predict#

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

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: