SR-Forge

Regularization

Penalties on a single tensor: no target, any shape.

Regularizer

Bases: Metric

Single-tensor magnitude penalty.

Takes one tensor field, applies a pointwise penalty function, and reduces the result to a per-sample scalar via :meth:Metric.reduce. Useful whenever you want to penalise the magnitude (or some function of the magnitude) of an auxiliary model output as part of the total loss.

Built-in penalties:

============== ============================ ================================== penalty= Function applied to x Common name ============== ============================ ================================== "identity" x Mean of the field "abs" |x| L1 norm / sparsity "square" x² L2 norm / magnitude "exp" exp(x) Penalises positive log-quantities "entropy" -x·log(x) Element-wise entropy "huber" smooth-L1 with huber_delta Quadratic near 0, linear elsewhere callable any Tensor → Tensor Custom — same-shape output required ============== ============================ ==================================

Multiple :class:Regularizer instances can be combined in a :class:LossCombiner with different penalties and/or fields — each is a regular Metric with its own weight and IO binding.

Parameters:

Name	Type	Description	Default
penalty	Union[str, Callable]	Either a built-in name (see table) or a callable Tensor → Tensor that returns the same shape as its input. Default "identity".	'identity'
huber_delta	float	Transition point for penalty="huber". Ignored for all other penalties. Default 1.0.	1.0

Example::

# L1 sparsity penalty on an attention map
Regularizer(penalty="abs").set_io({"inputs": {"x": "attention"}})

# L2 magnitude penalty (with mask)
Regularizer(penalty="square").set_io(
    {"inputs": {"x": "noise_pred", "y_mask": "valid_mask"}}
)

# Huber with custom delta
Regularizer(penalty="huber", huber_delta=0.5)

# Custom — log-L1 (robust soft-L1)
Regularizer(penalty=lambda x: torch.log1p(x.abs()))