61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
import torch
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import numpy as np
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from typing import Union
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class MovAvg(object):
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"""Class for moving average. It will automatically exclude the infinity and
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NaN. Usage:
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::
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>>> stat = MovAvg(size=66)
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>>> stat.add(torch.tensor(5))
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5.0
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>>> stat.add(float('inf')) # which will not add to stat
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5.0
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>>> stat.add([6, 7, 8])
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6.5
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>>> stat.get()
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6.5
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>>> print(f'{stat.mean():.2f}±{stat.std():.2f}')
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6.50±1.12
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"""
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def __init__(self, size: int = 100) -> None:
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super().__init__()
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self.size = size
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self.cache = []
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self.banned = [np.inf, np.nan, -np.inf]
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def add(self, x: Union[float, list, np.ndarray, torch.Tensor]) -> float:
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"""Add a scalar into :class:`MovAvg`. You can add ``torch.Tensor`` with
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only one element, a python scalar, or a list of python scalar.
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"""
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if isinstance(x, torch.Tensor):
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x = x.item()
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if isinstance(x, list) or isinstance(x, np.ndarray):
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for _ in x:
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if _ not in self.banned:
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self.cache.append(_)
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elif x not in self.banned:
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self.cache.append(x)
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if self.size > 0 and len(self.cache) > self.size:
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self.cache = self.cache[-self.size:]
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return self.get()
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def get(self) -> float:
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"""Get the average."""
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if len(self.cache) == 0:
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return 0
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return np.mean(self.cache)
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def mean(self) -> float:
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"""Get the average. Same as :meth:`get`."""
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return self.get()
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def std(self) -> float:
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"""Get the standard deviation."""
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if len(self.cache) == 0:
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return 0
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return np.std(self.cache)
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