[Feature] Support auto import modules from registry (#660)

* support auto import

* update

* fix lint

* update

* fix lint

* update

* refine

* update

* update colab

configs/tsne/resnet50_imagenet.py

@@ -14,7 +14,7 @@ model = dict(
         depth=50,
         in_channels=3,
         num_stages=4,
-        out_indices=(3),
+        out_indices=(3, ),
         norm_cfg=dict(type='BN'),
         frozen_stages=-1),
     neck=dict(type='GlobalAveragePooling'),

demo/mae_visualization.ipynb

@@ -62,8 +62,7 @@
     "import torch\n",
     "from mmengine.dataset import Compose, default_collate\n",
     "\n",
-    "from mmselfsup.apis import inference_model, init_model\n",
-    "from mmselfsup.utils import register_all_modules"
+    "from mmselfsup.apis import inference_model, init_model"
    ]
   },
   {
@@ -238,7 +237,6 @@
    ],
    "source": [
     "# make random mask reproducible (comment out to make it change)\n",
-    "register_all_modules()\n",
     "torch.manual_seed(2)"
    ]
   },

demo/mmselfsup_colab_tutorial.ipynb

@@ -157,7 +157,7 @@
             ],
             "source": [
                 "# Check PyTorch installation\n",
-                "import torch, torchvision\n",
+                "import torch\n",
                 "print(torch.__version__)\n",
                 "print(torch.cuda.is_available())"
             ]
@@ -313,7 +313,7 @@
             "source": [
                 "!pip3 install openmim\n",
                 "!pip install -U openmim\n",
-                "!mim install 'mmengine' 'mmcv>=2.0.0rc1'"
+                "!mim install 'mmengine' 'mmcv>=2.0.0rc4'"
             ]
         },
         {
@@ -1418,15 +1418,8 @@
                 }
             ],
             "source": [
-                "from mmengine.config import Config, DictAction\n",
                 "from mmengine.runner import Runner\n",
                 "\n",
-                "from mmselfsup.utils import register_all_modules\n",
-                "\n",
-                "# register all modules in mmselfsup into the registries\n",
-                "# do not init the default scope here because it will be init in the runner\n",
-                "register_all_modules(init_default_scope=False)\n",
-                "\n",
                 "# build the runner from config\n",
                 "runner = Runner.from_cfg(cfg)\n",
                 "\n",
@@ -2669,15 +2662,8 @@
                 }
             ],
             "source": [
-                "from mmengine.config import Config, DictAction\n",
                 "from mmengine.runner import Runner\n",
                 "\n",
-                "from mmselfsup.utils import register_all_modules\n",
-                "\n",
-                "# register all modules in mmselfsup into the registries\n",
-                "# do not init the default scope here because it will be init in the runner\n",
-                "register_all_modules(init_default_scope=False)\n",
-                "\n",
                 "# build the runner from config\n",
                 "runner = Runner.from_cfg(benchmark_cfg)\n",
                 "\n",

mmselfsup/__init__.py

@@ -10,7 +10,7 @@ mmengine_minimum_version = '0.4.0'
 mmengine_maximum_version = '1.0.0'
 mmengine_version = digit_version(mmengine.__version__)
 
-mmcv_minimum_version = '2.0.0rc1'
+mmcv_minimum_version = '2.0.0rc4'
 mmcv_maximum_version = '2.1.0'
 mmcv_version = digit_version(mmcv.__version__)
 

mmselfsup/apis/inference.py

@@ -6,6 +6,7 @@ import numpy as np
 import torch
 from mmengine.config import Config
 from mmengine.dataset import Compose, default_collate
+from mmengine.registry import init_default_scope
 from mmengine.runner import load_checkpoint
 from torch import nn
 
@@ -36,8 +37,11 @@ def init_model(config: Union[str, Config],
     elif not isinstance(config, Config):
         raise TypeError('config must be a filename or Config object, '
                         f'but got {type(config)}')
+
     if options is not None:
         config.merge_from_dict(options)
+    init_default_scope(config.get('default_scope', 'mmselfsup'))
+
     config.model.pretrained = None
     config.model.setdefault('data_preprocessor',
                             config.get('data_preprocessor', None))

mmselfsup/registry.py

@@ -54,55 +54,93 @@ __all__ = [
 
 # Registries For Runner and the related
 # manage all kinds of runners like `EpochBasedRunner` and `IterBasedRunner`
-RUNNERS = Registry('runner', parent=MMENGINE_RUNNERS)
+RUNNERS = Registry(
+    'runner', parent=MMENGINE_RUNNERS, locations=['mmselfsup.engine.runner'])
 # manage runner constructors that define how to initialize runners
 RUNNER_CONSTRUCTORS = Registry(
-    'runner constructor', parent=MMENGINE_RUNNER_CONSTRUCTORS)
+    'runner constructor',
+    parent=MMENGINE_RUNNER_CONSTRUCTORS,
+    locations=['mmselfsup.engine.runner'])
 # manage all kinds of loops like `EpochBasedTrainLoop`
-LOOPS = Registry('loop', parent=MMENGINE_LOOPS)
+LOOPS = Registry(
+    'loop', parent=MMENGINE_LOOPS, locations=['mmselfsup.engine.runner'])
 # manage all kinds of hooks like `CheckpointHook`
-HOOKS = Registry('hook', parent=MMENGINE_HOOKS)
+HOOKS = Registry(
+    'hook', parent=MMENGINE_HOOKS, locations=['mmselfsup.engine.hooks'])
 
 # Registries For Data and the related
 # manage data-related modules
-DATASETS = Registry('dataset', parent=MMENGINE_DATASETS)
-DATA_SAMPLERS = Registry('data sampler', parent=MMENGINE_DATA_SAMPLERS)
-TRANSFORMS = Registry('transform', parent=MMENGINE_TRANSFORMS)
+DATASETS = Registry(
+    'dataset', parent=MMENGINE_DATASETS, locations=['mmselfsup.datasets'])
+DATA_SAMPLERS = Registry(
+    'data sampler',
+    parent=MMENGINE_DATA_SAMPLERS,
+    locations=['mmselfsup.datasets.samplers'])
+TRANSFORMS = Registry(
+    'transform',
+    parent=MMENGINE_TRANSFORMS,
+    locations=['mmselfsup.datasets.transforms'])
 
 # manage all kinds of modules inheriting `nn.Module`
-MODELS = Registry('model', parent=MMENGINE_MODELS)
+MODELS = Registry(
+    'model', parent=MMENGINE_MODELS, locations=['mmselfsup.models'])
 # manage all kinds of model wrappers like 'MMDistributedDataParallel'
-MODEL_WRAPPERS = Registry('model_wrapper', parent=MMENGINE_MODEL_WRAPPERS)
+MODEL_WRAPPERS = Registry(
+    'model_wrapper',
+    parent=MMENGINE_MODEL_WRAPPERS,
+    locations=['mmselfsup.models'])
 # manage all kinds of weight initialization modules like `Uniform`
 WEIGHT_INITIALIZERS = Registry(
-    'weight initializer', parent=MMENGINE_WEIGHT_INITIALIZERS)
+    'weight initializer',
+    parent=MMENGINE_WEIGHT_INITIALIZERS,
+    locations=['mmselfsup.models'])
 
 # Registries For Optimizer and the related
 # manage all kinds of optimizers like `SGD` and `Adam`
-OPTIMIZERS = Registry('optimizer', parent=MMENGINE_OPTIMIZERS)
+OPTIMIZERS = Registry(
+    'optimizer',
+    parent=MMENGINE_OPTIMIZERS,
+    locations=['mmselfsup.engine.optimizers'])
 # manage optimizer wrapper
-OPTIM_WRAPPERS = Registry('optimizer_wrapper', parent=MMENGINE_OPTIM_WRAPPERS)
+OPTIM_WRAPPERS = Registry(
+    'optimizer_wrapper',
+    parent=MMENGINE_OPTIM_WRAPPERS,
+    locations=['mmselfsup.engine.optimizers'])
 # manage constructors that customize the optimization hyperparameters.
 OPTIM_WRAPPER_CONSTRUCTORS = Registry(
     'optimizer wrapper constructor',
-    parent=MMENGINE_OPTIM_WRAPPER_CONSTRUCTORS)
+    parent=MMENGINE_OPTIM_WRAPPER_CONSTRUCTORS,
+    locations=['mmselfsup.engine.optimizers'])
 # manage all kinds of parameter schedulers like `MultiStepLR`
 PARAM_SCHEDULERS = Registry(
-    'parameter scheduler', parent=MMENGINE_PARAM_SCHEDULERS)
+    'parameter scheduler',
+    parent=MMENGINE_PARAM_SCHEDULERS,
+    locations=['mmselfsup.engine.schedulers'])
 
 # manage all kinds of metrics
-METRICS = Registry('metric', parent=MMENGINE_METRICS)
+METRICS = Registry(
+    'metric', parent=MMENGINE_METRICS, locations=['mmselfsup.evaluation'])
 # manage evaluator
-EVALUATOR = Registry('evaluator', parent=MMENGINE_EVALUATOR)
+EVALUATOR = Registry(
+    'evaluator', parent=MMENGINE_EVALUATOR, locations=['mmselfsup.evaluation'])
 
 # manage task-specific modules like anchor generators and box coders
-TASK_UTILS = Registry('task util', parent=MMENGINE_TASK_UTILS)
+TASK_UTILS = Registry(
+    'task util', parent=MMENGINE_TASK_UTILS, locations=['mmselfsup.models'])
 
-# Registries For Visualizer and the related
 # manage visualizer
-VISUALIZERS = Registry('visualizer', parent=MMENGINE_VISUALIZERS)
+VISUALIZERS = Registry(
+    'visualizer',
+    parent=MMENGINE_VISUALIZERS,
+    locations=['mmselfsup.visualization'])
 # manage visualizer backend
-VISBACKENDS = Registry('vis_backend', parent=MMENGINE_VISBACKENDS)
+VISBACKENDS = Registry(
+    'vis_backend',
+    parent=MMENGINE_VISBACKENDS,
+    locations=['mmselfsup.visualization'])
 
 # manage logprocessor
-LOG_PROCESSORS = Registry('log_processor', parent=MMENGINE_LOG_PROCESSORS)
+LOG_PROCESSORS = Registry(
+    'log_processor',
+    parent=MMENGINE_LOG_PROCESSORS,
+    locations=['mmselfsup.visualization'])

tests/test_apis/test_inference.py

@@ -7,11 +7,11 @@ import pytest
 import torch
 import torch.nn as nn
 from mmengine.config import Config
+from mmengine.registry import init_default_scope
 
 from mmselfsup.apis import inference_model
 from mmselfsup.models import BaseModel
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
 
 backbone = dict(
     type='ResNet',
@@ -37,12 +37,11 @@ class ExampleModel(BaseModel):
 
 @pytest.mark.skipif(platform.system() == 'Windows', reason='')
 def test_inference_model():
-    register_all_modules()
-
     # Specify the data settings
     cfg = Config.fromfile(
         'configs/selfsup/relative_loc/relative-loc_resnet50_8xb64-steplr-70e_in1k.py'  # noqa: E501
     )
+    init_default_scope(cfg.get('default_scope', 'mmselfsup'))
     # Build the algorithm
     model = ExampleModel()
     model.cfg = cfg

tests/test_datasets/test_deepcluster_imagenet.py

@@ -2,9 +2,9 @@
 import os.path as osp
 
 import pytest
+from mmengine.registry import init_default_scope
 
 from mmselfsup.datasets import DeepClusterImageNet
-from mmselfsup.utils import register_all_modules
 
 # dataset settings
 train_pipeline = [
@@ -14,7 +14,7 @@ train_pipeline = [
 
 
 def test_deepcluster_dataset():
-    register_all_modules()
+    init_default_scope('mmselfsup')
 
     data = dict(
         ann_file=osp.join(

tests/test_datasets/test_image_list_dataset.py

@@ -3,9 +3,9 @@ import os.path as osp
 
 import numpy as np
 import pytest
+from mmengine.registry import init_default_scope
 
 from mmselfsup.datasets import ImageList
-from mmselfsup.utils import register_all_modules
 
 # dataset settings
 train_pipeline = [
@@ -15,7 +15,7 @@ train_pipeline = [
 
 
 def test_image_list_dataset():
-    register_all_modules()
+    init_default_scope('mmselfsup')
 
     data = dict(
         ann_file='',

tests/test_models/test_algorithms/test_barlowtwins.py

@@ -7,9 +7,6 @@ import torch
 
 from mmselfsup.models import BarlowTwins
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(
     type='ResNet',

tests/test_models/test_algorithms/test_beitv1.py

@@ -7,7 +7,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models import BEiT
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
 
 data_preprocessor = dict(
     type='TwoNormDataPreprocessor',
@@ -37,8 +36,6 @@ target_generator = dict(type='DALL-E')
 
 @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
 def test_beitv1():
-    register_all_modules()
-
     model = BEiT(
         backbone=backbone,
         neck=neck,

tests/test_models/test_algorithms/test_beitv2.py

@@ -7,7 +7,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models import BEiT
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
 
 data_preprocessor = dict(
     type='TwoNormDataPreprocessor',
@@ -70,8 +69,6 @@ target_generator = dict(type='VQKD', encoder_config=vqkd_encoder)
 
 @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
 def test_beitv2():
-    register_all_modules()
-
     model = BEiT(
         backbone=backbone,
         neck=neck,

tests/test_models/test_algorithms/test_byol.py

@@ -7,9 +7,7 @@ import torch
 
 from mmselfsup.models.algorithms.byol import BYOL
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
 
-register_all_modules()
 backbone = dict(
     type='ResNet',
     depth=18,

tests/test_models/test_algorithms/test_cae.py

@@ -7,9 +7,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models.algorithms import CAE
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 # model settings
 backbone = dict(type='CAEViT', arch='b', patch_size=16, init_values=0.1)

tests/test_models/test_algorithms/test_deepcluster.py

@@ -8,9 +8,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models.algorithms import DeepCluster
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 num_classes = 5
 with_sobel = True,

tests/test_models/test_algorithms/test_densecl.py

@@ -9,9 +9,6 @@ import torch
 import mmselfsup
 from mmselfsup.models.algorithms.densecl import DenseCL
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 queue_len = 32
 feat_dim = 2

tests/test_models/test_algorithms/test_eva.py

@@ -8,9 +8,6 @@ import torch
 
 from mmselfsup.models.algorithms import EVA
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
 neck = dict(

tests/test_models/test_algorithms/test_mae.py

@@ -7,9 +7,6 @@ import torch
 
 from mmselfsup.models.algorithms.mae import MAE
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
 neck = dict(

tests/test_models/test_algorithms/test_maskfeat.py

@@ -9,9 +9,6 @@ from mmengine.utils import digit_version
 
 from mmselfsup.models.algorithms.maskfeat import MaskFeat
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(type='MaskFeatViT', arch='b', patch_size=16)
 neck = dict(

tests/test_models/test_algorithms/test_milan.py

@@ -8,9 +8,6 @@ import torch
 
 from mmselfsup.models.algorithms import MILAN
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(type='MILANViT', arch='b', patch_size=16, mask_ratio=0.75)
 neck = dict(

tests/test_models/test_algorithms/test_moco.py

@@ -7,9 +7,6 @@ import torch
 
 from mmselfsup.models.algorithms import MoCo
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 queue_len = 32
 feat_dim = 2

tests/test_models/test_algorithms/test_mocov3.py

@@ -7,9 +7,6 @@ import torch
 
 from mmselfsup.models import MoCoV3
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(
     type='MoCoV3ViT',

tests/test_models/test_algorithms/test_npid.py

@@ -8,9 +8,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models.algorithms import NPID
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(
     type='ResNet',

tests/test_models/test_algorithms/test_odc.py

@@ -8,9 +8,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models.algorithms import ODC
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 num_classes = 5
 backbone = dict(

tests/test_models/test_algorithms/test_relative_loc.py

@@ -7,9 +7,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models.algorithms.relative_loc import RelativeLoc
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(
     type='ResNet',

tests/test_models/test_algorithms/test_rotation_pred.py

@@ -8,9 +8,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models.algorithms.rotation_pred import RotationPred
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(
     type='ResNet',

tests/test_models/test_algorithms/test_simclr.py

@@ -7,9 +7,6 @@ import torch
 
 from mmselfsup.models.algorithms.simclr import SimCLR
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(
     type='ResNet',

tests/test_models/test_algorithms/test_simmim.py

@@ -7,9 +7,6 @@ from mmengine.structures import InstanceData
 
 from mmselfsup.models.algorithms import SimMIM
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 
 @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')

tests/test_models/test_algorithms/test_simsiam.py

@@ -7,9 +7,6 @@ import torch
 
 from mmselfsup.models.algorithms import SimSiam
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 backbone = dict(
     type='ResNet',

tests/test_models/test_algorithms/test_swav.py

@@ -7,9 +7,6 @@ import torch
 
 from mmselfsup.models.algorithms.swav import SwAV
 from mmselfsup.structures import SelfSupDataSample
-from mmselfsup.utils import register_all_modules
-
-register_all_modules()
 
 nmb_crops = [2, 6]
 backbone = dict(

tests/test_utils/test_setup_env.py

@@ -1,40 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import datetime
-import sys
-from unittest import TestCase
-
-from mmengine import DefaultScope
-
-from mmselfsup.utils import register_all_modules
-
-
-class TestSetupEnv(TestCase):
-
-    def test_register_all_modules(self):
-        from mmselfsup.registry import DATASETS
-
-        # not init default scope
-        sys.modules.pop('mmselfsup.datasets', None)
-        sys.modules.pop('mmselfsup.datasets.places205', None)
-        DATASETS._module_dict.pop('Places205', None)
-        self.assertFalse('Places205' in DATASETS.module_dict)
-        register_all_modules(init_default_scope=False)
-        self.assertTrue('Places205' in DATASETS.module_dict)
-
-        # init default scope
-        sys.modules.pop('mmselfsup.datasets')
-        sys.modules.pop('mmselfsup.datasets.places205')
-        DATASETS._module_dict.pop('Places205', None)
-        self.assertFalse('Places205' in DATASETS.module_dict)
-        register_all_modules(init_default_scope=True)
-        self.assertTrue('Places205' in DATASETS.module_dict)
-        self.assertEqual(DefaultScope.get_current_instance().scope_name,
-                         'mmselfsup')
-
-        # init default scope when another scope is init
-        name = f'test-{datetime.datetime.now()}'
-        DefaultScope.get_instance(name, scope_name='test')
-        with self.assertWarnsRegex(
-                Warning,
-                'The current default scope "test" is not "mmselfsup"'):
-            register_all_modules(init_default_scope=True)

tools/analysis_tools/browse_dataset.py

@@ -5,10 +5,10 @@ import os.path as osp
 import mmengine
 import numpy as np
 from mmengine import Config, DictAction
+from mmengine.registry import init_default_scope
 
 from mmselfsup.datasets.builder import build_dataset
 from mmselfsup.registry import VISUALIZERS
-from mmselfsup.utils import register_all_modules
 
 
 def parse_args():
@@ -41,12 +41,12 @@ def parse_args():
 
 def main():
     args = parse_args()
+
     cfg = Config.fromfile(args.config)
     if args.cfg_options is not None:
         cfg.merge_from_dict(args.cfg_options)
 
-    # register all modules in mmselfsup into the registries
-    register_all_modules()
+    init_default_scope(cfg.get('default_scope', 'mmselfsup'))
 
     dataset = build_dataset(cfg.train_dataloader.dataset)
 

tools/analysis_tools/visualize_reconstruction.py

@@ -11,7 +11,6 @@ import torch
 from mmengine.dataset import Compose, default_collate
 
 from mmselfsup.apis import inference_model, init_model
-from mmselfsup.utils import register_all_modules
 
 imagenet_mean = np.array([0.485, 0.456, 0.406])
 imagenet_std = np.array([0.229, 0.224, 0.225])
@@ -115,8 +114,6 @@ def main():
         help='The random seed for visualization')
     args = parser.parse_args()
 
-    register_all_modules()
-
     # build the model from a config file and a checkpoint file
     model = init_model(args.config, args.checkpoint, device=args.device)
     print('Model loaded.')

tools/analysis_tools/visualize_tsne.py

@@ -14,13 +14,13 @@ from mmengine.config import Config, DictAction
 from mmengine.dataset import default_collate, worker_init_fn
 from mmengine.dist import get_rank
 from mmengine.logging import MMLogger
+from mmengine.registry import init_default_scope
 from mmengine.utils import mkdir_or_exist
 from sklearn.manifold import TSNE
 from torch.utils.data import DataLoader
 
 from mmselfsup.apis import init_model
 from mmselfsup.registry import DATA_SAMPLERS, DATASETS
-from mmselfsup.utils import register_all_modules
 
 
 def parse_args():
@@ -99,19 +99,15 @@ def parse_args():
     return args
 
 
-def post_process():
-    pass
-
-
 def main():
     args = parse_args()
 
-    # register all modules in mmselfsup into the registries
-    register_all_modules()
-
     cfg = Config.fromfile(args.config)
     if args.cfg_options is not None:
         cfg.merge_from_dict(args.cfg_options)
+
+    init_default_scope(cfg.get('default_scope', 'mmselfsup'))
+
     # set cudnn_benchmark
     if cfg.get('cudnn_benchmark', False):
         torch.backends.cudnn.benchmark = True

tools/benchmarks/classification/knn_imagenet/test_knn.py

@@ -10,13 +10,13 @@ from mmengine.config import Config, DictAction
 from mmengine.dist import get_rank, init_dist
 from mmengine.logging import MMLogger
 from mmengine.model.wrappers import MMDistributedDataParallel, is_model_wrapper
+from mmengine.registry import init_default_scope
 from mmengine.runner import Runner, load_checkpoint
 from mmengine.utils import mkdir_or_exist
 
 from mmselfsup.evaluation.functional import knn_eval
 from mmselfsup.models.utils import Extractor
 from mmselfsup.registry import MODELS
-from mmselfsup.utils import register_all_modules
 
 
 def parse_args():
@@ -72,14 +72,13 @@ def parse_args():
 def main():
     args = parse_args()
 
-    # register all modules in mmselfsup into the registries
-    register_all_modules()
-
     # load config
     cfg = Config.fromfile(args.config)
     if args.cfg_options is not None:
         cfg.merge_from_dict(args.cfg_options)
 
+    init_default_scope(cfg.get('default_scope', 'mmselfsup'))
+
     # set cudnn_benchmark
     if cfg.env_cfg.get('cudnn_benchmark', False):
         torch.backends.cudnn.benchmark = True

tools/benchmarks/classification/svm_voc07/extract.py

@@ -13,13 +13,13 @@ from mmengine.dataset 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.registry import init_default_scope
 from mmengine.runner import load_checkpoint
 from mmengine.utils import mkdir_or_exist
 from torch.utils.data import DataLoader
 
 from mmselfsup.models.utils import Extractor
 from mmselfsup.registry import DATA_SAMPLERS, DATASETS, MODELS
-from mmselfsup.utils import register_all_modules
 
 
 def parse_args():
@@ -66,14 +66,13 @@ def parse_args():
 def main():
     args = parse_args()
 
-    # register all modules in mmselfsup into the registries
-    register_all_modules()
-
     # load config
     cfg = Config.fromfile(args.config)
     if args.cfg_options is not None:
         cfg.merge_from_dict(args.cfg_options)
 
+    init_default_scope(cfg.get('default_scope', 'mmselfsup'))
+
     # set cudnn_benchmark
     if cfg.env_cfg.get('cudnn_benchmark', False):
         torch.backends.cudnn.benchmark = True

tools/train.py

@@ -6,8 +6,6 @@ import os.path as osp
 from mmengine.config import Config, DictAction
 from mmengine.runner import Runner
 
-from mmselfsup.utils import register_all_modules
-
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Train a model')
@@ -51,10 +49,6 @@ def parse_args():
 def main():
     args = parse_args()
 
-    # register all modules in mmselfsup into the registries
-    # do not init the default scope here because it will be init in the runner
-    register_all_modules(init_default_scope=False)
-
     # load config
     cfg = Config.fromfile(args.config)
     cfg.launcher = args.launcher