FastInstanceSegmentationLoss#

class FastInstanceSegmentationLoss(cost_mask: float = 1.0, cost_dice: float = 1.0, cost_class: float = 0.0, num_points: int = 0, ignore_index: int = -1, loss_weight_focal: float = 1.0, loss_weight_dice: float = 1.0, cls_weight_matched: float = 2.0, cls_weight_noobj: float = 0.1, focal_alpha: float = 0.25, focal_gamma: float = 2.0, truth_label: str = 'segment', aux_loss_weight: float = 1.0, momentum_loss_weight: float = 0.0, iou_loss_weight: float = 0.0)[source]#

Bases: Module

Mask2Former-style instance segmentation loss (cached, vectorized).

Computes a set-prediction loss between per-query predicted masks and ground-truth instances. Targets are cached once per call (built from the per-point truth_label ids), a fast Hungarian matcher assigns queries to instances, and matched pairs incur a focal mask loss, a Dice loss, and a classification loss (matched -> instance class, unmatched -> a no-object class). Optional momentum and IoU terms are added when their weights are positive and the predictions/targets are present. Deep supervision over aux_outputs is summed and scaled by aux_loss_weight.

forward(pred, input_dict) expects pred to be a dict with pred_masks (list of (Q, P) tensors per batch element) and optionally pred_logits/pred_momentum/pred_iou/aux_outputs, and input_dict to provide truth_label (and segment for the class term). Returns (loss, components) where components is a dict of scalar diagnostics (focal, dice, cls_matched, num_pairs, …). Registered as FastInstanceSegmentationLoss.

Parameters:

  • cost_mask (float) – Matcher weight on the focal mask cost. Defaults to 1.0.

  • cost_dice (float) – Matcher weight on the Dice cost. Defaults to 1.0.

  • cost_class (float) – Matcher weight on the classification cost. Defaults to 0.0.

  • num_points (int) – Number of sampled points for the mask cost (0 uses all points). Defaults to 0.

  • ignore_index (int) – Instance/segment id treated as void. Defaults to -1.

  • loss_weight_focal (float) – Weight on the focal mask loss. Defaults to 1.0.

  • loss_weight_dice (float) – Weight on the Dice loss. Defaults to 1.0.

  • cls_weight_matched (float) – Weight on the matched-query CE term. Defaults to 2.0.

  • cls_weight_noobj (float) – Weight on the no-object CE term. Defaults to 0.1.

  • focal_alpha (float) – Focal-loss positive-class weight. Defaults to 0.25.

  • focal_gamma (float) – Focal-loss focusing exponent. Defaults to 2.0.

  • truth_label (str) – input_dict key holding per-point instance ids. Defaults to "segment".

  • aux_loss_weight (float) – Scale on the summed auxiliary (deep-supervision) loss. Defaults to 1.0.

  • momentum_loss_weight (float) – Weight on the per-instance momentum regression term (0 disables). Defaults to 0.0.

  • iou_loss_weight (float) – Weight on the mask-IoU prediction term (0 disables). Defaults to 0.0.

Example

>>> import torch
>>> from pimm.models.losses.instance_fast import FastInstanceSegmentationLoss
>>> crit = FastInstanceSegmentationLoss(truth_label="instance")
>>> # pred: dict with per-batch mask logits; here 1 event, Q=8 queries, P=50 points
>>> pred = {"pred_masks": [torch.randn(8, 50)]}
>>> inst = torch.zeros(50, dtype=torch.long)  # per-point instance ids
>>> inst[10:30] = 1; inst[30:50] = 2
>>> input_dict = {"instance": inst.view(-1, 1)}
>>> loss, comp = crit(pred, input_dict)       # scalar loss + diagnostics dict
>>> float(loss)                               # e.g. 0.71 (focal + dice)
0.7063...
>>> sorted(comp)[:4]
['cls_matched', 'cls_noobj', 'dice', 'focal']
>>> # add pred["pred_logits"]/["pred_momentum"] + input_dict["segment"]/["momentum"]
>>> # to activate the classification / momentum terms.
FastInstanceSegmentationLoss.forward(pred: Dict, input_dict: Dict) → Tensor[source]#