FUTURE MODEL

This is the base class for all models that support future prediction, i.e., zero-shot prediction.

It extends the ContinualModel class and adds the future_forward method, which should be implemented by all models that inherit from this class. Such a method should take an input tensor and return a tensor representing the future prediction. This method is used by the future prediction evaluation protocol.

The change_transform method is used to update the transformation applied to the input data. This is useful when the model is trained on a dataset and then evaluated on a different dataset. In this case, the transformation should be updated to match the new dataset.

Classes

class models.utils.future_model.FutureModel(backbone, loss, args, transform, dataset=None)

Bases: ContinualModel

change_transform(dataset)

Change the transformation applied to the input data. In Zero-shot learning, the model is trained on a dataset and then evaluated on a different one. In this case, the transformation should be updated to match the new dataset.

Parameters:

dataset (ContinualDataset) – An instance of the dataset on which the model will be evaluated on new classes.

future_forward(x, **kwargs)

Function that implements the forward pass of the model for future prediction. This method should be implemented by all models that inherit from this class.

Parameters:

x (torch.Tensor) – The input tensor.

Returns:

The output tensor representing the future prediction.

Return type:

torch.Tensor