base

Classes:

Name	Description
BaseNoiseLayer	Applies a stochastic transformation to a Tensor using the given Estimators.

BaseNoiseLayer

Bases: Module, Generic[EstimatorModuleT, ParameterizationT, OptionalMaskerT]

Applies a stochastic transformation to a Tensor using the given Estimators.

Parameters:

Name	Type	Description	Default
seed	int | None	Seed for the random number generator used to generate the stochastic transformation. If None, the global RNG state is used.	required
mean_estimator	Estimator[EstimatorModuleT, None, None]	The estimator to use to estimate the mean of the stochastic transformation.	required
std_estimator	Estimator[EstimatorModuleT, ParameterizationT, OptionalMaskerT]	The estimator to use to estimate the standard deviation and optional input mask of the stochastic transformation.	required

Methods:

Name	Description
__call__	Transform the input data.
__getstate__	Prepare a serializable copy of self.__dict__.
__init_subclass__	Set the default dtype to torch.float32 inside all subclass __init__ methods.
__setstate__	Restore from a serialized copy of self.__dict__.
forward	Transform the input data.
get_applied_transform_components_factory	Create a function that returns the elements of the transform components ('mean' and 'std') applied during the most recent
get_transformed_output_factory	Create a function that returns the transformed output from the most recent forward pass.
initial_seed	Return the initial seed of the CPU device's random number generator.
manual_seed	Seed each of the random number generators.
reset_parameters	Reinitialize parameters and buffers.
seed	Seed each of the random number generators using a non-deterministic random number.

__call__

__call__(
    input: Tensor,
    noise_mask: Tensor | None = None,
    **kwargs: Any,
) -> torch.Tensor

Transform the input data.

Parameters:

Name	Type	Description	Default
input	Tensor	The input to transform.	required
noise_mask	Tensor | None	An optional mask that selects the elements of input to transform. Where the mask is False, the original input value is returned. Also used to select the elements of the sampled standard deviations to use to mask the input. If None, the entire input is transformed.	None
**kwargs	Any	Additional keyword arguments to the estimator modules.	required

__getstate__

__getstate__() -> dict[str, Any]

Prepare a serializable copy of self.__dict__.

__init_subclass__

__init_subclass__() -> None

Set the default dtype to torch.float32 inside all subclass __init__ methods.

__setstate__

__setstate__(state: dict[str, Any]) -> None

Restore from a serialized copy of self.__dict__.

forward

forward(
    input: Tensor,
    noise_mask: Tensor | None = None,
    **kwargs: Any,
) -> torch.Tensor

Transform the input data.

Parameters:

Name	Type	Description	Default
input	Tensor	The input to transform.	required
noise_mask	Tensor | None	An optional mask that selects the elements of input to transform. Where the mask is 0, the original input value is returned. Also used to select the elements of the sampled standard deviations to use to mask the input. If None, the entire input is transformed.	None
**kwargs	Any	Additional keyword arguments to the estimator modules.	required

Returns:

Type	Description
torch.Tensor	The transformed input data.

get_applied_transform_components_factory

get_applied_transform_components_factory() -> Callable[
    [], dict[str, torch.Tensor]
]

Create a function that returns the elements of the transform components ('mean' and 'std') applied during the most recent forward pass.

Specifically, the applied elements are those selected by the noise mask (if supplied) and standard deviation mask (if std_estimator.masker is not None). If no masks are used, all elements are returned.

The applied transform components are returned flattened.

This function is intended to be used to log histograms of the transform components.

Returns:

Type	Description
Callable[[], dict[str, torch.Tensor]]	A function that returns the the elements of the transform components applied during the most recent forward pass.

Examples:

>>> from torch import nn
>>> from stainedglass_core import model as sg_model, noise_layer as sg_noise_layer
>>> base_model = nn.Linear(20, 2)
>>> noisy_model = sg_model.NoisyModel(
...     sg_noise_layer.CloakNoiseLayer1,
...     base_model,
...     target_parameter="input",
... )
>>> get_applied_transform_components = (
...     noisy_model.noise_layer.get_applied_transform_components_factory()
... )
>>> input = torch.ones(1, 20)
>>> noise_mask = torch.tensor(5 * [False] + 15 * [True])
>>> output = noisy_model(input, noise_mask=noise_mask)
>>> applied_transform_components = get_applied_transform_components()
>>> applied_transform_components
{'mean': tensor(...), 'std': tensor(...)}
>>> {
...     component_name: component.shape
...     for component_name, component in applied_transform_components.items()
... }
{'mean': torch.Size([15]), 'std': torch.Size([15])}

get_transformed_output_factory

get_transformed_output_factory() -> Callable[
    [], torch.Tensor
]

Create a function that returns the transformed output from the most recent forward pass.

If super batching is active, only the transformed half of the super batch output is returned.

Returns:

Type	Description
Callable[[], torch.Tensor]	A function that returns the transformed output from the most recent forward pass.

Examples:

>>> from stainedglass_core import noise_layer as sg_noise_layer
>>> noise_layer = sg_noise_layer.CloakNoiseLayer1()
>>> get_transformed_output = noise_layer.get_transformed_output_factory()
>>> input = torch.ones(2, 3, 32, 32)
>>> output = noise_layer(input)
>>> transformed_output = get_transformed_output()
>>> assert output.equal(transformed_output)

initial_seed

initial_seed() -> int

Return the initial seed of the CPU device's random number generator.

manual_seed

manual_seed(seed: int | None) -> None

Seed each of the random number generators.

Setting seed to None will destroy any existing generators.

Parameters:

Name	Type	Description	Default
seed	int | None	The seed to set.	required

reset_parameters

reset_parameters() -> None

Reinitialize parameters and buffers.

This method is useful for initializing tensors created on the meta device.

seed

seed() -> None

Seed each of the random number generators using a non-deterministic random number.