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)[source]
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)[source]
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.
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, ...])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.