[Refactor] Apply get_current_instance instead of get_root_logger (#394)

mmselfsup/engine/optimizers/layer_decay_optim_wrapper_constructor.py

@@ -4,12 +4,12 @@ from typing import Dict, List, Optional, Union
 
 import torch
 from mmengine.dist import get_dist_info
+from mmengine.logging import MMLogger
 from mmengine.optim import DefaultOptimWrapperConstructor
 from torch import nn
 
 from mmselfsup.registry import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIM_WRAPPERS,
                                 OPTIMIZERS)
-from mmselfsup.utils import get_root_logger
 
 
 def get_layer_id_for_vit(var_name: str, max_layer_id: int) -> int:
@@ -88,7 +88,7 @@ class LearningRateDecayOptimWrapperConstructor(DefaultOptimWrapperConstructor):
                 submodule of DCN, `is_dcn_module` will be passed to
                 control conv_offset layer's learning rate. Defaults to None.
         """
-        logger = get_root_logger()
+        logger = MMLogger.get_current_instance()
 
         model_type = optimizer_cfg.pop('model_type', None)
         # model_type should not be None

mmselfsup/utils/__init__.py

@@ -5,13 +5,11 @@ from .collect import dist_forward_collect, nondist_forward_collect
 from .collect_env import collect_env
 from .distributed_sinkhorn import distributed_sinkhorn
 from .gather import concat_all_gather, gather_tensors, gather_tensors_batch
-from .logger import get_root_logger
 from .setup_env import register_all_modules
 
 __all__ = [
     'AliasMethod', 'batch_shuffle_ddp', 'batch_unshuffle_ddp',
     'dist_forward_collect', 'nondist_forward_collect', 'collect_env',
     'sync_random_seed', 'distributed_sinkhorn', 'concat_all_gather',
-    'gather_tensors', 'gather_tensors_batch', 'get_root_logger',
+    'gather_tensors', 'gather_tensors_batch', 'register_all_modules'
-    'register_all_modules'
 ]

mmselfsup/utils/logger.py

@@ -1,27 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import logging
-
-from mmengine.logging import MMLogger
-
-
-def get_root_logger(log_file: str = None,
-                    log_level: int = logging.INFO) -> logging.Logger:
-    """Get root logger.
-
-    Args:
-        log_file (str, optional): File path of log. Defaults to None.
-        log_level (int, optional): The level of logger.
-            Defaults to :obj:`logging.INFO`.
-
-    Returns:
-        :obj:`logging.Logger`: The obtained logger
-    """
-    try:
-        return MMLogger.get_instance(
-            'mmselfsup',
-            logger_name='mmselfsup',
-            log_file=log_file,
-            log_level=log_level)
-    except AssertionError:
-        # if root logger already existed, no extra kwargs needed.
-        return MMLogger.get_instance('mmselfsup')

tests/test_utils/test_logger.py

@@ -1,34 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import tempfile
-
-from mmengine.logging import MMLogger
-
-from mmselfsup.utils import get_root_logger
-
-
-def test_get_root_logger():
-    # set all logger instance
-    MMLogger._instance_dict = {}
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        log_path = osp.join(tmpdirname, 'test.log')
-
-        logger = get_root_logger(log_file=log_path)
-        message1 = 'adhsuadghj'
-        logger.info(message1)
-
-        logger2 = get_root_logger()
-        message2 = 'm,tkrgmkr'
-        logger2.info(message2)
-
-        with open(log_path, 'r') as f:
-            lines = f.readlines()
-            assert message1 in lines[0]
-            assert message2 in lines[1]
-
-        assert logger is logger2
-
-        handlers = list(logger.handlers)
-        for handler in handlers:
-            handler.close()
-            logger.removeHandler(handler)

tools/analysis_tools/visualize_tsne.py

@@ -11,6 +11,7 @@ import torch
 from mmengine.config import Config, DictAction
 from mmengine.data import pseudo_collate, worker_init_fn
 from mmengine.dist import get_rank, init_dist
+from mmengine.logging import MMLogger
 from mmengine.model.wrappers import MMDistributedDataParallel, is_model_wrapper
 from mmengine.runner import load_checkpoint
 from mmengine.utils import mkdir_or_exist
@@ -19,7 +20,7 @@ from torch.utils.data import DataLoader
 
 from mmselfsup.models.utils import Extractor
 from mmselfsup.registry import DATA_SAMPLERS, DATASETS, MODELS
-from mmselfsup.utils import get_root_logger, register_all_modules
+from mmselfsup.utils import register_all_modules
 
 
 def parse_args():
@@ -136,7 +137,11 @@ def main():
 
     # init the logger before other steps
     log_file = osp.join(tsne_work_dir, 'extract.log')
-    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
+    logger = MMLogger.get_instance(
+        'mmselfsup',
+        logger_name='mmselfsup',
+        log_file=log_file,
+        log_level=cfg.log_level)
 
     # build the dataset
     dataset_cfg = Config.fromfile(args.dataset_config)

tools/benchmarks/classification/svm_voc07/extract.py

@@ -11,6 +11,7 @@ import torch
 from mmengine.config import Config, DictAction
 from mmengine.data import pseudo_collate, worker_init_fn
 from mmengine.dist import get_rank, init_dist
+from mmengine.logging import MMLogger
 from mmengine.model.wrappers import MMDistributedDataParallel, is_model_wrapper
 from mmengine.runner import load_checkpoint
 from mmengine.utils import mkdir_or_exist
@@ -18,7 +19,7 @@ from torch.utils.data import DataLoader
 
 from mmselfsup.models.utils import Extractor
 from mmselfsup.registry import DATA_SAMPLERS, DATASETS, MODELS
-from mmselfsup.utils import get_root_logger, register_all_modules
+from mmselfsup.utils import register_all_modules
 
 
 def parse_args():
@@ -100,7 +101,11 @@ def main():
     # init the logger before other steps
     timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
     log_file = osp.join(cfg.work_dir, f'extract_{timestamp}.log')
-    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
+    logger = MMLogger.get_instance(
+        'mmselfsup',
+        logger_name='mmselfsup',
+        log_file=log_file,
+        log_level=cfg.log_level)
 
     # build the dataset
     dataset_cfg = Config.fromfile(args.dataset_config)