Spaces:
Running
Running
File size: 1,621 Bytes
29f689c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
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.window_size = window_size
self.smoothed_losses_and_metrics = {
key: SmoothedValue(window_size)
for key in stats_keys
}
def update(self, stats):
for k, v in stats.items():
if k not in self.smoothed_losses_and_metrics:
self.smoothed_losses_and_metrics[k] = SmoothedValue(
self.window_size)
self.smoothed_losses_and_metrics[k].add_value(v)
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 = []
for k, v in d.items():
strs.append("{}: {:x<6f}".format(k, v))
strs = ", ".join(strs)
return strs
|