SemSegEvaluator

class SemSegEvaluator(write_cls_iou=False, every_n_steps=0, ignore_index=-1, macro_ignore_class_ids=None, per_instance_metrics=False)

Bases: HookBase

Evaluate point-wise semantic segmentation on the validation loader.

Runs the model over trainer.val_loader, argmaxes the semantic logits (seg_logits or sem_logits), and accumulates per-class intersection/union/target counts (all-reduced across ranks). It reports mIoU, mAcc, allAcc and macro precision/recall/F1 plus a per-class table, and publishes mIoU as the checkpoint-selection metric via trainer.comm_info["current_metric_value"] / ["current_metric_name"]. When origin_coord is present, predictions are interpolated back to the original (pre-grid) points with a kNN query. Optionally computes majority-vote per-instance class metrics. Runs after every step when every_n_steps > 0 (on iterations where (global_iter + 1) % every_n_steps == 0), otherwise after each epoch; only when cfg.evaluate is true. Registered as SemSegEvaluator (use as type in a hooks=[...] entry).

Parameters:

• write_cls_iou (bool) – Also log per-class IoU/F1/precision/recall to the writer. Defaults to False.

• every_n_steps (int) – Step cadence; 0 evaluates once per epoch. Defaults to 0.

• ignore_index (int) – Label id ignored when computing per-instance majority-vote metrics. Defaults to -1.

• macro_ignore_class_ids (Sequence[int] | None) – Class ids excluded from macro (mean) metrics; deduplicated and sorted. Defaults to None.

• per_instance_metrics (bool) – Compute majority-vote per-event instance class metrics when instance labels are available. Defaults to False.

Note

The selection metric is mIoU (higher is better). Per-instance metrics additionally require instance labels in the batch and per-event boundary tracking; they are logged but do not change the selection metric.

Example

Add to cfg.hooks before the checkpoint saver; every every_n_steps it validates and sets the selection metric:

hooks = [
    dict(type="SemSegEvaluator", every_n_steps=1000,
         write_cls_iou=True),
]
# → every 1000 steps logs  val/loss, val/mIoU, val/mAcc, val/allAcc,
#   val/mPrecision, val/mRecall, val/mF1  (+ per-class IoU/F1/Precision/
#   Recall with write_cls_iou) and sets the checkpoint-selection metric
#   to mIoU

after_epoch()

Run semantic evaluation after epochs when step cadence is disabled.

after_step()

Run semantic evaluation on the configured step cadence.

after_train()

Log the best semantic metric tracked by checkpoint hooks.

eval()

Compute point-wise validation metrics and publish mIoU to comm_info.

eval_per_instance(all_preds, all_segments, all_instances)

Compute majority-vote class metrics over per-event instances.