PCALoss

class lightning_pose.losses.losses.PCALoss(loss_name: Literal['pca_singleview', 'pca_multiview'], components_to_keep: int | float = 0.95, empirical_epsilon_percentile: float = 0.99, epsilon: float | None = None, empirical_epsilon_multiplier: float = 1.0, mirrored_column_matches: ListConfig | List | None = None, columns_for_singleview_pca: ListConfig | List | None = None, data_module: BaseDataModule | UnlabeledDataModule | None = None, log_weight: float = 0.0, device: Literal['cuda', 'cpu'] | device = 'cpu', centering_method: Literal['mean', 'median'] | None = None, **kwargs)

Bases: Loss

Penalize predictions that fall outside a low-dimensional subspace.

Methods Summary

__call__(keypoints_pred[, stage])	Call self as a function.
compute_loss(predictions)
remove_nans(**kwargs)

Methods Documentation

__call__(keypoints_pred: Tensor, stage: Literal['train', 'val', 'test'] | None = None, **kwargs) → Tuple[Tensor[Tensor], List[dict]]

Call self as a function.

compute_loss(predictions: Tensor[Tensor]) → Tensor[Tensor]

remove_nans(**kwargs)