[Doc] Add custom metrics document (#2799)

docs/en/advanced_guides/add_metrics.md

 # Add New Metrics
+
+## Develop with the source code of MMSegmentation
+
+Here we show how to develop a new metric with an example of `CustomMetric` as the following.
+
+1. Create a new file `mmseg/evaluation/metrics/custom_metric.py`.
+
+   ```python
+   from typing import List, Sequence
+
+   from mmengine.evaluator import BaseMetric
+
+   from mmseg.registry import METRICS
+
+
+   @METRICS.register_module()
+   class CustomMetric(BaseMetric):
+
+       def __init__(self, arg1, arg2):
+           """
+           The metric first processes each batch of data_samples and predictions,
+           and appends the processed results to the results list. Then it
+           collects all results together from all ranks if distributed training
+           is used. Finally, it computes the metrics of the entire dataset.
+           """
+
+       def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:
+           pass
+
+       def compute_metrics(self, results: list) -> dict:
+           pass
+
+       def evaluate(self, size: int) -> dict:
+           pass
+   ```
+
+   In the above example, `CustomMetric` is a subclass of `BaseMetric`. It has three methods: `process`, `compute_metrics` and `evaluate`.
+
+   - `process()` process one batch of data samples and predictions. The processed results are stored in `self.results` which will be used to compute the metrics after all the data samples are processed. Please refer to [MMEngine documentation](https://github.com/open-mmlab/mmengine/blob/main/docs/en/design/evaluation.md) for more details.
+
+   - `compute_metrics()` is used to compute the metrics from the processed results.
+
+   - `evaluate()` is an interface to compute the metrics and return the results. It will be called by `ValLoop` or `TestLoop` in the `Runner`. In most cases, you don't need to override this method, but you can override it if you want to do some extra work.
+
+   **Note:** You might find the details of calling `evaluate()` method in the `Runner` [here](https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py#L366). The `Runner` is the executor of the training and testing process, you can find more details about it at the [engine document](./engine.md).
+
+2. Import the new metric in `mmseg/evaluation/metrics/__init__.py`.
+
+   ```python
+   from .custom_metric import CustomMetric
+   __all__ = ['CustomMetric', ...]
+   ```
+
+3. Add the new metric to the config file.
+
+   ```python
+   val_evaluator = dict(type='CustomMetric', arg1=xxx, arg2=xxx)
+   test_evaluator = dict(type='CustomMetric', arg1=xxx, arg2=xxx)
+   ```
+
+## Develop with the released version of MMSegmentation
+
+The above example shows how to develop a new metric with the source code of MMSegmentation. If you want to develop a new metric with the released version of MMSegmentation, you can follow the following steps.
+
+1. Create a new file `/Path/to/metrics/custom_metric.py`, implement the `process`, `compute_metrics` and `evaluate` methods, `evaluate` method is optional.
+
+2. Import the new metric in your code or config file.
+
+   ```python
+   from path.to.metrics import CustomMetric
+   ```
+
+   or
+
+   ```python
+   custom_imports = dict(imports=['/Path/to/metrics'], allow_failed_imports=False)
+
+   val_evaluator = dict(type='CustomMetric', arg1=xxx, arg2=xxx)
+   test_evaluator = dict(type='CustomMetric', arg1=xxx, arg2=xxx)
+   ```

docs/zh_cn/advanced_guides/add_metrics.md

-# 新增评测指标 (待更新)
+# 新增评测指标
+
+## 使用 MMSegmentation 的源代码进行开发
+
+在这里，我们用 `CustomMetric` 作为例子来展示如何开发一个新的评测指标。
+
+1. 创建一个新文件 `mmseg/evaluation/metrics/custom_metric.py`。
+
+   ```python
+   from typing import List, Sequence
+
+   from mmengine.evaluator import BaseMetric
+
+   from mmseg.registry import METRICS
+
+
+   @METRICS.register_module()
+   class CustomMetric(BaseMetric):
+
+       def __init__(self, arg1, arg2):
+           """
+           The metric first processes each batch of data_samples and predictions,
+           and appends the processed results to the results list. Then it
+           collects all results together from all ranks if distributed training
+           is used. Finally, it computes the metrics of the entire dataset.
+           """
+
+       def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:
+           pass
+
+       def compute_metrics(self, results: list) -> dict:
+           pass
+
+       def evaluate(self, size: int) -> dict:
+           pass
+   ```
+
+   在上面的示例中，`CustomMetric` 是 `BaseMetric` 的子类。它有三个方法：`process`，`compute_metrics` 和 `evaluate`。
+
+   - `process()` 处理一批数据样本和预测。处理后的结果需要显示地传给 `self.results` ，将在处理所有数据样本后用于计算指标。更多细节请参考 [MMEngine 文档](https://github.com/open-mmlab/mmengine/blob/main/docs/zh_cn/design/evaluation.md)
+
+   - `compute_metrics()` 用于从处理后的结果中计算指标。
+
+   - `evaluate()` 是一个接口，用于计算指标并返回结果。它将由 `ValLoop` 或 `TestLoop` 在 `Runner` 中调用。在大多数情况下，您不需要重写此方法，但如果您想做一些额外的工作，可以重写它。
+
+   **注意：** 您可以在[这里](https://github.com/open-mmlab/mmengine/blob/main/mmengine/runner/loops.py#L366) 找到 `Runner` 调用 `evaluate()` 方法的过程。`Runner` 是训练和测试过程的执行器，您可以在[训练引擎文档](./engine.md)中找到有关它的详细信息。
+
+2. 在 `mmseg/evaluation/metrics/__init__.py` 中导入新的指标。
+
+   ```python
+   from .custom_metric import CustomMetric
+   __all__ = ['CustomMetric', ...]
+   ```
+
+3. 在配置文件中设置新的评测指标
+
+   ```python
+   val_evaluator = dict(type='CustomMetric', arg1=xxx, arg2=xxx)
+   test_evaluator = dict(type='CustomMetric', arg1=xxx, arg2=xxx)
+   ```
+
+## 使用发布版本的 MMSegmentation 进行开发
+
+上面的示例展示了如何使用 MMSegmentation 的源代码开发新指标。如果您想使用 MMSegmentation 的发布版本开发新指标，可以按照以下步骤操作。
+
+1. 创建一个新文件 `/Path/to/metrics/custom_metric.py`，实现 `process`，`compute_metrics` 和 `evaluate` 方法，`evaluate` 方法是可选的。
+
+2. 在代码或配置文件中导入新的指标。
+
+   ```python
+   from path.to.metrics import CustomMetric
+   ```
+
+   或者
+
+   ```python
+   custom_imports = dict(imports=['/Path/to/metrics'], allow_failed_imports=False)
+
+   val_evaluator = dict(type='CustomMetric', arg1=xxx, arg2=xxx)
+   test_evaluator = dict(type='CustomMetric', arg1=xxx, arg2=xxx)
+   ```