Datamanager

The datamanager wraps a dataloader and combines it with augmentations and multiprocessing.

BaseDataManager

class BaseDataManager(data, batch_size, n_process_augmentation, transforms, sampler_cls=<class 'delira.data_loading.sampler.sequential_sampler.SequentialSampler'>, data_loader_cls=None, dataset_cls=None, load_fn=<function default_load_fn_2d>, from_disc=True, **kwargs)

Bases: object

Class to Handle Data Creates Dataset , Dataloader and BatchGenerator

get_batchgen(seed=1)

Create DataLoader and Batchgenerator

Parameters:seed (int) – seed for Random Number Generator Returns:Batchgenerator Return type:MultiThreadedAugmenter Raises:AssertionError – BaseDataManager.n_batches is smaller than or equal to zero

n_batches

Returns Number of Batches based on batchsize, number of samples and number of processes

Returns:Number of Batches Return type:int Raises:AssertionError – BaseDataManager.n_samples is smaller than or equal to zero

n_samples

Number of Samples

Returns:Number of Samples Return type:int

train_test_split(*args, **kwargs)

Calls :method:`AbstractDataset.train_test_split` and returns a manager for each subset with same configuration as current manager

*args :

positional arguments for sklearn.model_selection.train_test_split

**kwargs :

keyword arguments for sklearn.model_selection.train_test_split