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94 lines
2.6 KiB
94 lines
2.6 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import collections |
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import numpy as np |
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__all__ = ['SmoothedValue', 'TrainingStats'] |
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class SmoothedValue(object): |
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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=20, fmt=None): |
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if fmt is None: |
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fmt = "{median:.4f} ({avg:.4f})" |
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self.deque = collections.deque(maxlen=window_size) |
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self.fmt = fmt |
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self.total = 0. |
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self.count = 0 |
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def update(self, value, n=1): |
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self.deque.append(value) |
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self.count += n |
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self.total += value * n |
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@property |
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def median(self): |
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return np.median(self.deque) |
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@property |
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def avg(self): |
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return np.mean(self.deque) |
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@property |
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def max(self): |
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return np.max(self.deque) |
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@property |
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def value(self): |
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return self.deque[-1] |
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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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def __str__(self): |
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return self.fmt.format( |
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median=self.median, avg=self.avg, max=self.max, value=self.value) |
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class TrainingStats(object): |
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def __init__(self, window_size, delimiter=' '): |
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self.meters = None |
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self.window_size = window_size |
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self.delimiter = delimiter |
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def update(self, stats): |
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if self.meters is None: |
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self.meters = { |
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k: SmoothedValue(self.window_size) |
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for k in stats.keys() |
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} |
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for k, v in self.meters.items(): |
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v.update(stats[k].numpy()) |
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def get(self, extras=None): |
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stats = collections.OrderedDict() |
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if extras: |
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for k, v in extras.items(): |
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stats[k] = v |
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for k, v in self.meters.items(): |
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stats[k] = format(v.median, '.6f') |
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return stats |
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def log(self, extras=None): |
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d = self.get(extras) |
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strs = [] |
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for k, v in d.items(): |
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strs.append("{}: {}".format(k, str(v))) |
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return self.delimiter.join(strs)
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