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# Copyright (c) 2022 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 datetime
import numpy as np
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window.
"""
def __init__(self, window_size=20):
if window_size is None:
self.deque = list()
else:
self.deque = collections.deque(maxlen=window_size)
def update(self, value):
self.deque.append(value)
def avg(self):
return np.mean(self.deque)
class TrainingStats(object):
def __init__(self, window_size=None, delimiter=', '):
self.meters = None
self.window_size = window_size
self.delimiter = delimiter
def update(self, stats):
if self.meters is None:
self.meters = {
k: SmoothedValue(self.window_size)
for k in stats.keys()
}
for k, v in self.meters.items():
v.update(stats[k].numpy())
def get(self, extras=None):
stats = collections.OrderedDict()
if extras:
for k, v in extras.items():
stats[k] = v
for k, v in self.meters.items():
stats[k] = v.avg()
return stats
def log(self, extras=None):
d = self.get(extras)
strs = []
for k, v in d.items():
strs.append("{}={}".format(k, str(v).format('8.6f')))
return self.delimiter.join(strs)