avalanche.training.MIR

class avalanche.training.MIR(*, 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], mem_size: int, subsample: int, batch_size_mem: int = 1, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = 1, device: str | ~torch.device = 'cpu', plugins: ~typing.List[~avalanche.core.SupervisedPlugin] | None = None, evaluator: ~avalanche.training.plugins.evaluation.EvaluationPlugin | ~typing.Callable[[], ~avalanche.training.plugins.evaluation.EvaluationPlugin] = <function default_evaluator>, eval_every=-1, **base_kwargs)

Maximally Interfered Replay Strategy See ER_MIR plugin for details.

__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], mem_size: int, subsample: int, batch_size_mem: int = 1, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = 1, device: str | ~torch.device = 'cpu', plugins: ~typing.List[~avalanche.core.SupervisedPlugin] | None = None, evaluator: ~avalanche.training.plugins.evaluation.EvaluationPlugin | ~typing.Callable[[], ~avalanche.training.plugins.evaluation.EvaluationPlugin] = <function default_evaluator>, eval_every=-1, **base_kwargs)

Init. :param model: The model. :param optimizer: The optimizer to use. :param criterion: The loss criterion to use. :param mem_size: Amount of fixed memory to use :param subsample: Size of the initial sample

from which to select the replay batch

Parameters:

• batch_size_mem – Size of the replay batch after loss-based selection

• 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, ...)	Init. :param model: The model. :param optimizer: The optimizer to use. :param criterion: The loss criterion to use. :param mem_size: Amount of fixed memory to use :param subsample: Size of the initial sample from which to select the replay batch :param batch_size_mem: Size of the replay batch after loss-based selection :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. :param **base_kwargs: any additional BaseTemplate constructor arguments.
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.