Function get_predictions

modAL/dropout.py:216–288

Runs num_predictions times the prediction of the classifier on the input X and puts the predictions in a list. Args: classifier: The classifier for which the labels are to be queried. X: The pool of samples to query from. dropout_layer_i

(classifier: BaseEstimator, X: modALinput, dropout_layer_indexes: list = [],
                    num_predictions: int = 50, sample_per_forward_pass: int = 1000,
                    logits_adaptor: Callable[[torch.tensor, modALinput], torch.tensor] = default_logits_adaptor)

def get_predictions(classifier: BaseEstimator, X: modALinput, dropout_layer_indexes: list = [],
                    num_predictions: int = 50, sample_per_forward_pass: int = 1000,
                    logits_adaptor: Callable[[torch.tensor, modALinput], torch.tensor] = default_logits_adaptor):
    """
        Runs num_predictions times the prediction of the classifier on the input X 
        and puts the predictions in a list.

        Args:
            classifier: The classifier for which the labels are to be queried.
            X: The pool of samples to query from.
            dropout_layer_indexes: Indexes of the dropout layers which should be activated
                Choose indices from : list(torch_model.modules())
            num_predictions: Number of predictions which should be made
            sample_per_forward_pass: max. sample number for each forward pass. 
                The allocated RAM does mainly depend on this.
                Small number --> small RAM allocation
            logits_adaptor: Callable which can be used to adapt the output of a forward pass 
                to the required vector format for the vectorised metric functions 
        Return: 
            prediction: list with all predictions
    """

    assert num_predictions > 0, 'num_predictions must be larger than zero'
    assert sample_per_forward_pass > 0, 'sample_per_forward_pass must be larger than zero'

    predictions = []
    # set dropout layers to train mode
    set_dropout_mode(classifier.estimator.module_,
                     dropout_layer_indexes, train_mode=True)

    split_args = []

    if isinstance(X, Mapping):  # check for dict
        for k, v in X.items():

            v.detach()
            split_v = torch.split(v, sample_per_forward_pass)
            # create sub-dictionary split for each forward pass with same keys&values
            for split_idx, split in enumerate(split_v):
                if len(split_args) <= split_idx:
                    split_args.append({})
                split_args[split_idx][k] = split

    elif torch.is_tensor(X):  # check for tensor
        X.detach()
        split_args = torch.split(X, sample_per_forward_pass)
    else:
        raise RuntimeError(
            "Error in model data type, only dict or tensors supported")

    for i in range(num_predictions):

        probas = []

        for samples in split_args:
            # call Skorch infer function to perform model forward pass
            # In comparison to: predict(), predict_proba() the infer()
            # does not change train/eval mode of other layers