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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 | |