81 lines
2.1 KiB
Python
81 lines
2.1 KiB
Python
from collections import defaultdict
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import torch
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__all__ = ["AverageMeter", "MetricMeter"]
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class AverageMeter:
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"""Compute and store the average and current value.
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Examples::
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>>> # 1. Initialize a meter to record loss
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>>> losses = AverageMeter()
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>>> # 2. Update meter after every mini-batch update
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>>> losses.update(loss_value, batch_size)
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"""
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def __init__(self, ema=False):
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"""
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Args:
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ema (bool, optional): apply exponential moving average.
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"""
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self.ema = ema
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self.reset()
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def reset(self):
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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if isinstance(val, torch.Tensor):
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val = val.item()
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self.val = val
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self.sum += val * n
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self.count += n
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if self.ema:
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self.avg = self.avg * 0.9 + self.val * 0.1
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else:
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self.avg = self.sum / self.count
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class MetricMeter:
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"""Store the average and current value for a set of metrics.
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Examples::
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>>> # 1. Create an instance of MetricMeter
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>>> metric = MetricMeter()
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>>> # 2. Update using a dictionary as input
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>>> input_dict = {'loss_1': value_1, 'loss_2': value_2}
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>>> metric.update(input_dict)
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>>> # 3. Convert to string and print
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>>> print(str(metric))
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"""
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def __init__(self, delimiter="\t"):
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self.meters = defaultdict(AverageMeter)
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self.delimiter = delimiter
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def update(self, input_dict):
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if input_dict is None:
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return
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if not isinstance(input_dict, dict):
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raise TypeError(
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"Input to MetricMeter.update() must be a dictionary"
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)
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for k, v in input_dict.items():
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if isinstance(v, torch.Tensor):
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v = v.item()
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self.meters[k].update(v)
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def __str__(self):
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output_str = []
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for name, meter in self.meters.items():
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output_str.append(f"{name} {meter.val:.4f} ({meter.avg:.4f})")
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return self.delimiter.join(output_str)
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