callbacks

Bases: Callback

Exponentially decay BatchNorm momentum over training epochs.

Reference implementation: facebookresearch/votenet (train.py).

At the start of each training epoch, every nn.BatchNorm* module in pl_module.model has its momentum set to

with \(m_0\) the initial momentum, \(\gamma\) the decay rate, \(s\) the decay step (epochs) and \(m_\text{clip}\) the floor.

Parameters:

• bn_momentum_init (float, default: 0.5 ) –

  Initial BatchNorm momentum \(m_0\).

• bn_decay_rate (float, default: 0.5 ) –

  Per-step multiplicative decay \(\gamma\).

• bn_decay_step (int, default: 20 ) –

  Number of epochs between decay steps \(s\).

• bn_momentum_clip (float, default: 0.001 ) –

  Lower bound on the momentum \(m_\text{clip}\).

Methods:

• on_train_epoch_start –

  Set the decayed momentum on every BatchNorm module of the model.

on_train_epoch_start(
    trainer: Trainer, pl_module: LightningModule
) -> None

Bases: Callback

Accumulate and log a torchmetrics metric over a validation/test epoch.

Model- and task-agnostic: the LightningModule's validation_step / test_step returns a {preds_key: ..., target_key: ...} dict (the repo's Lit* modules do), and this callback updates a torchmetrics Metric with it each batch, logging the epoch value as {stage}/{name}. List one per metric to plug accuracy, mIoU, mAP, ... onto any model.

metric is a ready torchmetrics Metric whose num_classes and ignore_index the caller sets to match the model and the criterion, so the callback holds it as-is and stays model-agnostic.

A step output may carry extra entries beyond preds_key / target_key (the Lit* modules' metric_input_keys passthrough); entries matching a parameter name of the metric's update signature (inspected once at construction) are forwarded as keyword arguments and the rest are ignored, so each metric declares the inputs it consumes.

A metric whose compute returns a dict (e.g. MeanAveragePrecision3D returning mAP@0.25 / mAP@0.5) is logged one entry per key as {stage}/{key}; a scalar metric is logged as {stage}/{name}.

Parameters:

• metric (Metric) –

  A torchmetrics Metric, e.g. JaccardIndex(task="multiclass", num_classes=..., ignore_index=...).

• name (str) –

  Metric name; logged as {stage}/{name} (ignored for dict-valued metrics, whose keys name themselves).

• stages (Sequence[str], default: ('val', 'test') ) –

  Stages to score; each listed stage's *_step must return preds_key / target_key.

• preds_key (str, default: 'preds' ) –

  Key in the step output holding predictions (logits, probabilities, labels, or detections).

• target_key (str, default: 'target' ) –

  Key in the step output holding the targets.

• batch_key (Optional[str], default: None ) –

  Optional key in the step output holding the per-point shape index, forwarded as a third positional argument to metric.update (packed multi-shape metrics like InstancePartMeanIoU need it). Leave None for two-argument metrics.

• prog_bar (bool, default: True ) –

  Whether to show the metric on the progress bar.

Methods:

• on_validation_epoch_start –

  Reset the metric when val is scored.

• on_validation_batch_end –

  Update the metric with the step output when val is scored.

• on_validation_epoch_end –

  Compute and log the metric when val is scored.

• on_test_epoch_start –

  Reset the metric when test is scored.

• on_test_batch_end –

  Update the metric with the step output when test is scored.

• on_test_epoch_end –

  Compute and log the metric when test is scored.

on_validation_epoch_start(
    trainer: Trainer, pl_module: LightningModule
) -> None

on_validation_batch_end(
    trainer: Trainer,
    pl_module: LightningModule,
    outputs: Any,
    batch: Dict[str, Any],
    batch_idx: int,
    dataloader_idx: int = 0,
) -> None

on_validation_epoch_end(
    trainer: Trainer, pl_module: LightningModule
) -> None

on_test_epoch_start(
    trainer: Trainer, pl_module: LightningModule
) -> None

on_test_batch_end(
    trainer: Trainer,
    pl_module: LightningModule,
    outputs: Any,
    batch: Dict[str, Any],
    batch_idx: int,
    dataloader_idx: int = 0,
) -> None

on_test_epoch_end(
    trainer: Trainer, pl_module: LightningModule
) -> None