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91 lines
2.4 KiB
91 lines
2.4 KiB
"""
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helper for logging
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NOTE: loggers are global objects use with caution
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"""
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import logging
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import math
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import tensorboardX
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_LOG_FMT = '%(asctime)s - %(levelname)s - %(name)s - %(message)s'
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_DATE_FMT = '%m/%d/%Y %H:%M:%S'
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logging.basicConfig(format=_LOG_FMT, datefmt=_DATE_FMT, level=logging.INFO)
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LOGGER = logging.getLogger('__main__') # this is the global logger
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def add_log_to_file(log_path):
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fh = logging.FileHandler(log_path)
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formatter = logging.Formatter(_LOG_FMT, datefmt=_DATE_FMT)
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fh.setFormatter(formatter)
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LOGGER.addHandler(fh)
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class TensorboardLogger(object):
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def __init__(self):
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self._logger = None
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self._global_step = 0
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def create(self, path):
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self._logger = tensorboardX.SummaryWriter(path)
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def noop(self, *args, **kwargs):
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return
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def step(self):
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self._global_step += 1
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@property
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def global_step(self):
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return self._global_step
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def log_scaler_dict(self, log_dict, prefix=''):
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""" log a dictionary of scalar values"""
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if self._logger is None:
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return
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if prefix:
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prefix = f'{prefix}_'
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for name, value in log_dict.items():
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if isinstance(value, dict):
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self.log_scaler_dict(value, self._global_step,
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prefix=f'{prefix}{name}')
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else:
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self._logger.add_scalar(f'{prefix}{name}', value,
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self._global_step)
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def __getattr__(self, name):
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if self._logger is None:
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return self.noop
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return self._logger.__getattribute__(name)
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TB_LOGGER = TensorboardLogger()
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class RunningMeter(object):
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""" running meteor of a scalar value
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(useful for monitoring training loss)
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"""
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def __init__(self, name, val=None, smooth=0.99):
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self._name = name
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self._sm = smooth
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self._val = val
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def __call__(self, value):
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val = (value if self._val is None
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else value*(1-self._sm) + self._val*self._sm)
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if not math.isnan(val) and not math.isinf(val):
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self._val = val
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else:
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print(f'Inf/Nan in {self._name}')
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def __str__(self):
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return f'{self._name}: {self._val:.4f}'
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@property
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def val(self):
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return self._val
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@property
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def name(self):
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return self._name
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