66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
|
# Copyright (c) 2020 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] = round(v.get_median_value(), 6)
|
||
|
|
|
||
|
|
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
|