Method std

modules/eg3ds/torch_utils/training_stats.py:200–212

r"""Returns the standard deviation of the scalars that were accumulated for the given statistic between the last two calls to `update()`, or NaN if no scalars were collected.

(self, name)

        return float(delta[1] / delta[0])

    def std(self, name):
        r"""Returns the standard deviation of the scalars that were
        accumulated for the given statistic between the last two calls to
        `update()`, or NaN if no scalars were collected.
        """
        delta = self._get_delta(name)
        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
            return float('nan')
        if int(delta[0]) == 1:
            return float(0)
        mean = float(delta[1] / delta[0])
        raw_var = float(delta[2] / delta[0])
        return np.sqrt(max(raw_var - np.square(mean), 0))

    def as_dict(self):
        r"""Returns the averages accumulated between the last two calls to