avalanche.training.EWC

class avalanche.training.EWC(model: torch.nn.modules.module.Module, optimizer: torch.optim.optimizer.Optimizer, criterion, ewc_lambda: float, mode: str = 'separate', decay_factor: typing.Optional[float] = None, keep_importance_data: bool = False, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: typing.Optional[int] = None, device=None, plugins: typing.Optional[typing.List[avalanche.core.SupervisedPlugin]] = None, evaluator: avalanche.training.plugins.evaluation.EvaluationPlugin = <avalanche.training.plugins.evaluation.EvaluationPlugin object>, eval_every=-1, **base_kwargs)[source]

Elastic Weight Consolidation (EWC) strategy.

See EWC plugin for details. This strategy does not use task identities.

__init__(model: torch.nn.modules.module.Module, optimizer: torch.optim.optimizer.Optimizer, criterion, ewc_lambda: float, mode: str = 'separate', decay_factor: typing.Optional[float] = None, keep_importance_data: bool = False, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: typing.Optional[int] = None, device=None, plugins: typing.Optional[typing.List[avalanche.core.SupervisedPlugin]] = None, evaluator: avalanche.training.plugins.evaluation.EvaluationPlugin = <avalanche.training.plugins.evaluation.EvaluationPlugin object>, eval_every=-1, **base_kwargs)[source]

Init.

Parameters
  • model – The model.

  • optimizer – The optimizer to use.

  • criterion – The loss criterion to use.

  • ewc_lambda – hyperparameter to weigh the penalty inside the total loss. The larger the lambda, the larger the regularization.

  • modeseparate to keep a separate penalty for each previous experience. onlinesum to keep a single penalty summed over all previous tasks. onlineweightedsum to keep a single penalty summed with a decay factor over all previous tasks.

  • decay_factor – used only if mode is onlineweightedsum. It specify the decay term of the importance matrix.

  • keep_importance_data – if True, keep in memory both parameter values and importances for all previous task, for all modes. If False, keep only last parameter values and importances. If mode is separate, the value of keep_importance_data is set to be True.

  • train_mb_size – The train minibatch size. Defaults to 1.

  • train_epochs – The number of training epochs. Defaults to 1.

  • eval_mb_size – The eval minibatch size. Defaults to 1.

  • device – The device to use. Defaults to None (cpu).

  • plugins – Plugins to be added. Defaults to None.

  • evaluator – (optional) instance of EvaluationPlugin for logging and metric computations.

  • 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.

  • base_kwargs – any additional BaseTemplate constructor arguments.

Methods

__init__(model, optimizer, criterion, ewc_lambda)

Init.

backward()

Run the backward pass.

criterion()

Loss function.

eval(exp_list, **kwargs)

Evaluate the current model on a series of experiences and returns the last recorded value for each metric.

eval_dataset_adaptation(**kwargs)

Initialize self.adapted_dataset.

eval_epoch(**kwargs)

Evaluation loop over the current self.dataloader.

forward()

Compute the model's output given the current mini-batch.

make_eval_dataloader([num_workers, ...])

Initializes the eval data loader. :param num_workers: How many subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0). :param pin_memory: If True, the data loader will copy Tensors into CUDA pinned memory before returning them. Defaults to True. :param kwargs: :return:.

make_optimizer()

Optimizer initialization.

make_train_dataloader([num_workers, ...])

Data loader initialization.

model_adaptation([model])

Adapts the model to the current data.

optimizer_step()

Execute the optimizer step (weights update).

stop_training()

Signals to stop training at the next iteration.

train(experiences[, eval_streams])

Training loop.

train_dataset_adaptation(**kwargs)

Initialize self.adapted_dataset.

training_epoch(**kwargs)

Training epoch.

Attributes

is_eval

True if the strategy is in evaluation mode.

mb_task_id

Current mini-batch task labels.

mb_x

Current mini-batch input.

mb_y

Current mini-batch target.