Class - GaNDLFDataLoaderWrapper

class openfl.federated.data.loader_gandlf.GaNDLFDataLoaderWrapper(data_path=None, **kwargs)

Bases: DataLoader

A class used to represent a data loader for the Generally Nuanced Deep Learning Framework (GaNDLF).

train_csv

Path to the training CSV file.

Type:

str

val_csv

Path to the validation CSV file.

Type:

str

train_dataloader

DataLoader object for the training data.

Type:

DataLoader

val_dataloader

DataLoader object for the validation data.

Type:

DataLoader

feature_shape

Shape of an example feature array.

Type:

tuple

__init__(data_path=None, **kwargs)

Initializes the GaNDLFDataLoaderWrapper object.

Parameters:

• data_path (str, optional) – The path to the directory containing the data. If None, initialize for model creation only.

• **kwargs – Additional arguments to pass to the function.

Methods

__init__([data_path])	Initializes the GaNDLFDataLoaderWrapper object.
get_feature_shape()	Returns the shape of an example feature array.
get_infer_loader()	Returns the data loader for inferencing data.
get_train_data_size()	Returns the total number of training samples.
get_train_loader([batch_size, num_batches])	Returns the data loader for the training data.
get_valid_data_size()	Returns the total number of validation samples.
get_valid_loader([batch_size])	Returns the data loader for the validation data.
set_dataloaders(train_dataloader, val_dataloader)	Sets the data loaders for the training and validation data.

get_feature_shape()

Returns the shape of an example feature array.

Returns:

The shape of an example feature array.

derives shape from the GANDLF config’s patch_size if available

Return type:

tuple

get_infer_loader()

Returns the data loader for inferencing data.

Raises:

NotImplementedError – This method must be implemented by a child class.

get_train_data_size()

Returns the total number of training samples.

Returns:

The total number of training samples or 0 if not loaded.

Return type:

int

get_train_loader(batch_size=None, num_batches=None)

Returns the data loader for the training data.

Parameters:

• batch_size (int, optional) – The batch size for the data loader (default is None).

• num_batches (int, optional) – The number of batches for the data loader (default is None).

Returns:

The DataLoader object for the training data.

Return type:

DataLoader

get_valid_data_size()

Returns the total number of validation samples.

Returns:

The total number of validation samples or 0 if not loaded.

Return type:

int

get_valid_loader(batch_size=None)

Returns the data loader for the validation data.

Parameters:

batch_size (int, optional) – The batch size for the data loader (default is None).

Returns:

The DataLoader object for the validation data.

Return type:

DataLoader

set_dataloaders(train_dataloader, val_dataloader)

Sets the data loaders for the training and validation data.

Parameters:

• train_dataloader (DataLoader) – The DataLoader object for the training data.

• val_dataloader (DataLoader) – The DataLoader object for the validation data.