[Docs] Refactor the structure of documentation (#1580)

* [Docs] Refactor the structure of documentation

* [Docs] Refactor the structure of documentation

* fix symlink

* fix link

* fix typo

* polish docstring

* fix docstring

.github/workflows/build.yml

@@ -6,7 +6,6 @@ on:
       - 'README.md'
       - 'README_zh-CN.md'
       - 'docs/**'
-      - 'docs_zh_CN/**'
       - 'examples/**'
       - '.dev_scripts/**'
 
@@ -15,7 +14,6 @@ on:
       - 'README.md'
       - 'README_zh-CN.md'
       - 'docs/**'
-      - 'docs_zh_CN/**'
       - 'examples/**'
       - '.dev_scripts/**'
 

.gitignore

@@ -67,8 +67,8 @@ instance/
 .scrapy
 
 # Sphinx documentation
-docs/_build/
-docs_zh_CN/_build/
+docs/en/_build/
+docs/zh_cn/_build/
 
 # PyBuilder
 target/

README.md

@@ -1,5 +1,5 @@
 <div align="center">
-    <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/mmcv-logo.png" width="300"/>
+    <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/en/mmcv-logo.png" width="300"/>
 </div>
 
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmcv)](https://pypi.org/project/mmcv/) [![PyPI](https://img.shields.io/pypi/v/mmcv)](https://pypi.org/project/mmcv) [![badge](https://github.com/open-mmlab/mmcv/workflows/build/badge.svg)](https://github.com/open-mmlab/mmcv/actions) [![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv) [![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
@@ -173,7 +173,7 @@ For more details, please refer the the following tables.
   </tbody>
 </table>
 
-**Note**: The pre-built packages provided above do not include all versions of mmcv-full, you can click on the corresponding links to see the supported versions. For example, you can click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html) and you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full. In addition, We no longer provide `mmcv-full` pre-built packages compiled with `PyTorch 1.3 & 1.4` since v1.3.17. You can find previous versions that compiled with PyTorch 1.3 & 1.4 [here](./docs/get_started/previous_versions.md). The compatibility is still ensured in our CI, but we will discard the support of PyTorch 1.3 & 1.4 next year.
+**Note**: The pre-built packages provided above do not include all versions of mmcv-full, you can click on the corresponding links to see the supported versions. For example, you can click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html) and you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full. In addition, We no longer provide `mmcv-full` pre-built packages compiled with `PyTorch 1.3 & 1.4` since v1.3.17. You can find previous versions that compiled with PyTorch 1.3 & 1.4 [here](./docs/en/get_started/previous_versions.md). The compatibility is still ensured in our CI, but we will discard the support of PyTorch 1.3 & 1.4 next year.
 
 Another way is to compile locally by running
 
@@ -191,7 +191,7 @@ pip install mmcv
 
 c. Install full version with custom operators for onnxruntime
 
-- Check [here](docs/deployment/onnxruntime_op.md) for detailed instruction.
+- Check [here](docs/en/deployment/onnxruntime_op.md) for detailed instruction.
 
 If you would like to build MMCV from source, please refer to the [guide](https://mmcv.readthedocs.io/en/latest/get_started/build.html).
 

README_zh-CN.md

@@ -1,5 +1,5 @@
 <div align="center">
-    <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/mmcv-logo.png" width="300"/>
+    <img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/en/mmcv-logo.png" width="300"/>
 </div>
 
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmcv)](https://pypi.org/project/mmcv/) [![PyPI](https://img.shields.io/pypi/v/mmcv)](https://pypi.org/project/mmcv) [![badge](https://github.com/open-mmlab/mmcv/workflows/build/badge.svg)](https://github.com/open-mmlab/mmcv/actions) [![codecov](https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmcv) [![license](https://img.shields.io/github/license/open-mmlab/mmcv.svg)](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
@@ -35,7 +35,7 @@ MMCV 提供了如下众多功能：
 - 多种 CNN 网络结构
 - 高质量实现的常见 CUDA 算子
 
-如想了解更多特性和使用，请参考[文档](http://mmcv.readthedocs.io/en/latest)。
+如想了解更多特性和使用，请参考[文档](http://mmcv.readthedocs.io/zh_CN/latest)。
 
 提示: MMCV 需要 Python 3.6 以上版本。
 
@@ -50,7 +50,7 @@ MMCV 有两个版本：
 
 a. 安装完整版
 
-在安装 mmcv-full 之前，请确保 PyTorch 已经成功安装在环境中，可以参考 PyTorch 官方[文档](https://pytorch.org/)。
+在安装 mmcv-full 之前，请确保 PyTorch 已经成功安装在环境中，可以参考 PyTorch [官方文档](https://pytorch.org/)。
 
 我们提供了不同 PyTorch 和 CUDA 版本的 mmcv-full 预编译包，可以大大简化用户安装编译过程。强烈推荐通过预编译包来安装。另外，安装完成后可以运行 [check_installation.py](.dev_scripts/check_installation.py) 脚本检查 mmcv-full 是否安装成功。
 
@@ -170,7 +170,7 @@ pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/t
   </tbody>
 </table>
 
-**注意**：以上提供的预编译包并不囊括所有的 mmcv-full 版本，你可以点击对应链接查看支持的版本。例如，点击 [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html)，可以看到 `cu102-torch1.8.0` 只提供了 1.3.0 及以上的 mmcv-full 版本。另外，从 `mmcv v1.3.17` 开始，我们不再提供`PyTorch 1.3 & 1.4` 对应的 mmcv-full 预编译包。你可以在 [这](./docs_zh_CN/get_started/previous_versions.md) 找到 `PyTorch 1.3 & 1.4` 对应的预编包。虽然我们不再提供 `PyTorch 1.3 & 1.4` 对应的预编译包，但是我们依然在 CI 中保证对它们的兼容持续到下一年。
+**注意**：以上提供的预编译包并不囊括所有的 mmcv-full 版本，你可以点击对应链接查看支持的版本。例如，点击 [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html)，可以看到 `cu102-torch1.8.0` 只提供了 1.3.0 及以上的 mmcv-full 版本。另外，从 `mmcv v1.3.17` 开始，我们不再提供`PyTorch 1.3 & 1.4` 对应的 mmcv-full 预编译包。你可以在 [这](./docs/zh_cn/get_started/previous_versions.md) 找到 `PyTorch 1.3 & 1.4` 对应的预编包。虽然我们不再提供 `PyTorch 1.3 & 1.4` 对应的预编译包，但是我们依然在 CI 中保证对它们的兼容持续到下一年。
 
 除了使用预编译包之外，另一种方式是在本地进行编译，直接运行下述命令
 
@@ -188,13 +188,13 @@ pip install mmcv
 
 c. 安装完整版并且编译 onnxruntime 的自定义算子
 
-- 详细的指南请查看 [这里](docs/deployment/onnxruntime_op.md)。
+- 详细的指南请查看[这里](docs/zh_cn/deployment/onnxruntime_op.md)。
 
-如果想从源码编译 MMCV，请参考[该文档](https://mmcv.readthedocs.io/en/latest/get_started/build.html)。
+如果想从源码编译 MMCV，请参考[该文档](https://mmcv.readthedocs.io/zh_CN/latest/get_started/build.html)。
 
 ## FAQ
 
-如果你遇到了安装问题，CUDA 相关的问题或者 RuntimeErrors，可以首先参考[问题解决页面](https://mmcv.readthedocs.io/en/latest/faq.html) 看是否已经有解决方案。
+如果你遇到了安装问题，CUDA 相关的问题或者 RuntimeErrors，可以首先参考[问题解决页面](https://mmcv.readthedocs.io/zh_CN/latest/faq.html) 看是否已经有解决方案。
 
 ## 贡献指南
 
@@ -208,7 +208,7 @@ c. 安装完整版并且编译 onnxruntime 的自定义算子
 扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=GJP18SjI)
 
 <div align="center">
-<img src="docs/_static/zhihu_qrcode.jpg" height="400" />  <img src="docs/_static/qq_group_qrcode.jpg" height="400" />
+<img src="docs/en/_static/zhihu_qrcode.jpg" height="400" />  <img src="docs/en/_static/qq_group_qrcode.jpg" height="400" />
 </div>
 
 我们会在 OpenMMLab 社区为大家

docs/community/contributing.md

@@ -1 +0,0 @@
-../../CONTRIBUTING.md

docs/en/api.rst

@@ -38,6 +38,11 @@ runner
 .. automodule:: mmcv.runner
     :members:
 
+engine
+------
+.. automodule:: mmcv.engine
+    :members:
+
 ops
 ------
 .. automodule:: mmcv.ops

docs/en/community/contributing.md

@@ -0,0 +1 @@
+../../../CONTRIBUTING.md

docs/en/conf.py

@@ -17,9 +17,9 @@ import sys
 import pytorch_sphinx_theme
 from sphinx.builders.html import StandaloneHTMLBuilder
 
-sys.path.insert(0, os.path.abspath('..'))
+sys.path.insert(0, os.path.abspath('../..'))
 
-version_file = '../mmcv/version.py'
+version_file = '../../mmcv/version.py'
 with open(version_file, 'r') as f:
     exec(compile(f.read(), version_file, 'exec'))
 __version__ = locals()['__version__']

docs/en/get_started/installation.md

@@ -138,7 +138,7 @@ For more details, please refer the the following tables.
 </table>
 
 ```{note}
-The pre-built packages provided above do not include all versions of mmcv-full, you can click on the corresponding links to see the supported versions. For example, if you click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html), you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full. In addition, We no longer provide `mmcv-full` pre-built packages compiled with `PyTorch 1.3 & 1.4` since v1.3.17. You can find previous versions that compiled with PyTorch 1.3 & 1.4 [here](./docs/get_started/previous_versions.md). The compatibility is still ensured in our CI, but we will discard the support of PyTorch 1.3 & 1.4 next year.
+The pre-built packages provided above do not include all versions of mmcv-full, you can click on the corresponding links to see the supported versions. For example, if you click [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html), you can see that `cu102-torch1.8.0` only provides 1.3.0 and above versions of mmcv-full. In addition, We no longer provide `mmcv-full` pre-built packages compiled with `PyTorch 1.3 & 1.4` since v1.3.17. You can find previous versions that compiled with PyTorch 1.3 & 1.4 [here](./previous_versions.md). The compatibility is still ensured in our CI, but we will discard the support of PyTorch 1.3 & 1.4 next year.
 ```
 
 Another way is to compile locally by running

docs/zh_cn/api.rst

@@ -38,6 +38,11 @@ runner
 .. automodule:: mmcv.runner
     :members:
 
+engine
+------
+.. automodule:: mmcv.engine
+    :members:
+
 ops
 ------
 .. automodule:: mmcv.ops

docs/zh_cn/community/pr.md

@@ -24,7 +24,7 @@
 + 当你第一次提 PR 时
 
   复刻 OpenMMLab 原代码库，点击 GitHub 页面右上角的 **Fork** 按钮即可
-    ![avatar](../../docs/_static/community/1.png)
+    ![avatar](../../en/_static/community/1.png)
 
   克隆复刻的代码库到本地
 
@@ -73,14 +73,14 @@ git commit -m 'messages'
     ```
 
 + 创建一个`拉取请求`
-![avatar](../../docs/_static/community/2.png)
+![avatar](../../en/_static/community/2.png)
 
 + 修改`拉取请求`信息模板，描述修改原因和修改内容。还可以在 PR 描述中，手动关联到相关的`议题` (issue),（更多细节，请参考[官方文档](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue)）。
 
 #### 5. 讨论并评审你的代码
 
 + 创建`拉取请求`时，可以关联给相关人员进行评审
-![avatar](../../docs/_static/community/3.png)
+![avatar](../../en/_static/community/3.png)
 
 + 根据评审人员的意见修改代码，并推送修改
 

docs/zh_cn/conf.py

@@ -17,9 +17,9 @@ import sys
 import pytorch_sphinx_theme
 from sphinx.builders.html import StandaloneHTMLBuilder
 
-sys.path.insert(0, os.path.abspath('..'))
+sys.path.insert(0, os.path.abspath('../..'))
 
-version_file = '../mmcv/version.py'
+version_file = '../../mmcv/version.py'
 with open(version_file, 'r') as f:
     exec(compile(f.read(), version_file, 'exec'))
 __version__ = locals()['__version__']

docs/zh_cn/get_started/installation.md

@@ -134,7 +134,7 @@ pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/t
 </table>
 
 ```{note}
-以上提供的预编译包并不囊括所有的 mmcv-full 版本，我们可以点击对应链接查看支持的版本。例如，点击 [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html)，可以看到 `cu102-torch1.8.0` 只提供了 1.3.0 及以上的 mmcv-full 版本。另外，从 `mmcv v1.3.17` 开始，我们不再提供`PyTorch 1.3 & 1.4` 对应的 mmcv-full 预编译包。你可以在 [这](./docs_zh_CN/get_started/previous_versions.md) 找到 `PyTorch 1.3 & 1.4` 对应的预编包。虽然我们不再提供 `PyTorch 1.3 & 1.4` 对应的预编译包，但是我们依然在 CI 中保证对它们的兼容持续到下一年。
+以上提供的预编译包并不囊括所有的 mmcv-full 版本，我们可以点击对应链接查看支持的版本。例如，点击 [cu102-torch1.8.0](https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html)，可以看到 `cu102-torch1.8.0` 只提供了 1.3.0 及以上的 mmcv-full 版本。另外，从 `mmcv v1.3.17` 开始，我们不再提供`PyTorch 1.3 & 1.4` 对应的 mmcv-full 预编译包。你可以在 [这](./previous_versions.md) 找到 `PyTorch 1.3 & 1.4` 对应的预编包。虽然我们不再提供 `PyTorch 1.3 & 1.4` 对应的预编译包，但是我们依然在 CI 中保证对它们的兼容持续到下一年。
 ```
 
 除了使用预编译包之外，另一种方式是在本地进行编译，直接运行下述命令

docs/zh_cn/understand_mmcv/data_process.md

@@ -252,9 +252,9 @@ flow = mmcv.flowread('compressed.jpg', quantize=True, concat_axis=1)
 mmcv.flowshow(flow)
 ```
 
-![progress](../../docs/_static/flow_visualization.png)
+![progress](../../en/_static/flow_visualization.png)
 
-3. 流变换
+1. 流变换
 
 ```python
 img1 = mmcv.imread('img1.jpg')
@@ -264,12 +264,12 @@ warpped_img2 = mmcv.flow_warp(img1, flow)
 
 img1 (左) and img2 (右)
 
-![raw images](../../docs/_static/flow_raw_images.png)
+![raw images](../../en/_static/flow_raw_images.png)
 
 光流 (img2 -> img1)
 
-![optical flow](../../docs/_static/flow_img2toimg1.png)
+![optical flow](../../en/_static/flow_img2toimg1.png)
 
 变换后的图像和真实图像的差异
 
-![warpped image](../../docs/_static/flow_warp_diff.png)
+![warpped image](../../en/_static/flow_warp_diff.png)

docs/zh_cn/understand_mmcv/utils.md

@@ -17,7 +17,7 @@ mmcv.track_progress(func, tasks)
 ```
 
 效果如下
-![progress](../../docs/_static/progress.*)
+![progress](../../en/_static/progress.*)
 
 如果你想可视化多进程任务的进度，你可以使用 `track_parallel_progress` 。
 
@@ -25,7 +25,7 @@ mmcv.track_progress(func, tasks)
 mmcv.track_parallel_progress(func, tasks, 8)  # 8 workers
 ```
 
-![progress](../../docs/_static/parallel_progress.*)
+![progress](../../_static/parallel_progress.*)
 
 如果你想要迭代或枚举数据列表并可视化进度,你可以使用 `track_iter_progress` 。
 

mmcv/cnn/utils/weight_init.py

@@ -548,7 +548,7 @@ def _initialize_override(module, override, cfg):
 
 
 def initialize(module, init_cfg):
-    """Initialize a module.
+    r"""Initialize a module.
 
     Args:
         module (``torch.nn.Module``): the module will be initialized.
@@ -556,6 +556,7 @@ def initialize(module, init_cfg):
             define initializer. OpenMMLab has implemented 6 initializers
             including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
             ``Kaiming``, and ``Pretrained``.
+
     Example:
         >>> module = nn.Linear(2, 3, bias=True)
         >>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2)

mmcv/ops/nms.py

@@ -260,8 +260,9 @@ def soft_nms(boxes,
 def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False):
     r"""Performs non-maximum suppression in a batched fashion.
 
-    Modified from
-    https://github.com/pytorch/vision/blob/505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39.
+    Modified from `torchvision/ops/boxes.py#L39
+    <https://github.com/pytorch/vision/blob/
+    505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39>`_.
     In order to perform NMS independently per class, we add an offset to all
     the boxes. The offset is dependent only on the class idx, and is large
     enough so that boxes from different classes do not overlap.

mmcv/runner/__init__.py

@@ -14,10 +14,21 @@ from .hooks import (HOOKS, CheckpointHook, ClosureHook, DistEvalHook,
                     DistSamplerSeedHook, DvcliveLoggerHook, EMAHook, EvalHook,
                     Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook,
                     GradientCumulativeOptimizerHook, Hook, IterTimerHook,
-                    LoggerHook, LrUpdaterHook, MlflowLoggerHook,
-                    NeptuneLoggerHook, OptimizerHook, PaviLoggerHook,
-                    SyncBuffersHook, TensorboardLoggerHook, TextLoggerHook,
-                    WandbLoggerHook)
+                    LoggerHook, MlflowLoggerHook, NeptuneLoggerHook,
+                    OptimizerHook, PaviLoggerHook, SyncBuffersHook,
+                    TensorboardLoggerHook, TextLoggerHook, WandbLoggerHook)
+from .hooks.lr_updater import StepLrUpdaterHook  # noqa
+from .hooks.lr_updater import (CosineAnnealingLrUpdaterHook,
+                               CosineRestartLrUpdaterHook, CyclicLrUpdaterHook,
+                               ExpLrUpdaterHook, FixedLrUpdaterHook,
+                               FlatCosineAnnealingLrUpdaterHook,
+                               InvLrUpdaterHook, LrUpdaterHook,
+                               OneCycleLrUpdaterHook, PolyLrUpdaterHook)
+from .hooks.momentum_updater import (CosineAnnealingMomentumUpdaterHook,
+                                     CyclicMomentumUpdaterHook,
+                                     MomentumUpdaterHook,
+                                     OneCycleMomentumUpdaterHook,
+                                     StepMomentumUpdaterHook)
 from .iter_based_runner import IterBasedRunner, IterLoader
 from .log_buffer import LogBuffer
 from .optimizer import (OPTIMIZER_BUILDERS, OPTIMIZERS,
@@ -29,6 +40,12 @@ from .utils import get_host_info, get_time_str, obj_from_dict, set_random_seed
 __all__ = [
     'BaseRunner', 'Runner', 'EpochBasedRunner', 'IterBasedRunner', 'LogBuffer',
     'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook',
+    'FixedLrUpdaterHook', 'StepLrUpdaterHook', 'ExpLrUpdaterHook',
+    'PolyLrUpdaterHook', 'InvLrUpdaterHook', 'CosineAnnealingLrUpdaterHook',
+    'FlatCosineAnnealingLrUpdaterHook', 'CosineRestartLrUpdaterHook',
+    'CyclicLrUpdaterHook', 'OneCycleLrUpdaterHook', 'MomentumUpdaterHook',
+    'StepMomentumUpdaterHook', 'CosineAnnealingMomentumUpdaterHook',
+    'CyclicMomentumUpdaterHook', 'OneCycleMomentumUpdaterHook',
     'OptimizerHook', 'IterTimerHook', 'DistSamplerSeedHook', 'LoggerHook',
     'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook',
     'NeptuneLoggerHook', 'WandbLoggerHook', 'MlflowLoggerHook',

mmcv/runner/hooks/__init__.py

@@ -8,9 +8,17 @@ from .iter_timer import IterTimerHook
 from .logger import (DvcliveLoggerHook, LoggerHook, MlflowLoggerHook,
                      NeptuneLoggerHook, PaviLoggerHook, TensorboardLoggerHook,
                      TextLoggerHook, WandbLoggerHook)
-from .lr_updater import LrUpdaterHook
+from .lr_updater import (CosineAnnealingLrUpdaterHook,
+                         CosineRestartLrUpdaterHook, CyclicLrUpdaterHook,
+                         ExpLrUpdaterHook, FixedLrUpdaterHook,
+                         FlatCosineAnnealingLrUpdaterHook, InvLrUpdaterHook,
+                         LrUpdaterHook, OneCycleLrUpdaterHook,
+                         PolyLrUpdaterHook, StepLrUpdaterHook)
 from .memory import EmptyCacheHook
-from .momentum_updater import MomentumUpdaterHook
+from .momentum_updater import (CosineAnnealingMomentumUpdaterHook,
+                               CyclicMomentumUpdaterHook, MomentumUpdaterHook,
+                               OneCycleMomentumUpdaterHook,
+                               StepMomentumUpdaterHook)
 from .optimizer import (Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook,
                         GradientCumulativeOptimizerHook, OptimizerHook)
 from .profiler import ProfilerHook
@@ -19,11 +27,16 @@ from .sync_buffer import SyncBuffersHook
 
 __all__ = [
     'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook',
-    'OptimizerHook', 'Fp16OptimizerHook', 'IterTimerHook',
-    'DistSamplerSeedHook', 'EmptyCacheHook', 'LoggerHook', 'MlflowLoggerHook',
-    'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook',
-    'NeptuneLoggerHook', 'WandbLoggerHook', 'DvcliveLoggerHook',
-    'MomentumUpdaterHook', 'SyncBuffersHook', 'EMAHook', 'EvalHook',
-    'DistEvalHook', 'ProfilerHook', 'GradientCumulativeOptimizerHook',
-    'GradientCumulativeFp16OptimizerHook'
+    'FixedLrUpdaterHook', 'StepLrUpdaterHook', 'ExpLrUpdaterHook',
+    'PolyLrUpdaterHook', 'InvLrUpdaterHook', 'CosineAnnealingLrUpdaterHook',
+    'FlatCosineAnnealingLrUpdaterHook', 'CosineRestartLrUpdaterHook',
+    'CyclicLrUpdaterHook', 'OneCycleLrUpdaterHook', 'OptimizerHook',
+    'Fp16OptimizerHook', 'IterTimerHook', 'DistSamplerSeedHook',
+    'EmptyCacheHook', 'LoggerHook', 'MlflowLoggerHook', 'PaviLoggerHook',
+    'TextLoggerHook', 'TensorboardLoggerHook', 'NeptuneLoggerHook',
+    'WandbLoggerHook', 'DvcliveLoggerHook', 'MomentumUpdaterHook',
+    'StepMomentumUpdaterHook', 'CosineAnnealingMomentumUpdaterHook',
+    'CyclicMomentumUpdaterHook', 'OneCycleMomentumUpdaterHook',
+    'SyncBuffersHook', 'EMAHook', 'EvalHook', 'DistEvalHook', 'ProfilerHook',
+    'GradientCumulativeOptimizerHook', 'GradientCumulativeFp16OptimizerHook'
 ]

mmcv/runner/hooks/evaluation.py

@@ -65,7 +65,7 @@ class EvalHook(Hook):
         **eval_kwargs: Evaluation arguments fed into the evaluate function of
             the dataset.
 
-    Notes:
+    Note:
         If new arguments are added for EvalHook, tools/test.py,
         tools/eval_metric.py may be affected.
     """

mmcv/runner/hooks/momentum_updater.py

@@ -232,7 +232,7 @@ class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
     This momentum scheduler usually used together with the CyclicLRUpdater
     to improve the performance in the 3D detection area.
 
-    Attributes:
+    Args:
         target_ratio (tuple[float]): Relative ratio of the lowest momentum and
             the highest momentum to the initial momentum.
         cyclic_times (int): Number of cycles during training