.github/ISSUE_TEMPLATE/1-bug-report.yml

@@ -19,8 +19,8 @@ body:
       label: Branch
       description: Which branch/version are you using?
       options:
-        - main branch (1.x version)
+        - master branch (0.x version, such as `v0.10.0`, or `dev` branch)
-        - 0.x branch
+        - 1.x branch (1.x version, such as `v1.0.0rc2`, or `dev-1.x` branch)
     validations:
       required: true
 

docs/en/get_started.md

@@ -62,7 +62,7 @@ We recommend users to follow our best practices to install MMSelfSup. However, t
 ```shell
 pip install -U openmim
 mim install mmengine
-mim install 'mmcv>=2.0.0'
+mim install 'mmcv>=2.0.0rc1'
 ```
 
 **Step 1.** Install MMSelfSup.
@@ -79,18 +79,25 @@ In this case, install mmselfsup from source:
 ```shell
 git clone https://github.com/open-mmlab/mmselfsup.git
 cd mmselfsup
+git checkout 1.x
 pip install -v -e .
 # "-v" means verbose, or more output
 # "-e" means installing a project in editable mode,
 # thus any local modifications made to the code will take effect without reinstallation.
 ```
 
+Optionally, if you want to [contribute](https://github.com/open-mmlab/mmselfsup/blob/main/docs/en/notes/contribution_guide.md) to MMSelfSup or experience experimental functions, please checkout to the `dev-1.x` branch:
+
+```shell
+git checkout dev-1.x
+```
+
 #### Install as a Python package
 
 Just install with pip.
 
 ```shell
-pip install 'mmselfsup>=1.0.0'
+pip install 'mmselfsup>=1.0.0rc0'
 ```
 
 ### Verify the installation

docs/zh_cn/get_started.md

@@ -62,7 +62,7 @@ conda install pytorch torchvision cpuonly -c pytorch
 ```shell
 pip install -U openmim
 mim install mmengine
-mim install 'mmcv>=2.0.0'
+mim install 'mmcv>=2.0.0rc1'
 ```
 
 **步骤 1.** 安装 MMSelfSup。
@@ -79,18 +79,25 @@ mim install 'mmcv>=2.0.0'
 ```shell
 git clone https://github.com/open-mmlab/mmselfsup.git
 cd mmselfsup
+git checkout 1.x
 pip install -v -e .
 # "-v" 表示详细，或更多输出
 # "-e" 表示以可编辑模式安装项目，
 # 因此，对代码所做的任何本地修改都将生效，无需重新安装。
 ```
 
+或者，如果您想为 MMSelfSup 做出[贡献](https://github.com/open-mmlab/mmselfsup/blob/main/docs/zh_cn/notes/contribution_guides.md)或体验其正在实验中的功能，请查看 `dev-1.x` 分支：
+
+```shell
+git checkout dev-1.x
+```
+
 #### 作为 Python 包安装
 
 直接用 pip 安装：
 
 ```shell
-pip install 'mmselfsup>=1.0.0'
+pip install 'mmselfsup>=1.0.0rc0'
 ```
 
 ### 验证安装

mmselfsup/registry.py

@@ -55,14 +55,15 @@ __all__ = [
 # Registries For Runner and the related
 # manage all kinds of runners like `EpochBasedRunner` and `IterBasedRunner`
 RUNNERS = Registry(
-    'runner', parent=MMENGINE_RUNNERS, locations=['mmselfsup.engine'])
+    '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,
-    locations=['mmselfsup.engine'])
+    locations=['mmselfsup.engine.runner'])
 # manage all kinds of loops like `EpochBasedTrainLoop`
-LOOPS = Registry('loop', parent=MMENGINE_LOOPS, locations=['mmselfsup.engine'])
+LOOPS = Registry(
+    'loop', parent=MMENGINE_LOOPS, locations=['mmselfsup.engine.runner'])
 # manage all kinds of hooks like `CheckpointHook`
 HOOKS = Registry(
     'hook', parent=MMENGINE_HOOKS, locations=['mmselfsup.engine.hooks'])

requirements/tests.txt

@@ -1,5 +1,5 @@
 asynctest
-coverage
+codecov
 flake8
 interrogate
 isort==5.10.1

tests/test_models/test_algorithms/test_beitv1.py

@@ -9,7 +9,7 @@ from mmselfsup.models import BEiT
 from mmselfsup.structures import SelfSupDataSample
 
 data_preprocessor = dict(
-    type='mmselfsup.TwoNormDataPreprocessor',
+    type='TwoNormDataPreprocessor',
     mean=(123.675, 116.28, 103.53),
     std=(58.395, 57.12, 57.375),
     second_mean=(-20.4, -20.4, -20.4),

tests/test_models/test_algorithms/test_beitv2.py

@@ -9,7 +9,7 @@ from mmselfsup.models import BEiT
 from mmselfsup.structures import SelfSupDataSample
 
 data_preprocessor = dict(
-    type='mmselfsup.TwoNormDataPreprocessor',
+    type='TwoNormDataPreprocessor',
     mean=(123.675, 116.28, 103.53),
     std=(58.395, 57.12, 57.375),
     second_mean=(127.5, 127.5, 127.5),