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# Copyright (c) OpenMMLab. All rights reserved.
import numbers
from abc import ABCMeta, abstractmethod
import numpy as np
import torch
from ..hook import Hook
class LoggerHook(Hook):
"""Base class for logger hooks.
Args:
interval (int): Logging interval (every k iterations).
ignore_last (bool): Ignore the log of last iterations in each epoch
if less than `interval`.
reset_flag (bool): Whether to clear the output buffer after logging.
by_epoch (bool): Whether EpochBasedRunner is used.
"""
__metaclass__ = ABCMeta
def __init__(self,
interval=10,
ignore_last=True,
reset_flag=False,
by_epoch=True):
self.interval = interval
self.ignore_last = ignore_last
self.reset_flag = reset_flag
self.by_epoch = by_epoch
@abstractmethod
def log(self, runner):
pass
@staticmethod
def is_scalar(val, include_np=True, include_torch=True):
"""Tell the input variable is a scalar or not.
Args:
val: Input variable.
include_np (bool): Whether include 0-d np.ndarray as a scalar.
include_torch (bool): Whether include 0-d torch.Tensor as a scalar.
Returns:
bool: True or False.
"""
if isinstance(val, numbers.Number):
return True
elif include_np and isinstance(val, np.ndarray) and val.ndim == 0:
return True
elif include_torch and isinstance(val, torch.Tensor) and len(val) == 1:
return True
else:
return False
def get_mode(self, runner):
if runner.mode == 'train':
if 'time' in runner.log_buffer.output:
mode = 'train'
else:
mode = 'val'
elif runner.mode == 'val':
mode = 'val'
else:
raise ValueError(f"runner mode should be 'train' or 'val', "
f'but got {runner.mode}')
return mode
def get_epoch(self, runner):
if runner.mode == 'train':
epoch = runner.epoch + 1
elif runner.mode == 'val':
# normal val mode
# runner.epoch += 1 has been done before val workflow
epoch = runner.epoch
else:
raise ValueError(f"runner mode should be 'train' or 'val', "
f'but got {runner.mode}')
return epoch
def get_iter(self, runner, inner_iter=False):
"""Get the current training iteration step."""
if self.by_epoch and inner_iter:
current_iter = runner.inner_iter + 1
else:
current_iter = runner.iter + 1
return current_iter
def get_lr_tags(self, runner):
tags = {}
lrs = runner.current_lr()
if isinstance(lrs, dict):
for name, value in lrs.items():
tags[f'learning_rate/{name}'] = value[0]
else:
tags['learning_rate'] = lrs[0]
return tags
def get_momentum_tags(self, runner):
tags = {}
momentums = runner.current_momentum()
if isinstance(momentums, dict):
for name, value in momentums.items():
tags[f'momentum/{name}'] = value[0]
else:
tags['momentum'] = momentums[0]
return tags
def get_loggable_tags(self,
runner,
allow_scalar=True,
allow_text=False,
add_mode=True,
tags_to_skip=('time', 'data_time')):
tags = {}
for var, val in runner.log_buffer.output.items():
if var in tags_to_skip:
continue
if self.is_scalar(val) and not allow_scalar:
continue
if isinstance(val, str) and not allow_text:
continue
if add_mode:
var = f'{self.get_mode(runner)}/{var}'
tags[var] = val
tags.update(self.get_lr_tags(runner))
tags.update(self.get_momentum_tags(runner))
return tags
def before_run(self, runner):
for hook in runner.hooks[::-1]:
if isinstance(hook, LoggerHook):
hook.reset_flag = True
break
def before_epoch(self, runner):
runner.log_buffer.clear() # clear logs of last epoch
def after_train_iter(self, runner):
if self.by_epoch and self.every_n_inner_iters(runner, self.interval):
runner.log_buffer.average(self.interval)
elif not self.by_epoch and self.every_n_iters(runner, self.interval):
runner.log_buffer.average(self.interval)
elif self.end_of_epoch(runner) and not self.ignore_last:
# not precise but more stable
runner.log_buffer.average(self.interval)
if runner.log_buffer.ready:
self.log(runner)
if self.reset_flag:
runner.log_buffer.clear_output()
def after_train_epoch(self, runner):
if runner.log_buffer.ready:
self.log(runner)
if self.reset_flag:
runner.log_buffer.clear_output()
def after_val_epoch(self, runner):
runner.log_buffer.average()
self.log(runner)
if self.reset_flag:
runner.log_buffer.clear_output()
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