PrepareDALI

class lightning_pose.data.dali.PrepareDALI

Bases: object

All the DALI stuff in one place.

Big picture: this will initialize the pipes and dataloaders for both training and prediction.

Attributes Summary

num_iters

Number of dataloader iterations required to process all frames.

Methods Summary

__call__()

Returns a LightningWrapper object.

Attributes Documentation

num_iters

Number of dataloader iterations required to process all frames.

Returns:

Integer count of how many times the dataloader must be enumerated to exhaust all video frames for the current train_stage and model_type configuration.

Methods Documentation

__call__() → LitDaliWrapper

Returns a LightningWrapper object.

__init__(train_stage: Literal['predict', 'train'], model_type: Literal['base', 'context'], filenames: list[str] | list[list[str]], resize_dims: list[int], dali_config: dict | DictConfig | ListConfig | None = None, imgaug: str | None = 'default', num_threads: int = 1) → None

Initialize DALI pipelines and dataloaders for training or prediction.

Parameters:

• train_stage – whether to set up pipelines for "train" or "predict".

• model_type – "base" for standard single-frame models, "context" for MHCRNN models that consume a temporal window.

• filenames – for single-view models, a flat list of video file paths; for multi-view models, a list of per-view lists of video file paths.

• resize_dims – [height, width] to resize frames to before feeding the model.

• dali_config – DALI-specific config dict; falls back to package defaults when None.

• imgaug – name of the augmentation pipeline to apply during training (e.g. "dlc"); pass "default" for resize-only or None to disable.

• num_threads – number of CPU threads used by DALI pipelines.

Raises:

FileNotFoundError – if any path in filenames does not exist or is not a file.

__new__(**kwargs)