minerva.utils.data

Classes

`RandomDataModule`
`SimpleDataset`

Functions

`full_dataset_from_dataloader`(dataloader)
`get_full_data_split`(data_module, stage)
`get_split_dataloader`(stage, data_module)

Module Contents

class minerva.utils.data.RandomDataModule(data_shape, label_shape=None, num_classes=None, num_train_samples=128, num_val_samples=8, num_test_samples=8, num_predict_samples=8, batch_size=8)

Bases: lightning.LightningDataModule

Parameters:

data_shape (Tuple[int, Ellipsis])
label_shape (int | Tuple[int, Ellipsis])
num_classes (int)
num_train_samples (int)
num_val_samples (int)
num_test_samples (int)
num_predict_samples (int)
batch_size (int)

_generate_data(num_samples, data_shape, label_shape, num_classes)

predict_dataloader()

setup(stage)

test_dataloader()

train_dataloader()

val_dataloader()

class minerva.utils.data.SimpleDataset(data, label=None)

Parameters:

data (torch.Tensor)
label (torch.Tensor)

__getitem__(idx)

__len__()

minerva.utils.data.full_dataset_from_dataloader(dataloader)

Parameters:: dataloader (torch.utils.data.DataLoader)

minerva.utils.data.get_full_data_split(data_module, stage)

Parameters:

data_module (lightning.LightningDataModule)
stage (str)

minerva.utils.data.get_split_dataloader(stage, data_module)

Parameters:

stage (str)
data_module (lightning.LightningDataModule)

Return type:

torch.utils.data.DataLoader