Source code for avalanche.logging.base_logger

from abc import ABC

from typing import TYPE_CHECKING, List

    from avalanche.evaluation.metric_results import MetricValue
    from import SupervisedTemplate

[docs]class BaseLogger(ABC): """Base class for loggers. Strategy loggers receive MetricValues from the Evaluation plugin and decide when and how to log them. MetricValues are processed by default using `log_metric` and `log_single_metric`. Additionally, loggers may implement any callback's handlers supported by the plugin's system of the template in use, which will be called automatically during the template's execution. This allows to control when the logging happen and how. For example, interactive loggers typically prints at the end of an epoch/experience/stream. Each child class should implement the `log_single_metric` method, which logs a single MetricValue. """
[docs] def __init__(self): super().__init__()
def log_single_metric(self, name, value, x_plot): """Log a metric value. This method is called whenever new metrics are available. By default, all the values are ignored. :param name: str, metric name :param value: the metric value, will be ignored if not supported by the logger :param x_plot: an integer representing the x value associated to the metric value """ pass def log_metrics(self, metric_values: List["MetricValue"]) -> None: """Receive a list of MetricValues to log. This method is called whenever new metrics are available. :param metric_values: list of MetricValues to log. :param callback: The name of the callback (event) during which the metric value was collected. :return: None """ for mval in metric_values: name = value = mval.value x_plot = mval.x_plot if isinstance(value, dict): for k, v in value.items(): n = f"{name}/{k}" self.log_single_metric(n, v, x_plot) else: self.log_single_metric(name, value, x_plot)
__all__ = ["BaseLogger"]