base

Modules:

Name	Description
sg_estimators
sg_loss
sg_maskers
sg_output
sg_parameterizations
sg_utils
stainedglass_core
version	Constants storing the version numbers for major changes to the codebase.

Classes:

Name	Description
BaseNoiseLayer	Base Class for Stained Glass Transform Layers.
NoiseLayerOutput	The output of BaseNoiseLayer.forward().

BaseNoiseLayer

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

Base Class for Stained Glass Transform Layers.

Methods:

Name	Description
__call__	Stochastically transform the input.
__getstate__	Prepare a serializable copy of self.__dict__.
__init__	Initialize necessary input_shape parameter to use Stained Glass Transform layers.
__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.

Attributes:

Name	Type	Description
input_shape	tuple[int, ...]	The shape of the expected input including its batch dimension.
mask	Tensor | None	The mask to apply calculated from parameters of the stochastic transformation computed during the most recent call to forward.
mean	Tensor	The means of the stochastic transformation computed during the most recent call to forward.
std	Tensor	The standard deviations of the stochastic transformation computed during the most recent call to forward.

input_shape property

input_shape: tuple[int, ...]

The shape of the expected input including its batch dimension.

mask property writable

mask: Tensor | None

The mask to apply calculated from parameters of the stochastic transformation computed during the most recent call to forward.

mean property writable

mean: Tensor

The means of the stochastic transformation computed during the most recent call to forward.

std property writable

std: Tensor

The standard deviations of the stochastic transformation computed during the most recent call to forward.

__call__

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

Stochastically transform the input.

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__

__init__(input_shape: tuple[int, ...], seed: int | None, mean_estimator: Estimator[EstimatorModuleT, None, None], std_estimator: Estimator[EstimatorModuleT, ParameterizationT, OptionalMaskerT]) -> None

Initialize necessary input_shape parameter to use Stained Glass Transform layers.

Parameters:

Name	Type	Description	Default
input_shape	tuple[int, ...]	Shape of given inputs. The first dimension may be -1, meaning variable batch size.	required
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

__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 abstractmethod

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

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
NoiseLayerOutput	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,
...     input_shape=(-1, 20),
... )
>>> 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(input_shape=(-1, 3, 32, 32))
>>> 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.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.

NoiseLayerOutput dataclass

Bases: ModelOutput

The output of BaseNoiseLayer.forward().

Methods:

Name	Description
__init_subclass__	Register subclasses as pytree nodes.
to_tuple	Convert self to a tuple containing all the attributes/keys that are not None.

__init_subclass__

__init_subclass__() -> None

Register subclasses as pytree nodes.

This is necessary to synchronize gradients when using torch.nn.parallel.DistributedDataParallel(static_graph=True) with modules that output ModelOutput subclasses.

See: https://github.com/pytorch/pytorch/issues/106690.

to_tuple

to_tuple() -> tuple[Any, ...]

Convert self to a tuple containing all the attributes/keys that are not None.

Returns:

Type	Description
tuple[Any, ...]	A tuple of all attributes/keys that are not None.