avalanche.logging.InteractiveLogger

class avalanche.logging.InteractiveLogger[source]

The InteractiveLogger class provides logging facilities for the console standard output. The logger shows a progress bar during training and evaluation flows and interactively display metric results as soon as they become available. The logger writes metric results after each training epoch, evaluation experience and at the end of the entire evaluation stream.

Note

To avoid an excessive amount of printed lines, this logger will not print results after each iteration. If the user is monitoring metrics which emit results after each minibatch (e.g., MinibatchAccuracy), only the last recorded value of such metrics will be reported at the end of the epoch.

Note

Since this logger works on the standard output, metrics producing images or more complex visualizations will be converted to a textual format suitable for console printing. You may want to add more loggers to your EvaluationPlugin to better support different formats.

__init__()[source]

Creates an instance of TextLogger class.

Parameters

file – destination file to which print metrics (default=sys.stdout).

Methods

__init__()

Creates an instance of TextLogger class.

after_backward(strategy, *args, **kwargs)

Called after criterion.backward() by the BaseTemplate.

after_eval(strategy, metric_values, **kwargs)

Called after eval by the BaseTemplate.

after_eval_dataset_adaptation(strategy, ...)

Called after eval_dataset_adaptation by the BaseTemplate.

after_eval_exp(strategy, metric_values, **kwargs)

Called after eval_exp by the BaseTemplate.

after_eval_forward(strategy, *args, **kwargs)

Called after model.forward() by the BaseTemplate.

after_eval_iteration(strategy, ...)

Called after the end of an iteration by the BaseTemplate.

after_forward(strategy, *args, **kwargs)

Called after model.forward() by the BaseTemplate.

after_train_dataset_adaptation(strategy, ...)

Called after train_dataset_adapatation by the BaseTemplate.

after_training(strategy, metric_values, **kwargs)

Called after train by the BaseTemplate.

after_training_epoch(strategy, ...)

Called after train_epoch by the BaseTemplate.

after_training_exp(strategy, *args, **kwargs)

Called after train_exp by the BaseTemplate.

after_training_iteration(strategy, ...)

Called after the end of a training iteration by the BaseTemplate.

after_update(strategy, *args, **kwargs)

Called after optimizer.update() by the BaseTemplate.

before_backward(strategy, *args, **kwargs)

Called before criterion.backward() by the BaseTemplate.

before_eval(strategy, metric_values, **kwargs)

Called before eval by the BaseTemplate.

before_eval_dataset_adaptation(strategy, ...)

Called before eval_dataset_adaptation by the BaseTemplate.

before_eval_exp(strategy, metric_values, ...)

Called before eval_exp by the BaseTemplate.

before_eval_forward(strategy, *args, **kwargs)

Called before model.forward() by the BaseTemplate.

before_eval_iteration(strategy, *args, **kwargs)

Called before the start of a training iteration by the BaseTemplate.

before_forward(strategy, *args, **kwargs)

Called before model.forward() by the BaseTemplate.

before_train_dataset_adaptation(strategy, ...)

Called before train_dataset_adapatation by the BaseTemplate.

before_training(strategy, metric_values, ...)

Called before train by the BaseTemplate.

before_training_epoch(strategy, ...)

Called before train_epoch by the BaseTemplate.

before_training_exp(strategy, metric_values, ...)

Called before train_exp by the BaseTemplate.

before_training_iteration(strategy, *args, ...)

Called before the start of a training iteration by the BaseTemplate.

before_update(strategy, *args, **kwargs)

Called before optimizer.update() by the BaseTemplate.

log_metrics(metric_values)

Receive a list of MetricValues to log.

log_single_metric(name, value, x_plot)

Log a metric value.

print_current_metrics()