Apply transformation in parallel to both inp and target.

Special Methods:

Inherited from : py: class:_PartialTupleTrafo

__init__(trafo)

Init.
Inherited from : py: class:TwoTupleTransforms

__call__(inp, target)

Apply the transformation to the tuple (inp, target); the output again is a tuple.
Inherited from : py: class:TupleTransforms

__init__(trafo)

Init.

__call__(inp, target)

Apply the transformation to the tuple (inp, target); the output again is a tuple.
Inherited from : py: class:Transform

__repr__()

Return repr(self).

__eq__(other)

Return self==value.

__copy__()

Return a shallow copy of self using settings.

__add__(other)

Return a flat composition of self with other.

__radd__(other)

Return a flat composition of other and self.

__call__(inp, target)

Apply the transformation to the tuple (inp, target); the output again is a tuple.
Parameters

trafo (Callable) –

apply_to(inp, target)[source]

Apply transformation in parallel to both inp and target.

Return type

Tuple[Any, Any]