avalanche.training.MER

class avalanche.training.MER(*, model: ~torch.nn.modules.module.Module, optimizer: ~torch.optim.optimizer.Optimizer, criterion: ~torch.nn.modules.module.Module | ~typing.Callable[[~torch.Tensor, ~torch.Tensor], ~torch.Tensor] = CrossEntropyLoss(), mem_size=200, batch_size_mem=10, n_inner_steps=5, beta=0.1, gamma=0.1, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = 1, device: str | ~torch.device = 'cpu', plugins: ~typing.Sequence[~avalanche.core.SupervisedPlugin] | None = None, evaluator: ~avalanche.training.plugins.evaluation.EvaluationPlugin | ~typing.Callable[[], ~avalanche.training.plugins.evaluation.EvaluationPlugin] = <function default_evaluator>, eval_every=-1, peval_mode='epoch', **kwargs)[source]
__init__(*, model: ~torch.nn.modules.module.Module, optimizer: ~torch.optim.optimizer.Optimizer, criterion: ~torch.nn.modules.module.Module | ~typing.Callable[[~torch.Tensor, ~torch.Tensor], ~torch.Tensor] = CrossEntropyLoss(), mem_size=200, batch_size_mem=10, n_inner_steps=5, beta=0.1, gamma=0.1, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = 1, device: str | ~torch.device = 'cpu', plugins: ~typing.Sequence[~avalanche.core.SupervisedPlugin] | None = None, evaluator: ~avalanche.training.plugins.evaluation.EvaluationPlugin | ~typing.Callable[[], ~avalanche.training.plugins.evaluation.EvaluationPlugin] = <function default_evaluator>, eval_every=-1, peval_mode='epoch', **kwargs)[source]
Implementation of Look-ahead MAML (LaMAML) algorithm in Avalanche

using Higher library for applying fast updates.

Parameters:
  • model – PyTorch model.

  • optimizer – PyTorch optimizer.

  • criterion – loss function.

  • mem_size – maximum size of the buffer.

  • batch_size_mem – number of samples to retrieve from buffer for each sample.

  • n_inner_steps – number of inner updates per sample.

  • beta – coefficient for within-batch Reptile update.

  • gamma – coefficient for within-task Reptile update.

Methods

__init__(*, model, optimizer[, criterion, ...])

Implementation of Look-ahead MAML (LaMAML) algorithm in Avalanche

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.