stats.py 1.6 KB

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  1. import collections
  2. import numpy as np
  3. import datetime
  4. __all__ = ["TrainingStats", "Time"]
  5. class SmoothedValue(object):
  6. """Track a series of values and provide access to smoothed values over a
  7. window or the global series average.
  8. """
  9. def __init__(self, window_size):
  10. self.deque = collections.deque(maxlen=window_size)
  11. def add_value(self, value):
  12. self.deque.append(value)
  13. def get_median_value(self):
  14. return np.median(self.deque)
  15. def Time():
  16. return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")
  17. class TrainingStats(object):
  18. def __init__(self, window_size, stats_keys):
  19. self.window_size = window_size
  20. self.smoothed_losses_and_metrics = {
  21. key: SmoothedValue(window_size)
  22. for key in stats_keys
  23. }
  24. def update(self, stats):
  25. for k, v in stats.items():
  26. if k not in self.smoothed_losses_and_metrics:
  27. self.smoothed_losses_and_metrics[k] = SmoothedValue(
  28. self.window_size)
  29. self.smoothed_losses_and_metrics[k].add_value(v)
  30. def get(self, extras=None):
  31. stats = collections.OrderedDict()
  32. if extras:
  33. for k, v in extras.items():
  34. stats[k] = v
  35. for k, v in self.smoothed_losses_and_metrics.items():
  36. stats[k] = round(v.get_median_value(), 6)
  37. return stats
  38. def log(self, extras=None):
  39. d = self.get(extras)
  40. strs = []
  41. for k, v in d.items():
  42. strs.append("{}: {:x<6f}".format(k, v))
  43. strs = ", ".join(strs)
  44. return strs