def __init__(self,
logdir: str,
fps=1,
window=10,
exp_start_time: float = None):
"""
:param logdir: path to some directory, where all file storing methods write their files to,
placeholder {t} is automatically replaced by the start time of the training
:param fps: the rate in which the logger prints data on the console (frames per second)
:param window: the memory depth of log data, i.e. the logger only
maintains the top-k most recent batches in the .log method. At the end of an epoch the mean of
those top-k batches is additionally saved.
:param exp_start_time: start time of the overall training, just used for
replacing {t} in logdir with it, defaults to the start time of this logger
"""
self.start = int(time.time())
self.exp_start_time = self.start if exp_start_time is None else exp_start_time
self.dir = logdir.replace('{t}', time_format(self.exp_start_time))
if not pt.exists(os.path.dirname(self.dir)):
os.makedirs(os.path.dirname(self.dir))
self.t = time.time()
self.fps = fps
self.history = defaultdict(DefaultList)
self.history['err_all'] = CircleList(window)
self.__window = window
self.__lastepoch = 0
self.__further_keys = []
self.config_outfile = None
self.logtxtfile = None
self.loggingtxt = ''
self.printlog = ''
self.__warnings = []
def log_info(self, info: dict, epoch: int = None):
"""
Logs a dictionary of metrics (unique name -> scalar value) {str -> Tensor} in CircleLists.
Does not compute an average at the end of an epoch. This is done in the .log method.
:param info: dictionary of metrics that are to be maintained like the loss.
:param epoch: current epoch
"""
for k, v in info.items():
if k not in self.__further_keys:
if '{}_all'.format(k) in self.history:
raise ValueError(
'{} is already part of the history.'.format(k))
self.history['{}_all'.format(k)] = CircleList(self.__window)
self.__further_keys.append(k)
self.history['{}_all'.format(k)].append(v.data.item())
if epoch is not None:
self.history[k][epoch] = np.mean(
self.history['{}_all'.format(k)])
def reset(self, logdir: str = None, exp_start_time: float = None):
"""
Resets all stored information. Also sets the start time
:param logdir: sets a new logdir, defaults to None, which means keeping the old one
:param exp_start_time: start time of the overall training, just used for
replacing {t} in logdir with it (if logdir is not None), defaults to the old start time
"""
self.start = int(time.time())
self.exp_start_time = self.exp_start_time if exp_start_time is None else exp_start_time
self.t = time.time()
self.history = defaultdict(DefaultList)
self.history['err_all'] = CircleList(self.__window)
self.history['err'] = DefaultList()
self.__lastepoch = 0
self.__further_keys = []
self.logtxtfile = None
self.loggingtxt = ''
self.log_prints()
self.__warnings = []
if logdir is not None:
self.dir = logdir.replace('{t}', time_format(self.exp_start_time))
if not pt.exists(os.path.dirname(self.dir)):
os.makedirs(os.path.dirname(self.dir))