67 lines
1.8 KiB
Python
Executable File
67 lines
1.8 KiB
Python
Executable File
from collections import deque, defaultdict
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import numpy as np
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import torch
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class SmoothedValue:
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"""Track a series of values and provide access to smoothed values over a
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window or the global series average.
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"""
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def __init__(self, window_size=10):
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self.deque = deque(maxlen=window_size)
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self.value = np.nan
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self.series = []
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self.total = 0.0
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self.count = 0
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def update(self, value):
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self.deque.append(value)
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self.series.append(value)
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self.count += 1
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self.total += value
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self.value = value
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@property
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def median(self):
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values = np.array(self.deque)
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return np.median(values)
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@property
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def avg(self):
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values = np.array(self.deque)
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return np.mean(values)
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@property
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def global_avg(self):
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return self.total / self.count
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class MetricLogger:
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def __init__(self, delimiter=", "):
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self.meters = defaultdict(SmoothedValue)
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self.delimiter = delimiter
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def update(self, **kwargs):
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for k, v in kwargs.items():
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if isinstance(v, torch.Tensor):
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v = v.item()
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assert isinstance(v, (float, int))
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self.meters[k].update(v)
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def __getattr__(self, attr):
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if attr in self.meters:
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return self.meters[attr]
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if attr in self.__dict__:
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return self.__dict__[attr]
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raise AttributeError("'{}' object has no attribute '{}'".format(
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type(self).__name__, attr))
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def __str__(self):
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loss_str = []
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for name, meter in self.meters.items():
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loss_str.append(
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"{}: {:.3f} ({:.3f})".format(name, meter.avg, meter.global_avg)
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)
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return self.delimiter.join(loss_str)
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