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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
from ...dist_utils import master_only
from ..hook import HOOKS
from .base import LoggerHook
@HOOKS.register_module()
class TensorboardLoggerHook(LoggerHook):
def __init__(self,
log_dir=None,
interval=10,
ignore_last=True,
reset_flag=False,
by_epoch=True):
super(TensorboardLoggerHook, self).__init__(interval, ignore_last,
reset_flag, by_epoch)
self.log_dir = log_dir
@master_only
def before_run(self, runner):
super(TensorboardLoggerHook, self).before_run(runner)
if (TORCH_VERSION == 'parrots'
or digit_version(TORCH_VERSION) < digit_version('1.1')):
try:
from tensorboardX import SummaryWriter
except ImportError:
raise ImportError('Please install tensorboardX to use '
'TensorboardLoggerHook.')
else:
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
raise ImportError(
'Please run "pip install future tensorboard" to install '
'the dependencies to use torch.utils.tensorboard '
'(applicable to PyTorch 1.1 or higher)')
if self.log_dir is None:
self.log_dir = osp.join(runner.work_dir, 'tf_logs')
self.writer = SummaryWriter(self.log_dir)
@master_only
def log(self, runner):
tags = self.get_loggable_tags(runner, allow_text=True)
for tag, val in tags.items():
if isinstance(val, str):
self.writer.add_text(tag, val, self.get_iter(runner))
else:
self.writer.add_scalar(tag, val, self.get_iter(runner))
@master_only
def after_run(self, runner):
self.writer.close()
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