HistoryBuffer — mmengine 0.10.7 documentation (original) (raw)
class mmengine.logging.HistoryBuffer(log_history=[], count_history=[], max_length=1000000)[source]¶
Unified storage format for different log types.
HistoryBuffer records the history of log for further statistics.
Examples
history_buffer = HistoryBuffer()
Update history_buffer.
history_buffer.update(1) history_buffer.update(2) history_buffer.min() # minimum of (1, 2) 1 history_buffer.max() # maximum of (1, 2) 2 history_buffer.mean() # mean of (1, 2) 1.5 history_buffer.statistics('mean') # access method by string. 1.5
Parameters:
- log_history (Sequence) – History logs. Defaults to [].
- count_history (Sequence) – Counts of history logs. Defaults to [].
- max_length (int) – The max length of history logs. Defaults to 1000000.
Return the recently updated values in log histories.
Returns:
Recently updated values in log histories.
Return type:
np.ndarray
property data_: Tuple[ndarray, ndarray]_¶
Get the _log_history and _count_history.
Returns:
History logs and the counts of the history logs.
Return type:
Tuple[np.ndarray, np.ndarray]
max(window_size=None)[source]¶
Return the maximum value of the latest window_size values in log histories.
If window_size is None or window_size > len(self._log_history), return the global maximum value of history logs.
Parameters:
window_size (int, optional) – Size of statistics window.
Returns:
The maximum value within the window.
Return type:
np.ndarray
mean(window_size=None)[source]¶
Return the mean of the latest window_size values in log histories.
If window_size is None or window_size > len(self._log_history), return the global mean value of history logs.
Parameters:
window_size (int, optional) – Size of statistics window.
Returns:
Mean value within the window.
Return type:
np.ndarray
min(window_size=None)[source]¶
Return the minimum value of the latest window_size values in log histories.
If window_size is None or window_size > len(self._log_history), return the global minimum value of history logs.
Parameters:
window_size (int, optional) – Size of statistics window.
Returns:
The minimum value within the window.
Return type:
np.ndarray
classmethod register_statistics(method)[source]¶
Register custom statistics method to _statistics_methods.
The registered method can be called by history_buffer.statisticswith corresponding method name and arguments.
Examples
@HistoryBuffer.register_statistics def weighted_mean(self, window_size, weight): assert len(weight) == window_size return (self._log_history[-window_size:] * np.array(weight)).sum() / >>> self._count_history[-window_size:]
log_buffer = HistoryBuffer([1, 2], [1, 1]) log_buffer.statistics('weighted_mean', 2, [2, 1]) 2
Parameters:
method (Callable) – Custom statistics method.
Returns:
Original custom statistics method.
Return type:
Callable
statistics(method_name, *arg, **kwargs)[source]¶
Access statistics method by name.
Parameters:
method_name (str) – Name of method.
Returns:
Depends on corresponding method.
Return type:
Any
update(log_val, count=1)[source]¶
Update the log history.
If the length of the buffer exceeds self._max_length, the oldest element will be removed from the buffer.
Parameters:
- log_val (int or float) – The value of log.
- count (int) – The accumulation times of log, defaults to 1.
- statistics. (count will be used in smooth) –
Return type:
None