add model registry

docs/registry.md

@@ -9,40 +9,29 @@ 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.
+The API reference could be find [here](https://mmcv.readthedocs.io/en/latest/api.html?highlight=registry#mmcv.utils.Registry).
 
 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
+1. Create a build method (optional, in most cases you can just use the default one).
+2. Create a registry.
+3. Use this registry to manage the modules.
+
+`build_func` argument of `Registry` is to customize how to instantiate the class instance, the default one is `build_from_cfg` implemented [here](https://mmcv.readthedocs.io/en/latest/api.html?highlight=registry#mmcv.utils.build_from_cfg).
 
 ### 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`.
+We create a 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`
@@ -51,7 +40,6 @@ Then we can implement different converters in the package. For example, implemen
 
 from .builder import CONVERTERS
 
-
 # use the registry to manage the module
 @CONVERTERS.register_module()
 class Converter1(object):
@@ -71,5 +59,95 @@ If the module is successfully registered, you can use this converter through con
 
 ```python
 converter_cfg = dict(type='Converter1', a=a_value, b=b_value)
-converter = build_converter(converter_cfg)
+converter = CONVERTERS.build(converter_cfg)
+```
+
+## Customize Build Function
+
+Suppose we would like to customize how `converters` are built, we could implement a customized `build_func` and pass it into the registry.
+
+```python
+from mmcv.utils import Registry
+
+# create a build function
+def build_converter(cfg, registry, *args, **kwargs):
+    cfg_ = cfg.copy()
+    converter_type = cfg_.pop('type')
+    if converter_type not in registry:
+        raise KeyError(f'Unrecognized converter type {converter_type}')
+    else:
+        converter_cls = registry.get(converter_type)
+
+    converter = converter_cls(*args, **kwargs, **cfg_)
+    return converter
+
+# create a registry for converters and pass ``build_converter`` function
+CONVERTERS = Registry('converter', build_func=build_converter)
 ```
+
+Note: in this example, we demonstrate how to use the `build_func` argument to customize the way to build a class instance.
+The functionality is similar to the default `build_from_cfg`. In most cases, default one would be sufficient.
+`build_model_from_cfg` is also implemented to build PyTorch module in `nn.Sequentail`, you may directly use them instead of implementing by yourself.
+
+## Hierarchy Registry
+
+You could also build modules from more than one OpenMMLab frameworks, e.g. you could use all backbones in [MMClassification](https://github.com/open-mmlab/mmclassification) for object detectors in [MMDetection](https://github.com/open-mmlab/mmdetection), you may also combine an object detection model in [MMDetection](https://github.com/open-mmlab/mmdetection) and semantic segmentation model in [MMSegmentation](https://github.com/open-mmlab/mmsegmentation).
+
+All `MODELS` registries of downstream codebases are children registries of MMCV's `MODELS` registry.
+Basically, there are two ways to build a module from child or sibling registries.
+
+1. Build from children registries.
+
+   For example:
+
+   In MMDetection we define:
+
+   ```python
+   from mmcv.utils import Registry
+   from mmcv.cnn import MODELS as MMCV_MODELS
+   MODELS = Registry('model', parent=MMCV_MODELS)
+
+   @MODELS.register_module()
+   class NetA(nn.Module):
+       def forward(self, x):
+           return x
+   ```
+
+   In MMClassification we define:
+
+   ```python
+   from mmcv.utils import Registry
+   from mmcv.cnn import MODELS as MMCV_MODELS
+   MODELS = Registry('model', parent=MMCV_MODELS)
+
+   @MODELS.register_module()
+   class NetB(nn.Module):
+       def forward(self, x):
+           return x + 1
+   ```
+
+   We could build two net in either MMDetection or MMClassification by:
+
+   ```python
+   from mmdet.models import MODELS
+   net_a = MODELS.build(cfg=dict(type='NetA'))
+   net_b = MODELS.build(cfg=dict(type='mmcls.NetB'))
+   ```
+
+   or
+
+   ```python
+   from mmcls.models import MODELS
+   net_a = MODELS.build(cfg=dict(type='mmdet.NetA'))
+   net_b = MODELS.build(cfg=dict(type='NetB'))
+   ```
+
+2. Build from parent registry.
+
+   The shared `MODELS` registry in MMCV is the parent registry for all downstream codebases (root registry):
+
+   ```python
+   from mmcv.cnn import MODELS as MMCV_MODELS
+   net_a = MMCV_MODELS.build(cfg=dict(type='mmdet.NetA'))
+   net_b = MMCV_MODELS.build(cfg=dict(type='mmcls.NetB'))
+   ```

mmcv/cnn/__init__.py

@@ -11,6 +11,7 @@
                      build_activation_layer, build_conv_layer,
                      build_norm_layer, build_padding_layer, build_plugin_layer,
                      build_upsample_layer, conv_ws_2d, is_norm)
+from .builder import MODELS, build_model_from_cfg
 # yapf: enable
 from .resnet import ResNet, make_res_layer
 from .utils import (INITIALIZERS, Caffe2XavierInit, ConstantInit, KaimingInit,
@@ -34,5 +35,5 @@
     'Linear', 'Conv2d', 'ConvTranspose2d', 'MaxPool2d', 'ConvTranspose3d',
     'MaxPool3d', 'Conv3d', 'initialize', 'INITIALIZERS', 'ConstantInit',
     'XavierInit', 'NormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
-    'Caffe2XavierInit'
+    'Caffe2XavierInit', 'MODELS', 'build_model_from_cfg'
 ]

mmcv/cnn/builder.py

@@ -0,0 +1,30 @@
+import torch.nn as nn
+
+from ..utils import Registry, build_from_cfg
+
+
+def build_model_from_cfg(cfg, registry, default_args=None):
+    """Build a PyTorch model from config dict(s). Different from
+    ``build_from_cfg``, if cfg is a list, a ``nn.Sequential`` will be built.
+
+    Args:
+        cfg (dict, list[dict]): The config of modules, is is either a config
+            dict or a list of config dicts. If cfg is a list, a
+            the built modules will be wrapped with ``nn.Sequential``.
+        registry (:obj:`Registry`): A registry the module belongs to.
+        default_args (dict, optional): Default arguments to build the module.
+            Defaults to None.
+
+    Returns:
+        nn.Module: A built nn module.
+    """
+    if isinstance(cfg, list):
+        modules = [
+            build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg
+        ]
+        return nn.Sequential(*modules)
+    else:
+        return build_from_cfg(cfg, registry, default_args)
+
+
+MODELS = Registry('model', build_func=build_model_from_cfg)