avalanche.training.ExpertGateStrategy

class avalanche.training.ExpertGateStrategy(model: ~torch.nn.modules.module.Module, optimizer: ~torch.optim.optimizer.Optimizer, criterion=CrossEntropyLoss(), train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int | None = None, device=None, plugins: ~typing.List[~avalanche.core.SupervisedPlugin] | None = None, evaluator: ~avalanche.training.plugins.evaluation.EvaluationPlugin = <function default_evaluator>, eval_every=-1, ae_train_mb_size=1, ae_train_epochs=2, ae_latent_dim=100, ae_lr=0.001, temp=2, rel_thresh=0.85, **base_kwargs)

Expert Gate strategy. New experts are trained and added to the model as tasks are learned sequentially.

Technique introduced in: ‘Aljundi, Rahaf, Punarjay Chakravarty, and Tinne Tuytelaars. “Expert gate: Lifelong learning with a network of experts.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.’ https://arxiv.org/abs/1611.06194

To use this strategy you need to instantiate an ExpertGate model. See the ExpertGate plugin for more details.

__init__(model: ~torch.nn.modules.module.Module, optimizer: ~torch.optim.optimizer.Optimizer, criterion=CrossEntropyLoss(), train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int | None = None, device=None, plugins: ~typing.List[~avalanche.core.SupervisedPlugin] | None = None, evaluator: ~avalanche.training.plugins.evaluation.EvaluationPlugin = <function default_evaluator>, eval_every=-1, ae_train_mb_size=1, ae_train_epochs=2, ae_latent_dim=100, ae_lr=0.001, temp=2, rel_thresh=0.85, **base_kwargs)

Init.

Parameters:

• model – PyTorch model.

• optimizer – PyTorch optimizer.

• criterion – loss function.

• train_mb_size – mini-batch size for training.

• train_epochs – number of training epochs.

• eval_mb_size – mini-batch size for eval.

• device – PyTorch device where the model will be allocated.

• plugins – (optional) list of StrategyPlugins.

• evaluator – (optional) instance of EvaluationPlugin for logging and metric computations. None to remove logging.

• 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

• ae_train_mb_size – mini-batch size for training of the autoencoder

• ae_train_epochs – number of training epochs for the autoencoder

• ae_lr – the learning rate for the autoencoder training

using vanilla SGD :param temp: the temperature hyperparameter when selecting the expert during the forward method

BaseTemplate constructor arguments.

Methods

__init__(model, optimizer[, criterion, ...])	Init.
backward()	Run the backward pass.
check_model_and_optimizer([...])
criterion()	Loss function for supervised problems.
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, shuffle, ...])	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([reset_optimizer_state])	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.
mbatch	Current mini-batch.
mb_output	Model's output computed on the current mini-batch.
dataloader	Dataloader.
optimizer	PyTorch optimizer.
loss	Loss of the current mini-batch.
train_epochs	Number of training epochs.
train_mb_size	Training mini-batch size.
eval_mb_size	Eval mini-batch size.
retain_graph	Retain graph when calling loss.backward().
evaluator	EvaluationPlugin used for logging and metric computations.
clock	Incremental counters for strategy events.
adapted_dataset	Data used to train.
model	PyTorch model.
device	PyTorch device where the model will be allocated.
plugins	List of `SupervisedPlugin`s. .
experience	Current experience.
is_training	True if the strategy is in training mode.
current_eval_stream	Current evaluation stream.