ConceptDetection2DTrainTestHandle

class hybrid_learning.concepts.models.concept_models.concept_detection.ConceptDetection2DTrainTestHandle(concept_model, data, *, model_output_transform=None, metric_input_transform=None, **kwargs)

Bases: TrainEvalHandle

Train test handle for concept localization models. Applies sensible defaults to model_output_transform.

Public Data Attributes:

DEFAULT_MASK_INTERPOLATION

Interpolation method used in default transforms for resizing masks to activation map size.

Inherited from : py: class:TrainEvalHandle

LOSS_KEY	Key for the loss evaluation results.
NLL_KEY	Key for the proper scoring evaluation function used for second stage training.
kpi_fns	Metric and loss functions.
settings	The current training settings as dictionary.

Public Methods:

Inherited from : py: class:TrainEvalHandle

add_callbacks(callbacks)	Append the given callbacks.
remove_callback(callback)	Remove a single given callback.
loader([data, mode, batch_size, shuffle, ...])	Prepare and return a torch data loader from the dataset according to settings.
reset_optimizer([optimizer, device, model])	Move model to correct device, init optimizer to parameters of model.
reset_training_handles([optimizer, device, ...])	(Re)set all handles associated with training, and move to device.
reset_kpis([kpis])	Resets aggregating kpis
train([train_loader, val_loader, ...])	Train the model according to the specified training parameters.
cross_validate([num_splits, train_val_data, ...])	Record training results for num_splits distinct val splits.
train_val_one_epoch([pbar_desc, ...])	Train for one epoch, evaluate, and return history and test results.
train_one_epoch([train_loader, ...])	Train for one epoch and return history results as pandas.DataFrame.
evaluate([mode, val_loader, prefix, ...])	Evaluate the model wrt.
second_stage_train([callback_context])	Do a second stage training for calibration using Laplace approximation.

Special Methods:

__init__(concept_model, data, *[, ...])

Init.

Inherited from : py: class:TrainEvalHandle

__repr__()	Return repr(self).
__init__(concept_model, data, *[, ...])	Init.

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Parameters

• concept_model (ConceptDetectionModel2D) –

• data (DataTriple) –

• model_output_transform (TupleTransforms) –

• metric_input_transform (TupleTransforms) –

__init__(concept_model, data, *, model_output_transform=None, metric_input_transform=None, **kwargs)

Init.

For further parameter descriptions see __init__() of TrainEvalHandle.

Parameters

• concept_model (ConceptDetectionModel2D) – the concept localization model to work on with concept.

• data (DataTriple) – data for the concept model, i.e. Sequence of tuples (activation, mask)

• model_output_transform (Optional[TupleTransforms]) –

• metric_input_transform (Optional[TupleTransforms]) –

DEFAULT_MASK_INTERPOLATION: str = 'bilinear'

Interpolation method used in default transforms for resizing masks to activation map size. Argument may be one of the modes accepted by torch.nn.functional.interpolate().