avalanche.training.JointTraining

class avalanche.training.JointTraining(model: ~torch.nn.modules.module.Module, optimizer: ~torch.optim.optimizer.Optimizer, criterion, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = 1, device='cpu', plugins: ~typing.Optional[~typing.Sequence[SupervisedPlugin]] = None, evaluator=<avalanche.training.plugins.evaluation.EvaluationPlugin object>, eval_every=-1)

Joint training on the entire stream.

JointTraining performs joint training (also called offline training) on the entire stream of data. This means that it is not a continual learning strategy but it can be used as an “offline” upper bound for them.

__init__(model: ~torch.nn.modules.module.Module, optimizer: ~torch.optim.optimizer.Optimizer, criterion, train_mb_size: int = 1, train_epochs: int = 1, eval_mb_size: int = 1, device='cpu', plugins: ~typing.Optional[~typing.Sequence[SupervisedPlugin]] = None, evaluator=<avalanche.training.plugins.evaluation.EvaluationPlugin object>, eval_every=-1)

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 to run the model.

• 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.

Methods

__init__(model, optimizer, criterion[, ...])	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 strategy's model for all experiences.
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)	Concatenates all the datastream.
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.