| import numpy as np | |
| def get_mean_std_max_min_dict(array, prefix): | |
| res = {} | |
| res[prefix + "/mean"] = np.mean(array) | |
| res[prefix + "/std"] = np.std(array) | |
| res[prefix + "/min"] = np.amin(array) | |
| res[prefix + "/max"] = np.amax(array) | |
| return res | |
| class Metrics: | |
| """Object keeping running average/latest of relevant metrics to log.""" | |
| def __init__(self, *args): | |
| self.metrics = {arg: 0 for arg in args} | |
| self.latest_metrics = {arg: 0 for arg in args} | |
| self.samples = {arg: 1e-8 for arg in args} | |
| self.logged_metrics = [arg for arg in args] | |
| def reset(self): | |
| for arg in self.metrics: | |
| self.metrics[arg] = 0 | |
| self.samples[arg] = 1e-8 | |
| def add(self, *args): | |
| for arg in args: | |
| if arg not in self.metrics: | |
| self.logged_metrics.append(arg) | |
| self.metrics[arg] = 0 | |
| self.latest_metrics[arg] = 0 | |
| self.samples[arg] = 1e-8 | |
| def update(self, **kwargs): | |
| for arg, val in kwargs.items(): | |
| if arg not in self.metrics: | |
| self.logged_metrics += arg | |
| self.metrics[arg] = 0 | |
| self.latest_metrics[arg] = 0 | |
| self.samples[arg] = 1e-8 | |
| self.metrics[arg] += val | |
| self.samples[arg] += 1 | |
| def set(self, **kwargs): | |
| for arg, val in kwargs.items(): | |
| if arg not in self.metrics: | |
| self.logged_metrics += arg | |
| self.metrics[arg] = val | |
| self.samples[arg] = 1 | |
| self.metrics[arg] = val | |
| self.samples[arg] = 1 | |
| def get(self): | |
| for arg, metric_agg in self.metrics.items(): | |
| samples = self.samples[arg] | |
| if samples >= 1: | |
| self.latest_metrics[arg] = metric_agg / samples | |
| return self.latest_metrics | |