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- # Copyright (c) 2019 PaddlePaddle 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.
- import collections
- import numpy as np
- import datetime
- __all__ = ['TrainingStats', 'Time']
- class SmoothedValue(object):
- """Track a series of values and provide access to smoothed values over a
- window or the global series average.
- """
- def __init__(self, window_size):
- self.deque = collections.deque(maxlen=window_size)
- def add_value(self, value):
- self.deque.append(value)
- def get_median_value(self):
- return np.median(self.deque)
- def Time():
- return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
- class TrainingStats(object):
- def __init__(self, window_size, stats_keys):
- self.smoothed_losses_and_metrics = {
- key: SmoothedValue(window_size)
- for key in stats_keys
- }
- def update(self, stats):
- for k, v in self.smoothed_losses_and_metrics.items():
- v.add_value(stats[k])
- def get(self, extras=None):
- stats = collections.OrderedDict()
- if extras:
- for k, v in extras.items():
- stats[k] = v
- for k, v in self.smoothed_losses_and_metrics.items():
- stats[k] = format(v.get_median_value(), '.6f')
- return stats
- def log(self, extras=None):
- d = self.get(extras)
- strs = ', '.join(str(dict({x: y})).strip('{}') for x, y in d.items())
- return strs
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