[Enhancement] Replace warnings.warn with print_log

mmengine/dataset/base_dataset.py

@@ -2,15 +2,16 @@
 import copy
 import functools
 import gc
+import logging
 import os.path as osp
 import pickle
-import warnings
 from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 from torch.utils.data import Dataset
 
 from mmengine.fileio import list_from_file, load
+from mmengine.logging import print_log
 from mmengine.registry import TRANSFORMS
 from mmengine.utils import is_abs
 
@@ -100,11 +101,13 @@ def wrapper(obj: object, *args, **kwargs):
         # `_fully_initialized` is False, call `full_init` and set
         # `_fully_initialized` to True
         if not getattr(obj, '_fully_initialized', False):
-            warnings.warn('Attribute `_fully_initialized` is not defined in '
-                          f'{type(obj)} or `type(obj)._fully_initialized is '
-                          'False, `full_init` will be called and '
-                          f'{type(obj)}._fully_initialized will be set to '
-                          'True')
+            print_log(
+                f'Attribute `_fully_initialized` is not defined in '
+                f'{type(obj)} or `type(obj)._fully_initialized is '
+                'False, `full_init` will be called and '
+                f'{type(obj)}._fully_initialized will be set to True',
+                logger='current',
+                level=logging.WARNING)
             obj.full_init()  # type: ignore
             obj._fully_initialized = True  # type: ignore
 
@@ -392,9 +395,11 @@ def __getitem__(self, idx: int) -> dict:
         # to manually call `full_init` before dataset fed into dataloader to
         # ensure all workers use shared RAM from master process.
         if not self._fully_initialized:
-            warnings.warn(
+            print_log(
                 'Please call `full_init()` method manually to accelerate '
-                'the speed.')
+                'the speed.',
+                logger='current',
+                level=logging.WARNING)
             self.full_init()
 
         if self.test_mode:
@@ -498,8 +503,11 @@ def _load_metainfo(cls, metainfo: dict = None) -> dict:
                 try:
                     cls_metainfo[k] = list_from_file(v)
                 except (TypeError, FileNotFoundError):
-                    warnings.warn(f'{v} is not a meta file, simply parsed as '
-                                  'meta information')
+                    print_log(
+                        f'{v} is not a meta file, simply parsed as meta '
+                        'information',
+                        logger='current',
+                        level=logging.WARNING)
                     cls_metainfo[k] = v
             else:
                 cls_metainfo[k] = v

mmengine/dataset/dataset_wrapper.py

@@ -1,13 +1,14 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import bisect
 import copy
+import logging
 import math
-import warnings
 from collections import defaultdict
 from typing import List, Sequence, Tuple, Union
 
 from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
 
+from mmengine.logging import print_log
 from mmengine.registry import DATASETS
 from .base_dataset import BaseDataset, force_full_init
 
@@ -148,8 +149,11 @@ def __len__(self):
 
     def __getitem__(self, idx):
         if not self._fully_initialized:
-            warnings.warn('Please call `full_init` method manually to '
-                          'accelerate the speed.')
+            print_log(
+                'Please call `full_init` method manually to '
+                'accelerate the speed.',
+                logger='current',
+                level=logging.WARNING)
             self.full_init()
         dataset_idx, sample_idx = self._get_ori_dataset_idx(idx)
         return self.datasets[dataset_idx][sample_idx]
@@ -263,8 +267,11 @@ def get_data_info(self, idx: int) -> dict:
 
     def __getitem__(self, idx):
         if not self._fully_initialized:
-            warnings.warn('Please call `full_init` method manually to '
-                          'accelerate the speed.')
+            print_log(
+                'Please call `full_init` method manually to accelerate the '
+                'speed.',
+                logger='current',
+                level=logging.WARNING)
             self.full_init()
 
         sample_idx = self._get_ori_dataset_idx(idx)
@@ -470,9 +477,12 @@ def get_data_info(self, idx: int) -> dict:
         return self.dataset.get_data_info(sample_idx)
 
     def __getitem__(self, idx):
-        warnings.warn('Please call `full_init` method manually to '
-                      'accelerate the speed.')
         if not self._fully_initialized:
+            print_log(
+                'Please call `full_init` method manually to accelerate '
+                'the speed.',
+                logger='current',
+                level=logging.WARNING)
             self.full_init()
 
         ori_index = self._get_ori_dataset_idx(idx)

mmengine/evaluator/metric.py

@@ -1,5 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-import warnings
+import logging
 from abc import ABCMeta, abstractmethod
 from typing import Any, List, Optional, Sequence, Union
 
@@ -44,8 +44,11 @@ def __init__(self,
         self.results: List[Any] = []
         self.prefix = prefix or self.default_prefix
         if self.prefix is None:
-            warnings.warn('The prefix is not set in metric class '
-                          f'{self.__class__.__name__}.')
+            print_log(
+                'The prefix is not set in metric class '
+                f'{self.__class__.__name__}.',
+                logger='current',
+                level=logging.WARNING)
 
     @property
     def dataset_meta(self) -> Optional[dict]:
@@ -97,10 +100,12 @@ def evaluate(self, size: int) -> dict:
             names of the metrics, and the values are corresponding results.
         """
         if len(self.results) == 0:
-            warnings.warn(
+            print_log(
                 f'{self.__class__.__name__} got empty `self.results`. Please '
                 'ensure that the processed results are properly added into '
-                '`self.results` in `process` method.')
+                '`self.results` in `process` method.',
+                logger='current',
+                level=logging.WARNING)
 
         results = collect_results(self.results, size, self.collect_device)
 

mmengine/fileio/backends/base.py

@@ -1,7 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-import warnings
+import logging
 from abc import ABCMeta, abstractmethod
 
+from mmengine.logging import print_log
+
 
 class BaseStorageBackend(metaclass=ABCMeta):
     """Abstract class of storage backends.
@@ -19,8 +21,10 @@ class BaseStorageBackend(metaclass=ABCMeta):
 
     @property
     def allow_symlink(self):
-        warnings.warn('allow_symlink will be deprecated in future',
-                      DeprecationWarning)
+        print_log(
+            'allow_symlink will be deprecated in future',
+            logger='current',
+            level=logging.WARNING)
         return self._allow_symlink
 
     @property

mmengine/fileio/file_client.py

@@ -1,10 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import inspect
-import warnings
+import logging
 from contextlib import contextmanager
 from pathlib import Path
 from typing import Any, Generator, Iterator, Optional, Tuple, Union
 
+from mmengine.logging import print_log
 from mmengine.utils import is_filepath
 from .backends import (BaseStorageBackend, HTTPBackend, LmdbBackend,
                        LocalBackend, MemcachedBackend, PetrelBackend)
@@ -14,9 +15,11 @@ class HardDiskBackend(LocalBackend):
     """Raw hard disks storage backend."""
 
     def __init__(self) -> None:
-        warnings.warn(
+        print_log(
             '"HardDiskBackend" is the alias of "LocalBackend" '
-            'and the former will be deprecated in future.', DeprecationWarning)
+            'and the former will be deprecated in future.',
+            logger='current',
+            level=logging.WARNING)
 
     @property
     def name(self):
@@ -83,11 +86,12 @@ class FileClient:
     client: Any
 
     def __new__(cls, backend=None, prefix=None, **kwargs):
-        warnings.warn(
+        print_log(
             '"FileClient" will be deprecated in future. Please use io '
             'functions in '
             'https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io',  # noqa: E501
-            DeprecationWarning)
+            logger='current',
+            level=logging.WARNING)
         if backend is None and prefix is None:
             backend = 'disk'
         if backend is not None and backend not in cls._backends:

mmengine/hooks/checkpoint_hook.py

@@ -1,12 +1,13 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import logging
 import os.path as osp
-import warnings
 from math import inf
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Sequence, Union
 
 from mmengine.dist import is_main_process
 from mmengine.fileio import FileClient, get_file_backend
+from mmengine.logging import print_log
 from mmengine.registry import HOOKS
 from mmengine.utils import is_list_of, is_seq_of
 from .hook import Hook
@@ -138,9 +139,11 @@ def __init__(self,
         self.args = kwargs
 
         if file_client_args is not None:
-            warnings.warn(
+            print_log(
                 '"file_client_args" will be deprecated in future. '
-                'Please use "backend_args" instead', DeprecationWarning)
+                'Please use "backend_args" instead',
+                logger='current',
+                level=logging.WARNING)
             if backend_args is not None:
                 raise ValueError(
                     '"file_client_args" and "backend_args" cannot be set '

mmengine/hooks/logger_hook.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import logging
 import os
 import os.path as osp
-import warnings
 from collections import OrderedDict
 from pathlib import Path
 from typing import Dict, Optional, Sequence, Union
@@ -12,6 +12,7 @@
 from mmengine.fileio import FileClient, dump
 from mmengine.fileio.io import get_file_backend
 from mmengine.hooks import Hook
+from mmengine.logging import print_log
 from mmengine.registry import HOOKS
 from mmengine.utils import is_tuple_of, scandir
 
@@ -94,9 +95,11 @@ def __init__(self,
         self.out_dir = out_dir
 
         if file_client_args is not None:
-            warnings.warn(
+            print_log(
                 '"file_client_args" will be deprecated in future. '
-                'Please use "backend_args" instead', DeprecationWarning)
+                'Please use "backend_args" instead',
+                logger='current',
+                level=logging.WARNING)
             if backend_args is not None:
                 raise ValueError(
                     '"file_client_args" and "backend_args" cannot be set '

mmengine/hooks/profiler_hook.py

@@ -1,14 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import logging
 import os
 import os.path as osp
 import sys
-import warnings
 from typing import Callable, Optional, Union
 
 import torch
 
 from mmengine.dist import master_only
 from mmengine.hooks import Hook
+from mmengine.logging import print_log
 from mmengine.registry import HOOKS
 
 
@@ -18,7 +19,7 @@ def check_kineto() -> bool:  # noqa
         if torch.autograd.kineto_available():
             kineto_exist = True
     except AttributeError:
-        warnings.warn('NO KINETO')
+        print_log('NO KINETO', logger='current', level=logging.WARNING)
     return kineto_exist
 
 

mmengine/model/averaged_model.py

@@ -1,5 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-import warnings
+import logging
 from abc import abstractmethod
 from copy import deepcopy
 from typing import Optional
@@ -8,6 +8,7 @@
 import torch.nn as nn
 from torch import Tensor
 
+from mmengine.logging import print_log
 from mmengine.registry import MODELS
 
 
@@ -184,11 +185,13 @@ def __init__(self,
         assert 0.0 < momentum < 1.0, 'momentum must be in range (0.0, 1.0)'\
                                      f'but got {momentum}'
         if momentum > 0.5:
-            warnings.warn(
+            print_log(
                 'The value of momentum in EMA is usually a small number,'
                 'which is different from the conventional notion of '
                 f'momentum but got {momentum}. Please make sure the '
-                f'value is correct.')
+                f'value is correct.',
+                logger='current',
+                level=logging.WARNING)
         self.momentum = momentum
 
     def avg_func(self, averaged_param: Tensor, source_param: Tensor,

mmengine/model/base_module.py

@@ -1,7 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
 import logging
-import warnings
 from abc import ABCMeta
 from collections import defaultdict
 from logging import FileHandler
@@ -139,8 +138,11 @@ def init_weights(self):
                 initialize(self, pretrained_cfg)
             self._is_init = True
         else:
-            warnings.warn(f'init_weights of {self.__class__.__name__} has '
-                          f'been called more than once.')
+            print_log(
+                f'init_weights of {self.__class__.__name__} has '
+                f'been called more than once.',
+                logger='current',
+                level=logging.WARNING)
 
         if is_top_level_module:
             # self._dump_init_info(logger_name)

mmengine/optim/optimizer/default_constructor.py

@@ -1,5 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-import warnings
+import logging
 from typing import List, Optional, Union
 
 import torch
@@ -205,8 +205,11 @@ def add_params(self,
         for name, param in module.named_parameters(recurse=False):
             param_group = {'params': [param]}
             if bypass_duplicate and self._is_in(param_group, params):
-                warnings.warn(f'{prefix} is duplicate. It is skipped since '
-                              f'bypass_duplicate={bypass_duplicate}')
+                print_log(
+                    f'{prefix} is duplicate. It is skipped since '
+                    f'bypass_duplicate={bypass_duplicate}',
+                    logger='current',
+                    level=logging.WARNING)
                 continue
             if not param.requires_grad:
                 params.append(param_group)