torch_concepts.data.CompletenessDataModule

class CompletenessDataModule(name: str, root: str, seed: int = 42, generation_seed: int = 42, p: int = 2, n_views: int = 10, n_concepts: int = 2, n_hidden_concepts: int = 0, n_tasks: int = 1, n_gen: int = 10000, val_size: int | float = 0.1, test_size: int | float = 0.2, batch_size: int = 512, backbone: str | Callable[[Tensor], Tensor] | None = None, precompute_embs: bool = False, force_recompute: bool = False, concept_subset: list | None = None, label_descriptions: dict | None = None, workers: int = 0, **kwargs)

__init__(name: str, root: str, seed: int = 42, generation_seed: int = 42, p: int = 2, n_views: int = 10, n_concepts: int = 2, n_hidden_concepts: int = 0, n_tasks: int = 1, n_gen: int = 10000, val_size: int | float = 0.1, test_size: int | float = 0.2, batch_size: int = 512, backbone: str | Callable[[Tensor], Tensor] | None = None, precompute_embs: bool = False, force_recompute: bool = False, concept_subset: list | None = None, label_descriptions: dict | None = None, workers: int = 0, **kwargs)

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__(name, root[, seed, ...])
from_datasets([train_dataset, val_dataset, ...])	Create an instance from torch.utils.data.Dataset.
get_dataloader([split, shuffle, batch_size])	Get the DataLoader for a specific split.
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.
remove_ignored_hparams(ignore_list)	Remove ignored hyperparameters from the stored state.
save_hyperparameters(*args[, ignore, frame, ...])	Save arguments to hparams attribute.
setup([stage, backbone_device, verbose])	Prepare the data for training, validation, or testing.
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([shuffle, batch_size])	Get the test DataLoader.
train_dataloader([shuffle, batch_size])	Get the training DataLoader.
transfer_batch_to_device(batch, device, ...)	Override this hook if your DataLoader returns tensors wrapped in a custom data structure.
val_dataloader([shuffle, batch_size])	Get the validation DataLoader.

Attributes

CHECKPOINT_HYPER_PARAMS_KEY
CHECKPOINT_HYPER_PARAMS_NAME
CHECKPOINT_HYPER_PARAMS_TYPE
backbone	The backbone model wrapper for feature extraction.
hparams	The collection of hyperparameters saved with save_hyperparameters().
hparams_initial	The collection of hyperparameters saved with save_hyperparameters().
n_samples	Total number of samples in the dataset.
name
test_len	Number of samples in the test set.
testset	The test subset.
train_len	Number of samples in the training set.
trainset	The training subset.
val_len	Number of samples in the validation set.
valset	The validation subset.