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| import numpy as np | |
| class Meter(object): | |
| """Meters provide a way to keep track of important statistics in an online manner. | |
| This class is abstract, but provides a standard interface for all meters to follow. | |
| """ | |
| def reset(self): | |
| """Reset the meter to default settings.""" | |
| pass | |
| def add(self, value): | |
| """Log a new value to the meter | |
| Args: | |
| value: Next result to include. | |
| """ | |
| pass | |
| def value(self): | |
| """Get the value of the meter in the current state.""" | |
| pass | |
| class AverageValueMeter(Meter): | |
| def __init__(self): | |
| super(AverageValueMeter, self).__init__() | |
| self.reset() | |
| self.val = 0 | |
| def add(self, value, n=1): | |
| self.val = value | |
| self.sum += value | |
| self.var += value * value | |
| self.n += n | |
| if self.n == 0: | |
| self.mean, self.std = np.nan, np.nan | |
| elif self.n == 1: | |
| self.mean = 0.0 + self.sum # This is to force a copy in torch/numpy | |
| self.std = np.inf | |
| self.mean_old = self.mean | |
| self.m_s = 0.0 | |
| else: | |
| self.mean = self.mean_old + (value - n * self.mean_old) / float(self.n) | |
| self.m_s += (value - self.mean_old) * (value - self.mean) | |
| self.mean_old = self.mean | |
| self.std = np.sqrt(self.m_s / (self.n - 1.0)) | |
| def value(self): | |
| return self.mean, self.std | |
| def reset(self): | |
| self.n = 0 | |
| self.sum = 0.0 | |
| self.var = 0.0 | |
| self.val = 0.0 | |
| self.mean = np.nan | |
| self.mean_old = 0.0 | |
| self.m_s = 0.0 | |
| self.std = np.nan | |