Add documentations for registry (#656)

* add documentations for registry

* update example

* update sphinx

* add intro

* resolve comments

docs/index.rst

@@ -13,6 +13,7 @@ Contents
    visualization.md
    utils.md
    runner.md
+   registry.md
    cnn.md
    ops.md
    trouble_shooting.md

docs/registry.md

+## Registry
+
+MMCV implements [registry](https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/registry.py) to manage different modules that share similar functionalities, e.g., backbones, head, and necks, in detectors.
+Most projects in OpenMMLab use registry to manage modules of datasets and models, such as [MMDetection](https://github.com/open-mmlab/mmdetection), [MMDetection3D](https://github.com/open-mmlab/mmdetection3d), [MMClassification](https://github.com/open-mmlab/mmclassification), [MMEditing](https://github.com/open-mmlab/mmediting), etc.
+
+### What is registry
+
+In MMCV, registry can be regarded as a mapping that maps a class to a string.
+These classes contained by a single registry usually have similar APIs but implement different algorithms or support different datasets.
+With the registry, users can find and instantiate the class through its corresponding string, and use the instantiated module as they want.
+One typical example is the config systems in most OpenMMLab projects, which use the registry to create hooks, runners, models, and datasets, through configs.
+
+To manage your modules in the codebase by `Registry`, there are three steps as below.
+
+1. Create an registry
+2. Create a build method
+3. Use this registry to manage the modules
+
+### A Simple Example
+
+Here we show a simple example of using registry to manage modules in a package.
+You can find more practical examples in OpenMMLab projects.
+
+Assuming we want to implement a series of Dataset Converter for converting different formats of data to the expected data format.
+We create directory as a package named `converters`.
+In the package, we first create a file to implement builders, named `converters/builder.py`, as below
+
+```python
+from mmcv.utils import Registry
+
+# create a registry for converters
+CONVERTERS = Registry('converter')
+
+
+# create a build function
+def build_converter(cfg, *args, **kwargs):
+    cfg_ = cfg.copy()
+    converter_type = cfg_.pop('type')
+    if converter_type not in CONVERTERS:
+        raise KeyError(f'Unrecognized task type {converter_type}')
+    else:
+        converter_cls = CONVERTERS.get(converter_type)
+
+    converter = converter_cls(*args, **kwargs, **cfg_)
+    return converter
+```
+
+Then we can implement different converters in the package. For example, implement `Converter1` in `converters/converter1.py`
+
+```python
+
+from .builder import CONVERTERS
+
+
+# use the registry to namge the module
+@CONVERTERS.register_module()
+class Converter1(object):
+    def __init__(self, a, b):
+        self.a = a
+        self.b = b
+```
+
+The key step to use registry for managing the modules is to register the implemented module into the registry `CONVERTERS` through
+`@CONVERTERS.register_module()` when you are creating the module.
+If the module is successfully registered, you can use this converter through configs as
+
+```python
+converter_cfg = dict(type='Converter1', a=a_value, b=b_value)
+converter = build_converter(converter_cfg)
+```