from __future__ import division, absolute_import
from collections import defaultdict
import torch
__all__ = ['AverageMeter', 'MetricMeter']
[docs]class AverageMeter(object):
"""Computes and stores the average and current value.
Examples::
>>> # Initialize a meter to record loss
>>> losses = AverageMeter()
>>> # Update meter after every minibatch update
>>> losses.update(loss_value, batch_size)
"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
[docs]class MetricMeter(object):
"""A collection of metrics.
Source: https://github.com/KaiyangZhou/Dassl.pytorch
Examples::
>>> # 1. Create an instance of MetricMeter
>>> metric = MetricMeter()
>>> # 2. Update using a dictionary as input
>>> input_dict = {'loss_1': value_1, 'loss_2': value_2}
>>> metric.update(input_dict)
>>> # 3. Convert to string and print
>>> print(str(metric))
"""
def __init__(self, delimiter='\t'):
self.meters = defaultdict(AverageMeter)
self.delimiter = delimiter
def update(self, input_dict):
if input_dict is None:
return
if not isinstance(input_dict, dict):
raise TypeError(
'Input to MetricMeter.update() must be a dictionary'
)
for k, v in input_dict.items():
if isinstance(v, torch.Tensor):
v = v.item()
self.meters[k].update(v)
def __str__(self):
output_str = []
for name, meter in self.meters.items():
output_str.append(
'{} {:.4f} ({:.4f})'.format(name, meter.val, meter.avg)
)
return self.delimiter.join(output_str)