MAEEvaluator

class MAEEvaluator(every_n_steps: int = 0, max_batches: int | None = None, log_pointclouds: bool = True)

Bases: HookBase

Validation hook for masked-autoencoder (MAE) pretraining.

Runs the MAE model over trainer.val_loader on rank 0 only (other ranks synchronize), averaging the reconstruction losses (total, coordinate, feature) and the actual mask ratio over the valid batches (batches whose loss == 0 from empty masks are skipped). On the first batch it can log ground-truth vs reconstructed point-cloud visualizations to Weights & Biases. It publishes the NEGATIVE average validation loss as the checkpoint-selection metric (current_metric_value = -avg_loss, current_metric_name = neg_val_loss) so that higher is better. Uses AMP autocast when cfg.enable_amp is set. Runs after every step when every_n_steps > 0 (when (global_iter + 1) % every_n_steps == 0), otherwise after each epoch; only when cfg.evaluate is true and val_loader is not None. Registered as MAEEvaluator (use as type in a hooks=[...] entry).

Parameters:

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

• max_batches (int | None) – Cap on validation batches per eval for speed; None uses all batches. Defaults to None.

• log_pointclouds (bool) – Log first-batch GT/reconstruction point clouds to wandb. Defaults to True.

Note

The selection metric is NEGATIVE validation loss (neg_val_loss); higher (less negative) is better. Evaluation runs on rank 0 only.

Example

Add to cfg.hooks for MAE pretraining; every every_n_steps it runs reconstruction validation on rank 0:

hooks = [dict(type="MAEEvaluator", every_n_steps=1000, max_batches=50)]
# → every 1000 steps logs  val/loss, val/coord_loss, val/feat_loss,
#   val/mask_ratio  to the writer (and first-batch GT/recon point clouds
#   to wandb), then sets the checkpoint-selection metric to neg_val_loss
#   (= -avg loss, so higher is better)

after_epoch()

Run MAE validation after epochs when step cadence is disabled.

after_step()

Run MAE validation on the configured step cadence.

eval()

Average reconstruction losses and optional point-cloud visualizations.