ncalab.training.kfold

Classes

`TrainValRecord`	Helper class, storing a training / validation data split to generate
`SplitDefinition`	Stores a k-fold cross-validation split.
`KFoldCrossValidationTrainer`

Module Contents

class ncalab.training.kfold.TrainValRecord(train: List[str], val: List[str])

Helper class, storing a training / validation data split to generate respective DataLoader objects.

Parameters:

train (List[str]) – List of training image file paths
val (List[str]) – List of validation image file paths

train

val

dataloaders(DatasetType: Type, path: pathlib.Path | pathlib.PosixPath, transform=None, batch_sizes=None): Generate a pair of training and validation DataLoader objects, based on a given DataSet subtype.

class ncalab.training.kfold.SplitDefinition

Stores a k-fold cross-validation split.

folds = []

dataloader_test = None

static read(path: pathlib.PosixPath) → SplitDefinition

Reads json files with split definitions, similar to those created by nnUNet.

Format is like

[
    {
        "train": [ "filename0", "filename1",... ]
        "val": [ "filename2", "filename3",... ]
    },
    {
        ...
    }
]

Parameters:: path – Path to JSON file containing split definition.
Returns:: SplitDefinition object
Return type:: SplitDefinition

__len__() → int

__getitem__(idx) → TrainValRecord

class ncalab.training.kfold.KFoldCrossValidationTrainer(trainer: ncalab.training.trainer.BasicNCATrainer, split: SplitDefinition)

Parameters:

[BasicNCATrainer] (trainer) – BasicNCATrainer, to train each individual fold.
[SplitDefinition] (split) – Definition of the split used for k-fold cross-training.

trainer

model_prototype

model_name

split

train(DatasetType: Type, datapath: pathlib.Path | pathlib.PosixPath, transform, batch_sizes: None | Dict = None, save_every: int | None = None) → List[ncalab.training.traininghistory.TrainingHistory]

Run training loop with a single function call.

Parameters:

[Type] (DatasetType) – Type of dataset class to use.
[Path] (datapath) – _description_
transform – Data transform, e.g. initialized via Albumentations.
batch_sizes – Dict of batch sizes per set, e.g. {“train”: 8, “val”: 16}. Defaults to None.
[int] (save_every) – _description_. Defaults to None.
plot_function – Plot function override. If None, use model’s default. Defaults to None.

Returns [List[TrainingHistory]]:

List of TrainingHistory objects, one per fold.