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import collections |
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import numpy as np |
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import datetime |
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__all__ = ['TrainingStats', 'Time'] |
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class SmoothedValue(object): |
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"""Track a series of values and provide access to smoothed values over a |
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window or the global series average. |
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""" |
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def __init__(self, window_size): |
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self.deque = collections.deque(maxlen=window_size) |
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def add_value(self, value): |
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self.deque.append(value) |
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def get_median_value(self): |
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return np.median(self.deque) |
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def Time(): |
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return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f') |
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class TrainingStats(object): |
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def __init__(self, window_size, stats_keys): |
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self.window_size = window_size |
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self.smoothed_losses_and_metrics = { |
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key: SmoothedValue(window_size) |
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for key in stats_keys |
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} |
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def update(self, stats): |
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for k, v in stats.items(): |
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if k not in self.smoothed_losses_and_metrics: |
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self.smoothed_losses_and_metrics[k] = SmoothedValue( |
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self.window_size) |
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self.smoothed_losses_and_metrics[k].add_value(v) |
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def get(self, extras=None): |
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stats = collections.OrderedDict() |
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if extras: |
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for k, v in extras.items(): |
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stats[k] = v |
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for k, v in self.smoothed_losses_and_metrics.items(): |
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stats[k] = round(v.get_median_value(), 6) |
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return stats |
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def log(self, extras=None): |
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d = self.get(extras) |
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strs = [] |
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for k, v in d.items(): |
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strs.append('{}: {:x<6f}'.format(k, v)) |
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strs = ', '.join(strs) |
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return strs |
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