LovaszLoss#

class LovaszLoss(mode: str, class_seen: int | None = None, class_weights: Dict[int, float] | Tensor | list | None = None, per_image: bool = False, ignore_index: int | None = None, loss_weight: float = 1.0, penalize_pred: bool = False)[source]#

Bases: _Loss

Lovasz loss: a direct surrogate for the IoU (Jaccard) metric.

Optimizes the Lovasz extension of the Jaccard index (Berman et al. 2018). forward(y_pred, y_true) takes y_pred of shape (N, C, ...) (logits; soft-maxed for multiclass) and y_true of shape (N, ...) (class indices) and returns the loss scaled by loss_weight. Supports binary, multiclass, and multilabel segmentation. Registered as LovaszLoss – use in a criteria=[...] list, typically paired with cross-entropy.

Parameters:

  • mode (str) – Loss mode, one of "binary", "multiclass", "multilabel".

  • class_seen (int | None) – Optional restriction of which classes contribute to the loss. Defaults to None.

  • class_weights (dict[int, float] | torch.Tensor | list | None) – Optional per-class weights (dict from class index to weight, tensor, or list). Defaults to None.

  • per_image (bool) – Compute the loss per image then average, instead of over the whole batch. Defaults to False.

  • ignore_index (int | None) – Label marking ignored pixels. Defaults to None.

  • loss_weight (float) – Global scale on the returned loss. Defaults to 1.0.

  • penalize_pred (bool) – Weight per-pixel errors by the predicted class (rather than weighting the per-class loss). Defaults to False.

Example

>>> import torch
>>> from pimm.models.losses.builder import build_criteria
>>> crit = build_criteria([dict(type="LovaszLoss", mode="multiclass",
...                              loss_weight=1.0)])
>>> pred = torch.randn(10, 3)              # (N=10 points, C=3 classes) logits
>>> target = torch.randint(0, 3, (10,))   # class indices
>>> crit(pred, target)                    # scalar Jaccard surrogate
tensor(0.7265)
LovaszLoss.forward(y_pred, y_true)[source]#