predict

maite.tasks.predict(*, model, dataloader=None, dataset=None, batch_size=1, augmentation=None, return_augmented_data=False)

Make predictions for a given model & data source with optional augmentation.

Parameters:

modelSomeModel

Maite Model object.

dataloaderSomeDataloader | None, (default=None)

Compatible maite dataloader.

datasetSomeDataset | None, (default=None)

Compatible maite dataset.

batch_sizeint, (default=1)

Batch size for use with dataset (ignored if dataset=None).

augmentationSomeAugmentation | None, (default=None)

Compatible maite augmentation.

return_augmented_databool, (default=False)

Set to True to return post-augmentation data as a function output.

Returns:

tuple[Sequence[Sequence[SomeTargetType], Sequence[tuple[Sequence[SomeInputType], Sequence[SomeTargetType], Sequence[SomeMetadataType]]],

A tuple of the predictions (as a sequence of batches) and a sequence of tuples containing the information associated with each batch. Note that the second return argument will be empty if return_augmented_data is False.

Raises:

ValueError

If neither a dataloader nor a dataset is provided.