pyprobound.base.Transform

class Transform(name='')

Bases: Component

Component that applies a transformation to a tensor.

Includes improved typing and caching outputs to avoid recomputation for transformations that appear multiple times in a loss module. See https://github.com/pytorch/pytorch/issues/45414 for typing information.

__init__(name='')

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

cache(fun)	Decorator for a function to cache its output.
check_length_consistency()	Checks that input lengths of Binding components are consistent.
components()	Iterator of child components.
forward(seqs)	A transformation applied to a sequence tensor.
freeze()	Turns off gradient calculation for all parameters.
max_embedding_size()	The maximum number of bytes needed to encode a sequence.
optim_procedure([ancestry, current_order])	The sequential optimization procedure for all Binding components.
reload(checkpoint)	Loads the model from a checkpoint file.
reload_from_state_dict(state_dict)	Loads the model from a state dict.
save(checkpoint[, flank_lengths])	Saves the model to a file with "state_dict" and "metadata" fields.
unfreeze([parameter])	Turns on gradient calculation for the specified parameter.

Attributes

unfreezable

alias of Literal['all']

Non-Inherited Members

abstract forward(seqs)

A transformation applied to a sequence tensor.

Return type:

Tensor

classmethod cache(fun)

Decorator for a function to cache its output.

The decorator must be applied to every function call whose output will be used in the cached function - generally all forward definitions.

Return type:

Callable[[TypeVar(ComponentT, bound= Component), Tensor], Tensor]