history_buffer.py

# coding=utf-8
# Copyright 2021 The OneFlow Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import List, Tuple

import numpy as np

# --------------------------------------------------------
# References:
# https://github.com/facebookresearch/fvcore/blob/main/fvcore/common/history_buffer.py
# --------------------------------------------------------


class HistoryBuffer:
    """
    Track a series of scalar values and provide access to smoothed values over a
    window or the global average of the series.
    """

    def __init__(self, max_length: int = 1000000):
        """
        Args:
            max_length: maximal number of values that can be stored in the
                buffer. When the capacity of the buffer is exhausted, old
                values will be removed.
        """
        self._max_length: int = max_length
        self._data: List[Tuple[float, float]] = []  # (value, iteration) pairs
        self._count: int = 0
        self._global_avg: float = 0

    def update(self, value: float, iteration: float = None):
        """
        Add a new scalar value produced at certain iteration. If the length
        of the buffer exceeds self._max_length, the oldest element will be
        removed from the buffer.
        """
        if iteration is None:
            iteration = self._count
        if len(self._data) == self._max_length:
            self._data.pop(0)
        self._data.append((value, iteration))

        self._count += 1
        self._global_avg += (value - self._global_avg) / self._count

    def latest(self):
        """
        Return the latest scalar value added to the buffer.
        """
        return self._data[-1][0]

    def median(self, window_size: int):
        """
        Return the median of the latest `window_size` values in the buffer.
        """
        return np.median([x[0] for x in self._data[-window_size:]])

    def avg(self, window_size: int):
        """
        Return the mean of the latest `window_size` values in the buffer.
        """
        return np.mean([x[0] for x in self._data[-window_size:]])

    def global_avg(self):
        """
        Return the mean of all the elements in the buffer. Note that this
        includes those getting removed due to limited buffer storage.
        """
        return self._global_avg

    def values(self):
        """
        Returns:
            list[(number, iteration)]: content of the current buffer.
        """
        return self._data