[Docs] Resolve warnings in sphinx build (#915)

* add ZeroOptimizer to optim

* resolve `duplicate label` warnings

* upgrade docutils && shpinx to resolve `unknown directive or role` warnings

* fix typo

* resolve literal_block && heading warnings

* resolve json literal_block warnings

* resolve python literal_block warnings

* resolve bunches of reference warnings

* resolve bunches of docstring warnings

* resolve warnings in autosummary

* resolve remaining warnings in en docs

* resolve heading warnings in zh_cn docs

* resolve remaining warnings in zh_cn docs

* fix as comments

* fix as comments

Author: C1rN09

docs/en/advanced_tutorials/basedataset.md

@@ -19,24 +19,20 @@ Here is an example of a JSON annotation file (where each raw data info contains
 ```json
 
 {
-    'metainfo':
+    "metainfo":
         {
-            'classes': ('cat', 'dog'),
-            ...
+            "classes": ["cat", "dog"]
         },
-    'data_list':
+    "data_list":
         [
             {
-                'img_path': "xxx/xxx_0.jpg",
-                'img_label': 0,
-                ...
+                "img_path": "xxx/xxx_0.jpg",
+                "img_label": 0
             },
             {
-                'img_path': "xxx/xxx_1.jpg",
-                'img_label': 1,
-                ...
-            },
-            ...
+                "img_path": "xxx/xxx_1.jpg",
+                "img_label": 1
+            }
         ]
 }
 ```

docs/en/advanced_tutorials/config.md

@@ -352,15 +352,16 @@ optimizer:
 
 `resnet50_dump.json`
 
-````json
+```json
 {"optimizer": {"type": "SGD", "lr": 0.02, "momentum": 0.9, "weight_decay": 0.0001}, "model": {"type": "ResNet", "depth": 50}}
+```
 
 In addition, `dump` can also dump `cfg` loaded from a dictionary.
 
 ```python
 cfg = Config(dict(a=1, b=2))
 cfg.dump('dump_dict.py')
-````
+```
 
 `dump_dict.py`
 

docs/en/advanced_tutorials/data_transform.md

@@ -15,7 +15,7 @@ processed data as a dictionary. A simple example is as belows:
 ```{note}
 In MMEngine, we don't have the implementations of data transforms. you can find the base data transform class
 and many other data transforms in MMCV. So you need to install MMCV before learning this tutorial, see the
-{external+mmcv:doc}`MMCV installation guild <get_started/installation>`.
+{external+mmcv:doc}`MMCV installation guide <get_started/installation>`.
 ```
 
 ```python

docs/en/advanced_tutorials/logging.md

@@ -4,7 +4,7 @@
 
 ## Flexible Logging System
 
-Logging system is configured by passing a [LogProcessor](mmengine.logging.LogProcessor) to the runner. If no log processor is passed, the runner will use the default log processor, which is equivalent to:
+Logging system is configured by passing a [LogProcessor](mmengine.runner.LogProcessor) to the runner. If no log processor is passed, the runner will use the default log processor, which is equivalent to:
 
 ```python
 log_processor = dict(window_size=10, by_epoch=True, custom_cfg=None, num_digits=4)
@@ -70,7 +70,7 @@ The significant digits(`num_digits`) of the log is 4 by default.
 Output the value of all custom logs at the last iteration by default.
 ```
 
-```{warnning}
+```{warning}
 log_processor outputs the epoch based log by default(`by_epoch=True`). To get an expected log matched with the `train_cfg`, we should set the same value for `by_epoch` in `train_cfg` and `log_processor`.
 ```
 
@@ -191,7 +191,7 @@ runner.train()
 08/21 03:17:26 - mmengine - INFO - Epoch(train) [1][20/25]  lr: 1.0000e-02  eta: 0:00:00  time: 0.0024  data_time: 0.0010  loss1: 0.5464  loss2: 0.7251  loss: 1.2715  loss1_local_max: 2.8872  loss1_global_max: 2.8872
 ```
 
-More examples can be found in [log_processor](mmengine.logging.LogProcessor).
+More examples can be found in [log_processor](mmengine.runner.LogProcessor).
 
 ## Customize log
 

docs/en/advanced_tutorials/registry.md

@@ -1,6 +1,6 @@
 # Registry
 
-OpenMMLab supports a rich collection of algorithms and datasets, therefore, many modules with similar functionality are implemented. For example, the implementations of `ResNet` and `SE-ResNet` are based on the classes `ResNet` and `SEResNet`, respectively, which have similar functions and interfaces and belong to the model components of the algorithm library. To manage these functionally similar modules, MMEngine implements the [registry](mmengine.registry.registry). Most of the algorithm libraries in OpenMMLab use `registry` to manage their modules, including [MMDetection](https://github.com/open-mmlab/mmdetection), [MMDetection3D](https://github.com/open-mmlab/mmdetection3d), [MMClassification](https://github.com/open-mmlab/mmclassification) and [MMEditing](https://github.com/open-mmlab/mmediting), etc.
+OpenMMLab supports a rich collection of algorithms and datasets, therefore, many modules with similar functionality are implemented. For example, the implementations of `ResNet` and `SE-ResNet` are based on the classes `ResNet` and `SEResNet`, respectively, which have similar functions and interfaces and belong to the model components of the algorithm library. To manage these functionally similar modules, MMEngine implements the [registry](mmengine.registry.Registry). Most of the algorithm libraries in OpenMMLab use `registry` to manage their modules, including [MMDetection](https://github.com/open-mmlab/mmdetection), [MMDetection3D](https://github.com/open-mmlab/mmdetection3d), [MMClassification](https://github.com/open-mmlab/mmclassification) and [MMEditing](https://github.com/open-mmlab/mmediting), etc.
 
 ## What is a registry
 

docs/en/advanced_tutorials/test_time_augmentation.md

@@ -103,20 +103,20 @@ The following diagram illustrates this sequence of method calls:
 After data augmentation with TestTimeAug, the resulting data will have the following format:
 
 ```python
-image1  = dict(
+image1 = dict(
     inputs=[data_1_1, data_1_2],
-    data_sample=[data_sample1_1, data_sample1_2])
-）
+    data_sample=[data_sample1_1, data_sample1_2]
+)
 
-image2  = dict(
+image2 = dict(
     inputs=[data_2_1, data_2_2],
-    data_sample=[data_sample2_1, data_sample2_2])
-）
+    data_sample=[data_sample2_1, data_sample2_2]
+)
 
-image3  = dict(
+image3 = dict(
     inputs=[data_3_1, data_3_2],
-    data_sample=[data_sample3_1, data_sample3_2])
-）
+    data_sample=[data_sample3_1, data_sample3_2]
+)
 ```
 
 where `data_{i}_{j}` means the enhanced data，and `data_sample_{i}_{j}` means the ground truth of enhanced data. Then the data will be processed by `Dataloader`, which contributes to the following format:

docs/en/advanced_tutorials/visualization.md

@@ -102,7 +102,7 @@ def draw_featmap(
     # the layout when multiple channels are expanded into multiple images
     arrangement: Tuple[int, int] = (5, 2),
     # scale the feature map
-    resize_shape：Optional[tuple] = None,
+    resize_shape: Optional[tuple] = None,
     # overlay ratio between input image and generated feature map
     alpha: float = 0.5,
 ) -> np.ndarray:

docs/en/conf.py

@@ -44,7 +44,6 @@
     'sphinx.ext.intersphinx',
     'sphinx.ext.napoleon',
     'sphinx.ext.viewcode',
-    'sphinx.ext.autosectionlabel',
     'myst_parser',
     'sphinx_copybutton',
     'sphinx.ext.autodoc.typehints',

docs/en/design/hook.md

@@ -201,4 +201,4 @@ There are 22 mount points in the [Base Hook](mmengine.hooks.Hook).
 - before_save_checkpoint
 - after_load_checkpoint
 
-Further readings: [Hook tutorial](../tutorials/hook.md) and [Hook API documentations](mmengine.hooks)
+Further readings: [Hook tutorial](../tutorials/hook.md) and [Hook API documentations](../api/hooks)

docs/en/design/logging.md

@@ -10,7 +10,7 @@
 
 ![image](https://user-images.githubusercontent.com/57566630/163441489-47999f3a-3259-44ab-949c-77a8a599faa5.png)
 
-Each scalar (losses, learning rates, etc.) during training is encapsulated by HistoryBuffer, managed by MessageHub in key-value pairs, formatted by LogProcessor and then exported to various visualization backends by [LoggerHook](mmengine.hook.LoggerHook). **In most cases, statistical methods of these scalars can be configured through the LogProcessor without understanding the data flow.**  Before diving into the design of the logging system, please read through [logging tutorial](../advanced_tutorials/logging.md) first for familiarizing basic use cases.
+Each scalar (losses, learning rates, etc.) during training is encapsulated by HistoryBuffer, managed by MessageHub in key-value pairs, formatted by LogProcessor and then exported to various visualization backends by [LoggerHook](mmengine.hooks.LoggerHook). **In most cases, statistical methods of these scalars can be configured through the LogProcessor without understanding the data flow.**  Before diving into the design of the logging system, please read through [logging tutorial](../advanced_tutorials/logging.md) first for familiarizing basic use cases.
 
 ## HistoryBuffer