[Feature] Support auto import modules from registry. (#2481)

## Motivation

The registry now supports auto-import modules from the given location.

register_all_modules before running is no longer needed. The modules
will be lazy-imported during building.

- [x] This PR can be merged after
https://github.com/open-mmlab/mmengine/pull/643. The MMEngine version
should be updated.

Ref: https://github.com/open-mmlab/mmdetection/pull/9143

configs/_base_/models/fpn_poolformer_s12.py

@@ -1,7 +1,10 @@
 # model settings
 norm_cfg = dict(type='SyncBN', requires_grad=True)
 checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth'  # noqa
-custom_imports = dict(imports='mmcls.models', allow_failed_imports=False)
+# TODO: delete custom_imports after mmcls supports auto import
+# please install mmcls>=1.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
 data_preprocessor = dict(
     type='SegDataPreProcessor',
     mean=[123.675, 116.28, 103.53],

configs/mask2former/mask2former_r50_8xb2-90k_cityscapes-512x1024.py

@@ -1,7 +1,5 @@
 _base_ = ['../_base_/default_runtime.py', '../_base_/datasets/cityscapes.py']
 
-custom_imports = dict(imports='mmdet.models', allow_failed_imports=False)
-
 crop_size = (512, 1024)
 data_preprocessor = dict(
     type='SegDataPreProcessor',

configs/mask2former/mask2former_swin-b-in1k-384x384-pre_8xb2-160k_ade20k-640x640.py

@@ -3,7 +3,6 @@ _base_ = [
 ]
 
 pretrained = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/swin/swin_base_patch4_window12_384_20220317-55b0104a.pth'  # noqa
-custom_imports = dict(imports='mmdet.models', allow_failed_imports=False)
 
 crop_size = (640, 640)
 data_preprocessor = dict(

demo/MMSegmentation_Tutorial.ipynb

@@ -460,12 +460,8 @@
    "outputs": [],
    "source": [
     "from mmengine.runner import Runner\n",
-    "from mmseg.utils import register_all_modules\n",
     "\n",
-    "# register all modules in mmseg 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",
-    "runner = Runner.from_cfg(cfg)\n"
+    "runner = Runner.from_cfg(cfg)"
    ]
   },
   {
@@ -523,7 +519,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.8.5 ('tensorflow')",
+   "display_name": "Python 3.10.6 ('pt1.12')",
    "language": "python",
    "name": "python3"
   },
@@ -537,7 +533,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.6"
   },
   "pycharm": {
    "stem_cell": {
@@ -550,7 +546,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "20d4b83e0c8b3730b580c42434163d64f4b735d580303a8fade7c849d4d29eba"
+    "hash": "0442e67aee3d9cbb788fa6e86d60c4ffa94ad7f1943c65abfecb99a6f4696c58"
    }
   }
  },

demo/image_demo.py

@@ -4,7 +4,6 @@ from argparse import ArgumentParser
 from mmengine.model import revert_sync_batchnorm
 
 from mmseg.apis import inference_model, init_model, show_result_pyplot
-from mmseg.utils import register_all_modules
 
 
 def main():
@@ -24,8 +23,6 @@ def main():
         '--title', default='result', help='The image identifier.')
     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)
     if args.device == 'cpu':

demo/video_demo.py

@@ -6,7 +6,6 @@ from mmengine.model.utils import revert_sync_batchnorm
 
 from mmseg.apis import inference_model, init_model
 from mmseg.apis.inference import show_result_pyplot
-from mmseg.utils import register_all_modules
 
 
 def main():
@@ -53,8 +52,6 @@ def main():
     assert args.show or args.output_file, \
         'At least one output should be enabled.'
 
-    register_all_modules()
-
     # build the model from a config file and a checkpoint file
     model = init_model(args.config, args.checkpoint, device=args.device)
     if args.device == 'cpu':

docs/en/advanced_guides/datasets.md

@@ -15,8 +15,8 @@ Instantiate Cityscapes training dataset:
 
 ```python
 from mmseg.datasets import CityscapesDataset
-from mmseg.utils import register_all_modules
-register_all_modules()
+from mmengine.registry import init_default_scope
+init_default_scope('mmseg')
 
 data_root = 'data/cityscapes/'
 data_prefix=dict(img_path='leftImg8bit/train', seg_map_path='gtFine/train')

docs/en/get_started.md

@@ -91,10 +91,8 @@ Option (b). If you install mmsegmentation with pip, open you python interpreter
 
 ```python
 from mmseg.apis import inference_model, init_model, show_result_pyplot
-from mmseg.utils import register_all_modules
 import mmcv
 
-register_all_modules()
 config_file = 'pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
 checkpoint_file = 'pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
 

docs/en/user_guides/3_inference.md

@@ -31,14 +31,10 @@ Example:
 
 ```python
 from mmseg.apis import init_model
-from mmseg.utils import register_all_modules
 
 config_path = 'configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
 checkpoint_path = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
 
-# register all modules in mmseg into the registries
-register_all_modules()
-
 # initialize model without checkpoint
 model = init_model(config_path)
 
@@ -76,14 +72,11 @@ Example:
 
 ```python
 from mmseg.apis import init_model, inference_model
-from mmseg.utils import register_all_modules
 
 config_path = 'configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
 checkpoint_path = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
 img_path = 'demo/demo.png'
 
-# register all modules in mmseg into the registries
-register_all_modules()
 
 model = init_model(config_path, checkpoint_path)
 result = inference_model(model, img_path)
@@ -115,14 +108,11 @@ Example:
 
 ```python
 from mmseg.apis import init_model, inference_model, show_result_pyplot
-from mmseg.utils import register_all_modules
 
 config_path = 'configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
 checkpoint_path = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
 img_path = 'demo/demo.png'
 
-# register all modules in mmseg into the registries
-register_all_modules()
 
 # build the model from a config file and a checkpoint file
 model = init_model(config_path, checkpoint_path, device='cuda:0')

docs/zh_cn/advanced_guides/datasets.md

@@ -9,9 +9,10 @@
 实例化 Cityscapes 训练数据集:
 
 ```python
+from mmengine.registry import init_default_scope
 from mmseg.datasets import CityscapesDataset
-from mmseg.utils import register_all_modules
-register_all_modules()
+
+init_default_scope('mmseg')
 
 data_root = 'data/cityscapes/'
 data_prefix=dict(img_path='leftImg8bit/train', seg_map_path='gtFine/train')

docs/zh_cn/get_started.md

@@ -92,10 +92,8 @@ python demo/image_demo.py demo/demo.png configs/pspnet/pspnet_r50-d8_4xb2-40k_ci
 
 ```python
 from mmseg.apis import inference_model, init_model, show_result_pyplot
-from mmseg.utils import register_all_modules
 import mmcv
 
-register_all_modules()
 config_file = 'pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py'
 checkpoint_file = 'pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
 

mmseg/__init__.py

@@ -9,7 +9,7 @@ from .version import __version__, version_info
 
 MMCV_MIN = '2.0.0rc4'
 MMCV_MAX = '2.1.0'
-MMENGINE_MIN = '0.2.0'
+MMENGINE_MIN = '0.4.0'
 MMENGINE_MAX = '1.0.0'
 
 

mmseg/apis/inference.py

@@ -9,6 +9,7 @@ import numpy as np
 import torch
 from mmengine import Config
 from mmengine.dataset import Compose
+from mmengine.registry import init_default_scope
 from mmengine.runner import load_checkpoint
 from mmengine.utils import mkdir_or_exist
 
@@ -48,6 +49,8 @@ def init_model(config: Union[str, Path, Config],
         config.model.backbone.init_cfg = None
     config.model.pretrained = None
     config.model.train_cfg = None
+    init_default_scope(config.get('default_scope', 'mmseg'))
+
     model = MODELS.build(config.model)
     if checkpoint is not None:
         checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')

mmseg/registry/__init__.py

@@ -1,13 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .registry import (DATA_SAMPLERS, DATASETS, HOOKS, LOOPS, METRICS,
-                       MODEL_WRAPPERS, MODELS, OPTIM_WRAPPER_CONSTRUCTORS,
-                       OPTIMIZERS, PARAM_SCHEDULERS, RUNNER_CONSTRUCTORS,
-                       RUNNERS, TASK_UTILS, TRANSFORMS, VISBACKENDS,
-                       VISUALIZERS, WEIGHT_INITIALIZERS)
+from .registry import (DATA_SAMPLERS, DATASETS, EVALUATOR, HOOKS, INFERENCERS,
+                       LOG_PROCESSORS, LOOPS, METRICS, MODEL_WRAPPERS, MODELS,
+                       OPTIM_WRAPPER_CONSTRUCTORS, OPTIM_WRAPPERS, OPTIMIZERS,
+                       PARAM_SCHEDULERS, RUNNER_CONSTRUCTORS, RUNNERS,
+                       TASK_UTILS, TRANSFORMS, VISBACKENDS, VISUALIZERS,
+                       WEIGHT_INITIALIZERS)
 
 __all__ = [
-    'RUNNERS', 'RUNNER_CONSTRUCTORS', 'HOOKS', 'DATASETS', 'DATA_SAMPLERS',
-    'TRANSFORMS', 'MODELS', 'WEIGHT_INITIALIZERS', 'OPTIMIZERS',
-    'OPTIM_WRAPPER_CONSTRUCTORS', 'TASK_UTILS', 'PARAM_SCHEDULERS', 'METRICS',
-    'MODEL_WRAPPERS', 'LOOPS', 'VISBACKENDS', 'VISUALIZERS'
+    'HOOKS', 'DATASETS', 'DATA_SAMPLERS', 'TRANSFORMS', 'MODELS',
+    'WEIGHT_INITIALIZERS', 'OPTIMIZERS', 'OPTIM_WRAPPER_CONSTRUCTORS',
+    'TASK_UTILS', 'PARAM_SCHEDULERS', 'METRICS', 'MODEL_WRAPPERS',
+    'VISBACKENDS', 'VISUALIZERS', 'RUNNERS', 'RUNNER_CONSTRUCTORS', 'LOOPS',
+    'EVALUATOR', 'LOG_PROCESSORS', 'OPTIM_WRAPPERS', 'INFERENCERS'
 ]

mmseg/registry/registry.py

@@ -10,6 +10,7 @@ from mmengine.registry import DATA_SAMPLERS as MMENGINE_DATA_SAMPLERS
 from mmengine.registry import DATASETS as MMENGINE_DATASETS
 from mmengine.registry import EVALUATOR as MMENGINE_EVALUATOR
 from mmengine.registry import HOOKS as MMENGINE_HOOKS
+from mmengine.registry import INFERENCERS as MMENGINE_INFERENCERS
 from mmengine.registry import LOG_PROCESSORS as MMENGINE_LOG_PROCESSORS
 from mmengine.registry import LOOPS as MMENGINE_LOOPS
 from mmengine.registry import METRICS as MMENGINE_METRICS
@@ -39,45 +40,82 @@ RUNNER_CONSTRUCTORS = Registry(
 # manage all kinds of loops like `EpochBasedTrainLoop`
 LOOPS = Registry('loop', parent=MMENGINE_LOOPS)
 # manage all kinds of hooks like `CheckpointHook`
-HOOKS = Registry('hook', parent=MMENGINE_HOOKS)
+HOOKS = Registry(
+    'hook', parent=MMENGINE_HOOKS, locations=['mmseg.engine.hooks'])
 
 # 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=['mmseg.datasets'])
+DATA_SAMPLERS = Registry(
+    'data sampler',
+    parent=MMENGINE_DATA_SAMPLERS,
+    locations=['mmseg.datasets.samplers'])
+TRANSFORMS = Registry(
+    'transform',
+    parent=MMENGINE_TRANSFORMS,
+    locations=['mmseg.datasets.transforms'])
 
 # mangage all kinds of modules inheriting `nn.Module`
-MODELS = Registry('model', parent=MMENGINE_MODELS)
+MODELS = Registry('model', parent=MMENGINE_MODELS, locations=['mmseg.models'])
 # mangage 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=['mmseg.models'])
 # mangage all kinds of weight initialization modules like `Uniform`
 WEIGHT_INITIALIZERS = Registry(
-    'weight initializer', parent=MMENGINE_WEIGHT_INITIALIZERS)
+    'weight initializer',
+    parent=MMENGINE_WEIGHT_INITIALIZERS,
+    locations=['mmseg.models'])
 
 # mangage all kinds of optimizers like `SGD` and `Adam`
-OPTIMIZERS = Registry('optimizer', parent=MMENGINE_OPTIMIZERS)
+OPTIMIZERS = Registry(
+    'optimizer',
+    parent=MMENGINE_OPTIMIZERS,
+    locations=['mmseg.engine.optimizers'])
 # manage optimizer wrapper
-OPTIM_WRAPPERS = Registry('optim_wrapper', parent=MMENGINE_OPTIM_WRAPPERS)
+OPTIM_WRAPPERS = Registry(
+    'optim_wrapper',
+    parent=MMENGINE_OPTIM_WRAPPERS,
+    locations=['mmseg.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=['mmseg.engine.optimizers'])
 # mangage all kinds of parameter schedulers like `MultiStepLR`
 PARAM_SCHEDULERS = Registry(
-    'parameter scheduler', parent=MMENGINE_PARAM_SCHEDULERS)
+    'parameter scheduler',
+    parent=MMENGINE_PARAM_SCHEDULERS,
+    locations=['mmseg.engine.schedulers'])
 
 # manage all kinds of metrics
-METRICS = Registry('metric', parent=MMENGINE_METRICS)
+METRICS = Registry(
+    'metric', parent=MMENGINE_METRICS, locations=['mmseg.evaluation'])
 # manage evaluator
-EVALUATOR = Registry('evaluator', parent=MMENGINE_EVALUATOR)
+EVALUATOR = Registry(
+    'evaluator', parent=MMENGINE_EVALUATOR, locations=['mmseg.evaluation'])
 
 # manage task-specific modules like ohem pixel sampler
-TASK_UTILS = Registry('task util', parent=MMENGINE_TASK_UTILS)
+TASK_UTILS = Registry(
+    'task util', parent=MMENGINE_TASK_UTILS, locations=['mmseg.models'])
 
 # manage visualizer
-VISUALIZERS = Registry('visualizer', parent=MMENGINE_VISUALIZERS)
+VISUALIZERS = Registry(
+    'visualizer',
+    parent=MMENGINE_VISUALIZERS,
+    locations=['mmseg.visualization'])
 # manage visualizer backend
-VISBACKENDS = Registry('vis_backend', parent=MMENGINE_VISBACKENDS)
+VISBACKENDS = Registry(
+    'vis_backend',
+    parent=MMENGINE_VISBACKENDS,
+    locations=['mmseg.visualization'])
 
 # manage logprocessor
-LOG_PROCESSORS = Registry('log_processor', parent=MMENGINE_LOG_PROCESSORS)
+LOG_PROCESSORS = Registry(
+    'log_processor',
+    parent=MMENGINE_LOG_PROCESSORS,
+    locations=['mmseg.visualization'])
+
+# manage inferencer
+INFERENCERS = Registry('inferencer', parent=MMENGINE_INFERENCERS)

requirements/mminstall.txt

@@ -1,4 +1,4 @@
 mmcls>=1.0.0rc0
 mmcv>=2.0.0rc4
 -e git+https://github.com/open-mmlab/mmdetection.git@dev-3.x#egg=mmdet
-mmengine>=0.2.0,<1.0.0
+mmengine>=0.4.0,<1.0.0

requirements/readthedocs.txt

@@ -1,5 +1,5 @@
 mmcv>=2.0.0rc1,<2.1.0
-mmengine>=0.1.0,<1.0.0
+mmengine>=0.4.0,<1.0.0
 prettytable
 scipy
 torch

tests/test_config.py

@@ -6,10 +6,10 @@ from os.path import dirname, exists, isdir, join, relpath
 import numpy as np
 from mmengine import Config
 from mmengine.dataset import Compose
+from mmengine.registry import init_default_scope
 from torch import nn
 
 from mmseg.models import build_segmentor
-from mmseg.utils import register_all_modules
 
 
 def _get_config_directory():
@@ -70,7 +70,7 @@ def test_config_data_pipeline():
         xdoctest -m tests/test_config.py test_config_build_data_pipeline
     """
 
-    register_all_modules()
+    init_default_scope('mmseg')
     config_dpath = _get_config_directory()
     print(f'Found config_dpath = {config_dpath!r}')
 

tests/test_datasets/test_dataset_builder.py

@@ -2,12 +2,12 @@
 import os.path as osp
 
 from mmengine.dataset import ConcatDataset, RepeatDataset
+from mmengine.registry import init_default_scope
 
 from mmseg.datasets import MultiImageMixDataset
 from mmseg.registry import DATASETS
-from mmseg.utils import register_all_modules
 
-register_all_modules()
+init_default_scope('mmseg')
 
 
 @DATASETS.register_module()

tests/test_datasets/test_transform.py

@@ -5,6 +5,7 @@ import os.path as osp
 import mmcv
 import numpy as np
 import pytest
+from mmengine.registry import init_default_scope
 from PIL import Image
 
 from mmseg.datasets.transforms import *  # noqa
@@ -12,9 +13,8 @@ from mmseg.datasets.transforms import (LoadBiomedicalData,
                                        LoadBiomedicalImageFromFile,
                                        PhotoMetricDistortion, RandomCrop)
 from mmseg.registry import TRANSFORMS
-from mmseg.utils import register_all_modules
 
-register_all_modules()
+init_default_scope('mmseg')
 
 
 def test_resize():

tests/test_engine/test_layer_decay_optimizer_constructor.py

@@ -5,12 +5,12 @@ import torch
 import torch.nn as nn
 from mmcv.cnn import ConvModule
 from mmengine.optim.optimizer import build_optim_wrapper
+from mmengine.registry import init_default_scope
 
 from mmseg.engine.optimizers.layer_decay_optimizer_constructor import \
     LearningRateDecayOptimizerConstructor
-from mmseg.utils import register_all_modules
 
-register_all_modules()
+init_default_scope('mmseg')
 
 base_lr = 1
 decay_rate = 2

tests/test_models/test_backbones/test_unet.py

@@ -2,14 +2,14 @@
 import pytest
 import torch
 from mmcv.cnn import ConvModule
+from mmengine.registry import init_default_scope
 
 from mmseg.models.backbones.unet import (BasicConvBlock, DeconvModule,
                                          InterpConv, UNet, UpConvBlock)
 from mmseg.models.utils import Upsample
-from mmseg.utils import register_all_modules
 from .utils import check_norm_state
 
-register_all_modules()
+init_default_scope('mmseg')
 
 
 def test_unet_basic_conv_block():

tests/test_models/test_forward.py

@@ -9,14 +9,14 @@ import pytest
 import torch
 import torch.nn as nn
 from mmengine.model.utils import revert_sync_batchnorm
+from mmengine.registry import init_default_scope
 from mmengine.structures import PixelData
 from mmengine.utils import is_list_of, is_tuple_of
 from torch import Tensor
 
 from mmseg.structures import SegDataSample
-from mmseg.utils import register_all_modules
 
-register_all_modules()
+init_default_scope('mmseg')
 
 
 def _demo_mm_inputs(batch_size=2, image_shapes=(3, 32, 32), num_classes=5):

tests/test_models/test_heads/test_maskformer_head.py

@@ -3,15 +3,15 @@ from os.path import dirname, join
 
 import torch
 from mmengine import Config
+from mmengine.registry import init_default_scope
 from mmengine.structures import PixelData
 
 from mmseg.registry import MODELS
 from mmseg.structures import SegDataSample
-from mmseg.utils import register_all_modules
 
 
 def test_maskformer_head():
-    register_all_modules()
+    init_default_scope('mmseg')
     repo_dpath = dirname(dirname(__file__))
     cfg = Config.fromfile(
         join(

tests/test_models/test_segmentors/test_seg_tta_model.py

@@ -2,14 +2,14 @@
 import torch
 from mmengine import ConfigDict
 from mmengine.model import BaseTTAModel
+from mmengine.registry import init_default_scope
 from mmengine.structures import PixelData
 
 from mmseg.registry import MODELS
 from mmseg.structures import SegDataSample
-from mmseg.utils import register_all_modules
 from .utils import *  # noqa: F401,F403
 
-register_all_modules()
+init_default_scope('mmseg')
 
 
 def test_encoder_decoder_tta():

tools/analysis_tools/benchmark.py

@@ -8,11 +8,11 @@ import torch
 from mmengine import Config
 from mmengine.fileio import dump
 from mmengine.model.utils import revert_sync_batchnorm
+from mmengine.registry import init_default_scope
 from mmengine.runner import Runner, load_checkpoint
 from mmengine.utils import mkdir_or_exist
 
 from mmseg.registry import MODELS
-from mmseg.utils import register_all_modules
 
 
 def parse_args():
@@ -32,8 +32,10 @@ def parse_args():
 
 def main():
     args = parse_args()
-    register_all_modules()
     cfg = Config.fromfile(args.config)
+
+    init_default_scope(cfg.get('default_scope', 'mmseg'))
+
     timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
     if args.work_dir is not None:
         mkdir_or_exist(osp.abspath(args.work_dir))

tools/misc/browse_dataset.py

@@ -3,10 +3,10 @@ import argparse
 import os.path as osp
 
 from mmengine import Config, DictAction
+from mmengine.registry import init_default_scope
 from mmengine.utils import ProgressBar
 
 from mmseg.registry import DATASETS, VISUALIZERS
-from mmseg.utils import register_all_modules
 
 
 def parse_args():
@@ -44,7 +44,7 @@ def main():
         cfg.merge_from_dict(args.cfg_options)
 
     # register all modules in mmseg into the registries
-    register_all_modules()
+    init_default_scope('mmseg')
 
     dataset = DATASETS.build(cfg.train_dataloader.dataset)
     cfg.visualizer['save_dir'] = args.output_dir

tools/test.py

@@ -6,8 +6,6 @@ import os.path as osp
 from mmengine.config import Config, DictAction
 from mmengine.runner import Runner
 
-from mmseg.utils import register_all_modules
-
 
 # TODO: support fuse_conv_bn, visualization, and format_only
 def parse_args():
@@ -77,10 +75,6 @@ def trigger_visualization_hook(cfg, args):
 def main():
     args = parse_args()
 
-    # register all modules in mmseg 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

tools/train.py

@@ -6,10 +6,9 @@ import os.path as osp
 
 from mmengine.config import Config, DictAction
 from mmengine.logging import print_log
-from mmengine.registry import RUNNERS
 from mmengine.runner import Runner
 
-from mmseg.utils import register_all_modules
+from mmseg.registry import RUNNERS
 
 
 def parse_args():
@@ -52,10 +51,6 @@ def parse_args():
 def main():
     args = parse_args()
 
-    # register all modules in mmseg 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