ertk.pytorch.dataset.MTLDataModule

class ertk.pytorch.dataset.MTLDataModule(*, dataset: Dataset, tasks: list[str], train_select: str | dict[str, collections.abc.Collection[str]] | ndarray | None = None, val_select: str | dict[str, collections.abc.Collection[str]] | ndarray | None = None, test_select: str | dict[str, collections.abc.Collection[str]] | ndarray | None = None, batch_size: int = 32, dl_num_workers: int = 0)

Bases: DataModuleAdapter

__init__(*, dataset: Dataset, tasks: list[str], train_select: str | dict[str, collections.abc.Collection[str]] | ndarray | None = None, val_select: str | dict[str, collections.abc.Collection[str]] | ndarray | None = None, test_select: str | dict[str, collections.abc.Collection[str]] | ndarray | None = None, batch_size: int = 32, dl_num_workers: int = 0)

Attributes:

prepare_data_per_node:

If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.

allow_zero_length_dataloader_with_multiple_devices:

If True, dataloader with zero length within local rank is allowed. Default value is False.

Methods

__init__(*, dataset, tasks[, train_select, ...])

Attributes:

Inherited Methods

from_datasets([train_dataset, val_dataset, ...])	Create an instance from torch.utils.data.Dataset.
load_from_checkpoint(checkpoint_path[, ...])	Primary way of loading a datamodule from a checkpoint.
load_state_dict(state_dict)	Called when loading a checkpoint, implement to reload datamodule state given datamodule state_dict.
on_after_batch_transfer(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch after it is transferred to the device.
on_before_batch_transfer(batch, dataloader_idx)	Override to alter or apply batch augmentations to your batch before it is transferred to the device.
on_exception(exception)	Called when the trainer execution is interrupted by an exception.
predict_dataloader()	An iterable or collection of iterables specifying prediction samples.
prepare_data()	Use this to download and prepare data.
save_hyperparameters(*args[, ignore, frame, ...])	Save arguments to hparams attribute.
setup([stage])	Called at the beginning of fit (train + validate), validate, test, or predict.
state_dict()	Called when saving a checkpoint, implement to generate and save datamodule state.
teardown(stage)	Called at the end of fit (train + validate), validate, test, or predict.
test_dataloader()	An iterable or collection of iterables specifying test samples.
train_dataloader()	An iterable or collection of iterables specifying training samples.
transfer_batch_to_device(batch, device, ...)	Override this hook if your DataLoader returns tensors wrapped in a custom data structure.
val_dataloader()	An iterable or collection of iterables specifying validation samples.

Attributes

CHECKPOINT_HYPER_PARAMS_KEY
CHECKPOINT_HYPER_PARAMS_NAME
CHECKPOINT_HYPER_PARAMS_TYPE
hparams	The collection of hyperparameters saved with save_hyperparameters().
hparams_initial	The collection of hyperparameters saved with save_hyperparameters().
name