Source code for

from typing import Optional, List

from torch.nn import Module
from torch.optim import Optimizer
from import ConcatDataset

from avalanche.benchmarks.utils import AvalancheConcatDataset
from import default_evaluator
from import SupervisedPlugin, EvaluationPlugin
from import SupervisedTemplate

[docs]class Cumulative(SupervisedTemplate): """Cumulative training strategy. At each experience, train model with data from all previous experiences and current experience. """
[docs] def __init__( self, model: Module, optimizer: Optimizer, criterion, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = None, device=None, plugins: Optional[List[SupervisedPlugin]] = None, evaluator: EvaluationPlugin = default_evaluator, eval_every=-1, ): """Init. :param model: The model. :param optimizer: The optimizer to use. :param criterion: The loss criterion to use. :param train_mb_size: The train minibatch size. Defaults to 1. :param train_epochs: The number of training epochs. Defaults to 1. :param eval_mb_size: The eval minibatch size. Defaults to 1. :param device: The device to use. Defaults to None (cpu). :param plugins: Plugins to be added. Defaults to None. :param evaluator: (optional) instance of EvaluationPlugin for logging and metric computations. :param eval_every: the frequency of the calls to `eval` inside the training loop. -1 disables the evaluation. 0 means `eval` is called only at the end of the learning experience. Values >0 mean that `eval` is called every `eval_every` epochs and at the end of the learning experience. """ super().__init__( model, optimizer, criterion, train_mb_size=train_mb_size, train_epochs=train_epochs, eval_mb_size=eval_mb_size, device=device, plugins=plugins, evaluator=evaluator, eval_every=eval_every, ) self.dataset = None # cumulative dataset
def train_dataset_adaptation(self, **kwargs): """ Concatenates all the previous experiences. """ if self.dataset is None: self.dataset = self.experience.dataset else: self.dataset = AvalancheConcatDataset( [self.dataset, self.experience.dataset] ) self.adapted_dataset = self.dataset