diff --git a/mmengine/hooks/logger_hook.py b/mmengine/hooks/logger_hook.py
index 65d5d8a4d523f206020733bb25bfad7c1e68e26a..5b5296489ae9da4e76725849a6123de7ceef8ba3 100644
--- a/mmengine/hooks/logger_hook.py
+++ b/mmengine/hooks/logger_hook.py
@@ -20,7 +20,7 @@ class LoggerHook(Hook):
``LoggerHook`` is used to record logs formatted by ``LogProcessor`` during
training/validation/testing phase. It is used to control following
- behaviers:
+ behaviors:
- The frequency of logs update in terminal, local, tensorboad wandb.etc.
- The frequency of show experiment information in terminal.
@@ -41,10 +41,10 @@ class LoggerHook(Hook):
of ``out_dir`` and the last level directory of
``runner.work_dir``. For example, if the input ``our_dir`` is
``./tmp`` and ``runner.work_dir`` is ``./work_dir/cur_exp``,
- then the log will be saved in ``./tmp/cur_exp``. Deafule to None.
- out_suffix (Tuple[str] or str): Those filenames ending with
- ``out_suffix`` will be copied to ``out_dir``. Defaults to
- ('.log.json', '.log', '.py').
+ then the log will be saved in ``./tmp/cur_exp``. Defaults to None.
+ out_suffix (Tuple[str] or str): Those files in ``runner._log_dir``
+ ending with ``out_suffix`` will be copied to ``out_dir``. Defaults
+ to ('json', '.log', '.py').
keep_local (bool): Whether to keep local logs in the local machine
when :attr:`out_dir` is specified. If False, the local log will be
removed. Defaults to True.
@@ -53,7 +53,7 @@ class LoggerHook(Hook):
Defaults to None.
Examples:
- >>> # A simplest LoggerHook config.
+ >>> # The simplest LoggerHook config.
>>> logger_hook_cfg = dict(interval=20)
"""
priority = 'BELOW_NORMAL'
@@ -64,7 +64,8 @@ class LoggerHook(Hook):
ignore_last: bool = True,
interval_exp_name: int = 1000,
out_dir: Optional[Union[str, Path]] = None,
- out_suffix: Union[Sequence[str], str] = ('.log.json', '.log', '.py'),
+ out_suffix: Union[Sequence[str],
+ str] = ('.json', '.log', '.py', 'yaml'),
keep_local: bool = True,
file_client_args: Optional[dict] = None,
):
@@ -86,6 +87,7 @@ class LoggerHook(Hook):
self.keep_local = keep_local
self.file_client_args = file_client_args
+ self.json_log_path: Optional[str] = None
if self.out_dir is not None:
self.file_client = FileClient.infer_client(file_client_args,
self.out_dir)
@@ -106,36 +108,32 @@ class LoggerHook(Hook):
(f'Text logs will be saved to {self.out_dir} by '
f'{self.file_client.name} after the training process.'))
- self.json_log_path = osp.join(runner.work_dir,
- f'{runner.timestamp}.log.json')
- self.yaml_log_path = osp.join(runner.work_dir,
- f'{runner.timestamp}.log.json')
+ self.json_log_path = f'{runner.timestamp}.json'
def after_train_iter(self,
runner,
batch_idx: int,
data_batch: DATA_BATCH = None,
outputs: Optional[dict] = None) -> None:
- """Record training logs after training iteration.
+ """Record logs after training iteration.
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the train loop.
data_batch (Sequence[dict], optional): Data from dataloader.
Defaults to None.
- outputs (dict, optional): Outputs from model.
- Defaults to None.
+ outputs (dict, optional): Outputs from model. Defaults to None.
"""
# Print experiment name every n iterations.
if self.every_n_iters(runner,
self.interval_exp_name) or (self.end_of_epoch(
- runner.train_dataloader, batch_idx)):
+ runner.train_loop.dataloader, batch_idx)):
exp_info = f'Exp name: {runner.experiment_name}'
runner.logger.info(exp_info)
if self.every_n_inner_iters(batch_idx, self.interval):
tag, log_str = runner.log_processor.get_log_after_iter(
runner, batch_idx, 'train')
- elif (self.end_of_epoch(runner.train_dataloader, batch_idx)
+ elif (self.end_of_epoch(runner.train_loop.dataloader, batch_idx)
and not self.ignore_last):
# `runner.max_iters` may not be divisible by `self.interval`. if
# `self.ignore_last==True`, the log of remaining iterations will
@@ -146,8 +144,8 @@ class LoggerHook(Hook):
else:
return
runner.logger.info(log_str)
- # TODO compatible with visualizer.
- runner.visualizer.add_scalars(tag, step=runner.iter + 1)
+ runner.visualizer.add_scalars(
+ tag, step=runner.iter + 1, file_path=self.json_log_path)
def after_val_iter(
self,
@@ -155,17 +153,18 @@ class LoggerHook(Hook):
batch_idx: int,
data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataElement]] = None) -> None:
- """Record validation logs after validation iteration.
+ """Record logs after validation iteration.
Args:
- runner (Runner): The runner of the training process.
- batch_idx (int): The index of the current batch in the train loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ runner (Runner): The runner of the validation process.
+ batch_idx (int): The index of the current batch in the validation
+ loop.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (sequence, optional): Outputs from model. Defaults to None.
"""
if self.every_n_inner_iters(batch_idx, self.interval):
- tag, log_str = runner.log_processor.get_log_after_iter(
+ _, log_str = runner.log_processor.get_log_after_iter(
runner, batch_idx, 'val')
runner.logger.info(log_str)
@@ -175,39 +174,39 @@ class LoggerHook(Hook):
batch_idx: int,
data_batch: DATA_BATCH = None,
outputs: Optional[Sequence[BaseDataElement]] = None) -> None:
- """Record testing logs after iteration.
+ """Record logs after testing iteration.
Args:
- runner (Runner): The runner of the training process.
- batch_idx (int): The index of the current batch in the train loop.
- data_batch (Sequence[Tuple[Any, BaseDataElement]], optional):
- Data from dataloader. Defaults to None.
+ runner (Runner): The runner of the testing process.
+ batch_idx (int): The index of the current batch in the test loop.
+ data_batch (Sequence[dict], optional): Data from dataloader.
+ Defaults to None.
outputs (sequence, optional): Outputs from model. Defaults to None.
"""
if self.every_n_inner_iters(batch_idx, self.interval):
- tag, log_str = runner.log_processor.get_log_after_iter(
+ _, log_str = runner.log_processor.get_log_after_iter(
runner, batch_idx, 'test')
runner.logger.info(log_str)
def after_val_epoch(self, runner) -> None:
- """Record validation logs after validation epoch.
+ """Record logs after validation epoch.
Args:
- runner (Runner): The runner of the training process.
+ runner (Runner): The runner of the validation process.
"""
tag, log_str = runner.log_processor.get_log_after_epoch(
runner, len(runner.val_dataloader), 'val')
runner.logger.info(log_str)
- # TODO compatible with visualizer.
- runner.visualizer.add_scalars(tag, step=runner.iter + 1)
+ runner.visualizer.add_scalars(
+ tag, step=runner.iter, file_path=self.json_log_path)
def after_test_epoch(self, runner) -> None:
- """Record testing logs after test epoch.
+ """Record logs after testing epoch.
Args:
- runner (Runner): The runner of the training process.
+ runner (Runner): The runner of the testing process.
"""
- tag, log_str = runner.log_processor.get_log_after_epoch(
+ _, log_str = runner.log_processor.get_log_after_epoch(
runner, len(runner.val_dataloader), 'test')
runner.logger.info(log_str)
@@ -215,13 +214,14 @@ class LoggerHook(Hook):
"""Copy logs to ``self.out_dir`` if ``self.out_dir is not None``
Args:
- runner (Runner): The runner of the training process.
+ runner (Runner): The runner of the training/testing/validation
+ process.
"""
# copy or upload logs to self.out_dir
if self.out_dir is None:
return
- for filename in scandir(runner.work_dir, self.out_suffix, True):
- local_filepath = osp.join(runner.work_dir, filename)
+ for filename in scandir(runner._log_dir, self.out_suffix, True):
+ local_filepath = osp.join(runner._log_dir, filename)
out_filepath = self.file_client.join_path(self.out_dir, filename)
with open(local_filepath, 'r') as f:
self.file_client.put_text(f.read(), out_filepath)
diff --git a/mmengine/logging/log_processor.py b/mmengine/logging/log_processor.py
index 3619bf4b7983d0cc1d15dec25f30362c1a838a30..8454112345a78e808dcc76881bd15f4539cc19c7 100644
--- a/mmengine/logging/log_processor.py
+++ b/mmengine/logging/log_processor.py
@@ -181,7 +181,7 @@ class LogProcessor:
"""Format log string after validation or testing epoch.
Args:
- runner (Runner): The runner of training phase.
+ runner (Runner): The runner of validation/testing phase.
batch_idx (int): The index of the current batch in the current
loop.
mode (str): Current mode of runner.
@@ -200,10 +200,12 @@ class LogProcessor:
custom_cfg_copy = self._parse_windows_size(runner, batch_idx)
# tag is used to write log information to different backends.
tag = self._collect_scalars(custom_cfg_copy, runner, mode)
- # validation log string needs cur epoch/iteration and max
- # epochs/iterations. test log string only needs length of test
- # dataloader.
- cur_iter = self._get_iter(runner, batch_idx)
+ # By epoch:
+ # Epoch(val) [10][1000/1000] ...
+ # Epoch(test) [1000/1000] ...
+ # By iteration:
+ # Iteration(val) [1000/1000] ...
+ # Iteration(test) [1000/1000] ...
if self.by_epoch:
if mode == 'val':
cur_epoch = self._get_epoch(runner, mode)
@@ -214,12 +216,9 @@ class LogProcessor:
f'Epoch({mode}) [{dataloader_len}/{dataloader_len}] ')
else:
- if mode == 'train':
- log_str = (f'Iter({mode}) [{cur_iter}/'
- f'{runner.train_loop.max_iters}] ')
- else:
- log_str = (
- f'Iter({mode}) [{dataloader_len}/{dataloader_len}] ')
+ log_str = (f'Iter({mode}) [{dataloader_len}/{dataloader_len}] ')
+ # `time` and `data_time` will not be recorded in after epoch log
+ # message.
log_items = []
for name, val in tag.items():
if name in ('time', 'data_time'):
@@ -237,9 +236,9 @@ class LogProcessor:
Args:
custom_cfg (List[dict]): A copy of ``self.custom_cfg`` with int
``window_size``.
- runner (Runner): The runner of the training process.
- mode (str): 'train' or 'val', which means the prefix attached by
- runner.
+ runner (Runner): The runner of the training/testing/validation
+ process.
+ mode (str): Current mode of runner.
Returns:
dict: Statistical values of logs.
@@ -253,7 +252,7 @@ class LogProcessor:
# according to mode.
for prefix_key, log_buffer in history_scalars.items():
if prefix_key.startswith(mode):
- key = prefix_key.split('/')[-1]
+ key = prefix_key.partition('/')[-1]
mode_history_scalars[key] = log_buffer
for key in mode_history_scalars:
# Update the latest learning rate and smoothed time logs.
@@ -287,25 +286,23 @@ class LogProcessor:
' is False.'
def _check_repeated_log_name():
- check_dict = dict()
# The `log_name` of the same data_src should not be repeated.
# If `log_name` is not specified, `data_src` will be overwritten.
# But only allowed to be overwritten once.
+ check_set = set()
for log_cfg in self.custom_cfg:
assert 'data_src' in log_cfg
data_src = log_cfg['data_src']
log_name = log_cfg.get('log_name', data_src)
- check_dict.setdefault(data_src,
- dict(log_names=set(), log_counts=0))
- check_dict[data_src]['log_names'].add(log_name)
- check_dict[data_src]['log_counts'] += 1
- assert (len(
- check_dict[data_src]
- ['log_names']) == check_dict[data_src]['log_counts']), (
- f'If you want to statistic {data_src} with multiple '
- 'statistics method, please check `log_name` is unique'
- f'and {data_src} will not be overwritten twice. See '
- f'more information in the docstring of `LogProcessor`')
+ assert log_name not in check_set, (
+ f'Found duplicate {log_name} for {data_src}. Please check'
+ 'your `custom_cfg` for `log_processor`. You should '
+ f'neither define duplicate `{log_name}` for {data_src} '
+ f'nor do not define any {log_name} for multiple '
+ f'{data_src}, See more information in the docstring of '
+ 'LogProcessor')
+
+ check_set.add(log_name)
_check_repeated_log_name()
_check_window_size()
@@ -314,7 +311,8 @@ class LogProcessor:
"""Parse window_size defined in custom_cfg to int value.
Args:
- runner (Runner): The runner of the training process.
+ runner (Runner): The runner of the training/testing/validation
+ process.
batch_idx (int): The iteration index of current dataloader.
"""
custom_cfg_copy = copy.deepcopy(self.custom_cfg)
@@ -337,7 +335,8 @@ class LogProcessor:
for a given device.
Args:
- runner (Runner): The runner of the training process.
+ runner (Runner): The runner of the training/testing/validation
+ process.
Returns:
The maximum GPU memory occupied by tensors in megabytes for a given
@@ -352,11 +351,12 @@ class LogProcessor:
return int(mem_mb.item())
def _get_iter(self, runner, batch_idx: int = None) -> int:
- """Get current training iteration step.
+ """Get current iteration index.
Args:
- runner (Runner): The runner of the training process.
- batch_idx (int, optional): The interaction index of current
+ runner (Runner): The runner of the training/testing/validation
+ process.
+ batch_idx (int, optional): The iteration index of current
dataloader. Defaults to None.
Returns:
@@ -372,8 +372,9 @@ class LogProcessor:
"""Get current epoch according to mode.
Args:
- runner (Runner): The runner of the training/validation process.
- mode (str): Current mode of runner, "train" or "val".
+ runner (Runner): The runner of the training/testing/validation
+ process.
+ mode (str): Current mode of runner.
Returns:
int: The current epoch.
@@ -395,7 +396,7 @@ class LogProcessor:
Args:
runner (Runner): The runner of the training/validation/testing
process.
- mode (str): Current mode of runner, "train", "val" or test.
+ mode (str): Current mode of runner.
Returns:
BaseLoop: Current loop of runner.
diff --git a/mmengine/runner/runner.py b/mmengine/runner/runner.py
index 98cddde636272c2e842a2d6a58380764e3479af0..ec4fccbba67eb959ab670251e54305c5251e5f38 100644
--- a/mmengine/runner/runner.py
+++ b/mmengine/runner/runner.py
@@ -64,7 +64,9 @@ class Runner:
Args:
model (:obj:`torch.nn.Module` or dict): The model to be run. It can be
a dict used for build a model.
- work_dir (str): The working directory to save checkpoints and logs.
+ work_dir (str): The working directory to save checkpoints. The logs
+ will be saved in the subdirectory of `work_dir` named
+ :attr:`timestamp`.
train_dataloader (Dataloader or dict, optional): A dataloader object or
a dict to build a dataloader. If ``None`` is given, it means
skipping training steps. Defaults to None.
@@ -637,7 +639,7 @@ class Runner:
MMLogger: A MMLogger object build from ``logger``.
"""
if log_file is None:
- log_file = osp.join(self._log_dir, f'{self._experiment_name}.log')
+ log_file = osp.join(self._log_dir, f'{self.timestamp}.log')
log_cfg = dict(log_level=log_level, log_file=log_file, **kwargs)
log_cfg.setdefault('name', self._experiment_name)
diff --git a/tests/test_hook/test_logger_hook.py b/tests/test_hook/test_logger_hook.py
index 138ce2a1f031c549483bade8e4d2e4114ae469fa..2e05b84fb685cc7932a085ea6891e7ef8c6374e8 100644
--- a/tests/test_hook/test_logger_hook.py
+++ b/tests/test_hook/test_logger_hook.py
@@ -15,7 +15,7 @@ class TestLoggerHook:
assert logger_hook.interval == 10
assert logger_hook.ignore_last
assert logger_hook.interval_exp_name == 1000
- assert logger_hook.out_suffix == ('.log.json', '.log', '.py')
+ assert logger_hook.out_suffix == ('.json', '.log', '.py', 'yaml')
assert logger_hook.keep_local
assert logger_hook.file_client_args is None
assert isinstance(logger_hook.file_client.client, HardDiskBackend)
@@ -29,36 +29,42 @@ class TestLoggerHook:
def test_before_run(self):
runner = MagicMock()
runner.iter = 10
- runner.timestamp = 'timestamp'
+ runner.timestamp = '20220429'
+ runner._log_dir = f'work_dir/{runner.timestamp}'
runner.work_dir = 'work_dir'
runner.logger = MagicMock()
logger_hook = LoggerHook(out_dir='out_dir')
logger_hook.before_run(runner)
assert logger_hook.out_dir == osp.join('out_dir', 'work_dir')
- assert logger_hook.json_log_path == osp.join('work_dir',
- 'timestamp.log.json')
+ assert logger_hook.json_log_path == f'{runner.timestamp}.json'
def test_after_run(self, tmp_path):
# Test
+ timestamp = '20220429'
out_dir = tmp_path / 'out_dir'
out_dir.mkdir()
work_dir = tmp_path / 'work_dir'
work_dir.mkdir()
- work_dir_json = work_dir / 'tmp.log.json'
+ log_dir = work_dir / timestamp
+ log_dir.mkdir()
+ log_dir_json = log_dir / 'tmp.log.json'
runner = MagicMock()
- runner.work_dir = work_dir
+ runner._log_dir = str(log_dir)
+ runner.timestamp = timestamp
+ runner.work_dir = str(work_dir)
# Test without out_dir.
logger_hook = LoggerHook()
logger_hook.after_run(runner)
# Test with out_dir and make sure json file has been moved to out_dir.
- json_f = open(work_dir_json, 'w')
+ json_f = open(log_dir_json, 'w')
json_f.close()
- logger_hook = LoggerHook(out_dir=str(tmp_path), keep_local=False)
+ logger_hook = LoggerHook(out_dir=str(out_dir), keep_local=False)
logger_hook.out_dir = str(out_dir)
+ logger_hook.before_run(runner)
logger_hook.after_run(runner)
# Verify that the file has been moved to `out_dir`.
- assert not osp.exists(str(work_dir_json))
- assert osp.exists(str(out_dir / 'tmp.log.json'))
+ assert not osp.exists(str(log_dir_json))
+ assert osp.exists(str(out_dir / 'work_dir' / 'tmp.log.json'))
def test_after_train_iter(self):
# Test LoggerHook by iter.
@@ -89,7 +95,7 @@ class TestLoggerHook:
runner.log_processor.get_log_after_iter = MagicMock(
return_value=(dict(), 'log_str'))
logger_hook = LoggerHook(ignore_last=False)
- runner.train_dataloader = [0] * 5
+ runner.train_loop.dataloader = [0] * 5
logger_hook.after_train_iter(runner, batch_idx=4)
runner.log_processor.get_log_after_iter.assert_called()