avalanche.evaluation.metric_definitions.GenericPluginMetric

class avalanche.evaluation.metric_definitions.GenericPluginMetric(metric: TMetric, reset_at: Literal['iteration', 'epoch', 'experience', 'stream', 'never'] = 'experience', emit_at: Literal['iteration', 'epoch', 'experience', 'stream'] = 'experience', mode: Literal['train'] = 'train')

class avalanche.evaluation.metric_definitions.GenericPluginMetric(metric: TMetric, reset_at: Literal['iteration', 'experience', 'stream', 'never'] = 'experience', emit_at: Literal['iteration', 'experience', 'stream'] = 'experience', mode: Literal['eval'] = 'eval')

This class provides a generic implementation of a Plugin Metric. The user can subclass this class to easily implement custom plugin metrics.

__init__(metric: TMetric, reset_at: Literal['iteration', 'epoch', 'experience', 'stream', 'never'] = 'experience', emit_at: Literal['iteration', 'epoch', 'experience', 'stream'] = 'experience', mode: Literal['train'] = 'train')

__init__(metric: TMetric, reset_at: Literal['iteration', 'experience', 'stream', 'never'] = 'experience', emit_at: Literal['iteration', 'experience', 'stream'] = 'experience', mode: Literal['eval'] = 'eval')

Creates an instance of a plugin metric.

Child classes can safely invoke this (super) constructor as the first experience.

Methods

__init__()	Creates an instance of a plugin metric.
after_backward(strategy)
after_eval(strategy)
after_eval_dataset_adaptation(strategy)
after_eval_exp(strategy)
after_eval_forward(strategy)
after_eval_iteration(strategy)
after_forward(strategy)
after_train_dataset_adaptation(strategy)
after_training(strategy)
after_training_epoch(strategy)
after_training_exp(strategy)
after_training_iteration(strategy)
after_update(strategy)
before_backward(strategy)
before_eval(strategy)
before_eval_dataset_adaptation(strategy)
before_eval_exp(strategy)
before_eval_forward(strategy)
before_eval_iteration(strategy)
before_forward(strategy)
before_train_dataset_adaptation(strategy)
before_training(strategy)
before_training_epoch(strategy)
before_training_exp(strategy)
before_training_iteration(strategy)
before_update(strategy)
reset()	Resets the metric internal state.
result()	Obtains the value of the metric.
update(strategy)