DeepLearningExamples/MxNet/Classification/RN50v1.5/global_metrics.py
2021-04-07 17:46:50 +02:00

100 lines
2.1 KiB
Python

import numpy as np
class CompositeMeter:
def __init__(self):
self.register = {}
def register_metric(self, name, metric):
self.register[name] = metric
def _validate(self, metric_name):
if metric_name not in self.register:
raise ValueError('{} is not registered metric'.format(metric_name))
def update_metric(self, metric_name, value):
self._validate(metric_name)
self.register[metric_name].update(value)
def update_dict(self, dict_metric):
for name, val in dict_metric.items():
if name in self.register.keys():
self.update_metric(name, val)
def get(self, metric_name=None):
if metric_name is not None:
self._validate(metric_name)
return self.register[metric_name].get()
res_dict = {name: metric.get() for name, metric in self.register.items()}
return res_dict
class MaxMeter:
def __init__(self):
self.max = None
self.n = 0
def reset(self):
self.max = None
self.n = 0
def update(self, val):
if self.max is None:
self.max = val
else:
self.max = max(self.max, val)
def get(self):
return self.max
class MinMeter:
def __init__(self):
self.min = None
self.n = 0
def reset(self):
self.min = None
self.n = 0
def update(self, val):
if self.min is None:
self.min = val
else:
self.min = min(self.min, val)
def get(self):
return self.min
class AvgMeter:
def __init__(self):
self.sum = 0
self.n = 0
def reset(self):
self.sum = 0
self.n = 0
def update(self, val):
self.sum += val
self.n += 1
def get(self):
return self.sum / self.n
class PercentileMeter:
def __init__(self, q):
self.data = []
self.q = q
def reset(self):
self.data = []
def update(self, data):
self.data.extend(data)
def get(self):
return np.percentile(self.data, self.q)