[Doc] Add English version document of the Training Engine

[xiexinch]

Motivation

As title.

Modification

• docs/en/advanced_guides/engine.md

[csatsurnh]

Suggested change

	MMEngine defines some [basic loop controllers](https:/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py) such as epoch-based training loop (`EpochBasedTrainLoop`), iteration-based training loop (`IterBasedTrainLoop`), standard validation loop (`ValLoop`), and standard testing loop (`TestLoop`).
	MMEngine defined some [basic loop controllers](https:/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py) such as epoch-based training loop (`EpochBasedTrainLoop`), iteration-based training loop (`IterBasedTrainLoop`), standard validation loop (`ValLoop`), and standard testing loop (`TestLoop`).

[csatsurnh]

Suggested change

	OpenMMLab's algorithm libraries like MMSegmentation abstract model training, testing, and inference as executors (`Runner`) to handle. Users can use the default executor in MMEngine directly or modify the executor to meet customized needs. This document mainly introduces how users can configure existing running settings, hooks, and optimizers' basic concepts and usage methods.
	OpenMMLab's algorithm libraries like MMSegmentation abstract model training, testing, and inference as `Runner` to handle. Users can use the default runner in MMEngine directly or modify the runner to meet customized needs. This document mainly introduces how users can configure existing running settings, hooks, and optimizers' basic concepts and usage methods.

[csatsurnh]

Suggested change

	Loop controllers refer to the execution process during training, validation, and testing. They use `train_cfg`, `val_cfg`, and `test_cfg` to build these processes in the configuration file. MMSegmentation sets commonly used training lengths in the configs/_base_/schedules folder's train_cfg.
	Loop controllers refer to the execution process during training, validation, and testing. `train_cfg`, `val_cfg`, and `test_cfg` are used to build these processes in the configuration file. MMSegmentation sets commonly used training iterations in `train_cfg` under the `configs/_base_/schedules` folder.

[csatsurnh]

Suggested change

	The priority of custom hooks needs to be set in the configuration file; if not set, it will be set to `NORMAL` by default. The following are some custom hooks implemented in MMEngine:
	The priority of custom hooks needs to be set in the configuration file; if not, it will be set to `NORMAL` by default. The following are some custom hooks implemented in MMEngine:

[csatsurnh]

Suggested change

	MMSegmentation implements [`SegVisualizationHook`](https:/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/engine/hooks/visualization_hook.py#L17), which is used to visualize prediction results during validation and testing.
	MMSegmentation implemented [`SegVisualizationHook`](https:/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/engine/hooks/visualization_hook.py#L17), which is used to visualize prediction results during validation and testing.

[csatsurnh]

Suggested change

	## Configuring Running Settings
	## Configuring Runtime Settings

[csatsurnh]

Suggested change

	In the previous configuration and runtime settings, we provided a simple example of configuring the training optimizer. This section will further detail how to configure optimizers in MMSegmentation.
	In the previous configuration and runtime settings, we provided a simple example of configuring the training optimizer. This section will further detailly introduce how to configure optimizers in MMSegmentation.

[csatsurnh]

Suggested change

The default optimizer wrapper constructor [`DefaultOptimWrapperConstructor`](https:/open-mmlab/mmengine/blob/main/mmengine/optim/optimizer/default_constructor.py#L19) builds the optimizer used in training based on the input `optim_wrapper` and the `paramwise_cfg` defined in the `optim_wrapper`. When the functionality of [`DefaultOptimWrapperConstructor`](https:/open-mmlab/mmengine/blob/main/mmengine/optim/optimizer/default_constructor.py#L19) does not meet the requirements, you can customize the optimizer wrapper constructor to implement the configuration of hyperparameters.

The default optimizer wrapper constructor [`DefaultOptimWrapperConstructor`](https:/open-mmlab/mmengine/blob/main/mmengine/optim/optimizer/default_constructor.py#L19) builds the optimizer used in training based on the input `optim_wrapper` and `paramwise_cfg` defined in the `optim_wrapper`. When the functionality of [`DefaultOptimWrapperConstructor`](https:/open-mmlab/mmengine/blob/main/mmengine/optim/optimizer/default_constructor.py#L19) does not meet the requirements, you can customize the optimizer wrapper constructor to implement the configuration of hyperparameters.