Set alpha value to 0 for RunningAverage

❓ Questions/Help/Support

When I look at the source code of RunningAverage class, I see that there is a factor variable, alpha, which is set to 0.98 as default. As far as I know, this is to smooth the value of a metric like this:

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

The use case is that I want to log my metrics during validation time (using contrib.metrics.ProgressBar), these metrics should not be smoothed, however. One more case is that the smoothed value when plotting to Tensorboard is smoothed once again, which leads to quite unreliable the plot. By disabling the smooth factor, I set alpha to 0 and it raised an exception:

if not (0.0 < alpha <= 1.0):
    raise ValueError("Argument alpha should be a float between 0.0 and 1.0.")

I wonder why it is not 0.0 <= alpha <= 1.0 ? Is it a mistake or any idea behind it?

[vfdev-5]

@VinhLoiIT we just merged the PR #979 with MetricUsage, it will be available today or tomorrow in nightly releases. If you can give it a try and leave us a feedback, it would be awesome! Thanks !

Meanwhile I close the issue as solved. Please, open another issue if need more support.

[VinhLoiIT]

I think, we should definitely have an interface for that. I also agree that if you set alpha to 0, you don’t have a running average any more.

@justusschock I thought about adding an option to Metric.attach, something like this : #644 or just put an option for batch-wise computation…

@vfdev-5 I think it worth to mention that the new metric (e.g., Running) should have an option to reset the metric very reset_interval (similar to the behavior of running_loss in pytorch tutorial), so it can be used in both training and validating. Therefore, to calculate running metrics while training or validating, we can use Running class to get raw value and RunningAverage to get smoothed metric value. What do you think?