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Source code for ignite.metrics.running_average

from typing import Callable, Optional, Sequence, Union

import torch

import ignite.distributed as idist
from ignite.engine import Engine, Events
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce

__all__ = ["RunningAverage"]


[docs]class RunningAverage(Metric): """Compute running average of a metric or the output of process function. Args: src (Metric or None): input source: an instance of :class:`~ignite.metrics.Metric` or None. The latter corresponds to `engine.state.output` which holds the output of process function. alpha (float, optional): running average decay factor, default 0.98 output_transform (callable, optional): a function to use to transform the output if `src` is None and corresponds the output of process function. Otherwise it should be None. epoch_bound (boolean, optional): whether the running average should be reset after each epoch (defaults to True). device (str of torch.device, optional): unused argument. Examples: .. code-block:: python alpha = 0.98 acc_metric = RunningAverage(Accuracy(output_transform=lambda x: [x[1], x[2]]), alpha=alpha) acc_metric.attach(trainer, 'running_avg_accuracy') avg_output = RunningAverage(output_transform=lambda x: x[0], alpha=alpha) avg_output.attach(trainer, 'running_avg_loss') @trainer.on(Events.ITERATION_COMPLETED) def log_running_avg_metrics(engine): print("running avg accuracy:", engine.state.metrics['running_avg_accuracy']) print("running avg loss:", engine.state.metrics['running_avg_loss']) """ _required_output_keys = None def __init__( self, src: Optional[Metric] = None, alpha: float = 0.98, output_transform: Optional[Callable] = None, epoch_bound: bool = True, device: Optional[Union[str, torch.device]] = None, ): if not (isinstance(src, Metric) or src is None): raise TypeError("Argument src should be a Metric or None.") if not (0.0 < alpha <= 1.0): raise ValueError("Argument alpha should be a float between 0.0 and 1.0.") if isinstance(src, Metric): if output_transform is not None: raise ValueError("Argument output_transform should be None if src is a Metric.") if device is not None: raise ValueError("Argument device should be None if src is a Metric.") self.src = src self._get_src_value = self._get_metric_value self.iteration_completed = self._metric_iteration_completed else: if output_transform is None: raise ValueError( "Argument output_transform should not be None if src corresponds " "to the output of process function." ) self._get_src_value = self._get_output_value self.update = self._output_update self.alpha = alpha self.epoch_bound = epoch_bound super(RunningAverage, self).__init__(output_transform=output_transform, device=device) @reinit__is_reduced def reset(self) -> None: self._value = None @reinit__is_reduced def update(self, output: Sequence) -> None: # Implement abstract method pass def compute(self) -> Union[torch.Tensor, float]: if self._value is None: self._value = self._get_src_value() else: self._value = self._value * self.alpha + (1.0 - self.alpha) * self._get_src_value() return self._value def attach(self, engine: Engine, name: str): if self.epoch_bound: # restart average every epoch engine.add_event_handler(Events.EPOCH_STARTED, self.started) # compute metric engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed) # apply running average engine.add_event_handler(Events.ITERATION_COMPLETED, self.completed, name) def _get_metric_value(self) -> Union[torch.Tensor, float]: return self.src.compute() @sync_all_reduce("src") def _get_output_value(self) -> Union[torch.Tensor, float]: # we need to compute average instead of sum produced by @sync_all_reduce("src") output = self.src / idist.get_world_size() return output def _metric_iteration_completed(self, engine: Engine) -> None: self.src.started(engine) self.src.iteration_completed(engine) @reinit__is_reduced def _output_update(self, output: Union[torch.Tensor, float]) -> None: if isinstance(output, torch.Tensor): output = output.detach().clone() self.src = output

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