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. Most of the algorithm libraries in OpenMMLab use registry to manage their modules, including MMDetection, MMDetection3D, MMClassification and MMEditing, etc.

What is a registry

The registry in MMEngine can be considered as a union of a mapping table and a build function of modules. The mapping table maintains a mapping from strings to classes or functions, allowing the user to find the corresponding class or function with its name/notation. For example, the mapping from the string "ResNet" to the ResNet class. The module build function defines how to find the corresponding class or function based on a string and how to instantiate the class or call the function. For example, finding nn.BatchNorm2d and instantiating the BatchNorm2d module by the string "bn", or finding the build_batchnorm2d function by the string "build_batchnorm2d" and then returning the result. The registries in MMEngine use the build_from_cfg function by default to find and instantiate the class or function corresponding to the string.

The classes or functions managed by a registry usually have similar interfaces and functionality, so the registry can be treated as an abstraction of those classes or functions. For example, the registry MODELS can be treated as an abstraction of all models, which manages classes such as ResNet, SEResNet and RegNetX and constructors such as build_ResNet, build_SEResNet and build_RegNetX.

Getting started

There are three steps required to use the registry to manage modules in the codebase.

  1. Create a registry.

  2. Create a build method for instantiating the class (optional because in most cases you can just use the default method).

  3. Add the module to the registry

Suppose we want to implement a series of activation modules and want to be able to switch to different modules by just modifying the configuration without modifying the code.

Let’s create a registry first.

from mmengine import Registry
# `scope` represents the domain of the registry. If not set, the default value is the package name.
# e.g. in mmdetection, the scope is mmdet
# `locations` indicates the location where the modules in this registry are defined.
# The Registry will automatically import the modules when building them according to these predefined locations.
ACTIVATION = Registry('activation', scope='mmengine', locations=['mmengine.models.activations'])

The module mmengine.models.activations specified by locations corresponds to the mmengine/models/ file. When building modules with registry, the ACTIVATION registry will automatically import implemented modules from this file. Therefore, we can implement different activation layers in the mmengine/models/ file, such as Sigmoid, ReLU, and Softmax.

import torch.nn as nn

# use the register_module
class Sigmoid(nn.Module):
    def __init__(self):

    def forward(self, x):
        print('call Sigmoid.forward')
        return x

class ReLU(nn.Module):
    def __init__(self, inplace=False):

    def forward(self, x):
        print('call ReLU.forward')
        return x

class Softmax(nn.Module):
    def __init__(self):

    def forward(self, x):
        print('call Softmax.forward')
        return x

The key of using the registry module is to register the implemented modules into the ACTIVATION registry. With the @ACTIVATION.register_module() decorator added before the implemented module, the mapping between strings and classes or functions can be built and maintained by ACTIVATION. We can achieve the same functionality with ACTIVATION.register_module(module=ReLU) as well.

By registering, we can create a mapping between strings and classes or functions via ACTIVATION.

# {
#     'Sigmoid': __main__.Sigmoid,
#     'ReLU': __main__.ReLU,
#     'Softmax': __main__.Softmax
# }


The key to trigger the registry mechanism is to make the module imported. There are three ways to register a module into the registry

  1. Implement the module in the locations. The registry will automatically import modules in the predefined locations. This is to ease the usage of algorithm libraries so that users can directly use

  2. Import the file manually. This is common when developers implement a new module in/out side the algorithm library.

  3. Use custom_imports field in config. Please refer to Importing custom Python modules for more details.

Once the implemented module is successfully registered, we can use the activation module in the configuration file.

import torch

input = torch.randn(2)

act_cfg = dict(type='Sigmoid')
activation =
output = activation(input)
# call Sigmoid.forward

We can switch to ReLU by just changing this configuration.

act_cfg = dict(type='ReLU', inplace=True)
activation =
output = activation(input)
# call ReLU.forward

If we want to check the type of input parameters (or any other operations) before creating an instance, we can implement a build method and pass it to the registry to implement a custom build process.

Create a build_activation function.

def build_activation(cfg, registry, *args, **kwargs):
    cfg_ = cfg.copy()
    act_type = cfg_.pop('type')
    print(f'build activation: {act_type}')
    act_cls = registry.get(act_type)
    act = act_cls(*args, **kwargs, **cfg_)
    return act

Pass the buid_activation to build_func.

ACTIVATION = Registry('activation', build_func=build_activation, scope='mmengine', locations=['mmengine.models.activations'])

class Tanh(nn.Module):
    def __init__(self):

    def forward(self, x):
        print('call Tanh.forward')
        return x

act_cfg = dict(type='Tanh')
activation =
output = activation(input)
# build activation: Tanh
# call Tanh.forward


In the above example, we demonstrate how to customize the method of building an instance of a class using the build_func. This is similar to the default build_from_cfg method. In most cases, using the default method will be fine.

MMEngine’s registry can register classes as well as functions.

FUNCTION = Registry('function', scope='mmengine')

def print_args(**kwargs):

func_cfg = dict(type='print_args', a=1, b=2)
func_res =

Advanced usage

The registry in MMEngine supports hierarchical registration, which enables cross-project calls, meaning that modules from one project can be used in another project. Though there are other ways to implement this, the registry provides a much easier solution.

To easily make cross-library calls, MMEngine provides twenty two root registries, including:

  • RUNNERS: the registry for Runner.

  • RUNNER_CONSTRUCTORS: the constructors for Runner.

  • LOOPS: manages training, validation and testing processes, such as EpochBasedTrainLoop.

  • HOOKS: the hooks, such as CheckpointHook, and ParamSchedulerHook.

  • DATASETS: the datasets.

  • DATA_SAMPLERS: Sampler of DataLoader, used to sample the data.

  • TRANSFORMS: various data preprocessing methods, such as Resize, and Reshape.

  • MODELS: various modules of the model.

  • MODEL_WRAPPERS: model wrappers for parallelizing distributed data, such as MMDistributedDataParallel.

  • WEIGHT_INITIALIZERS: the tools for weight initialization.

  • OPTIMIZERS: registers all Optimizers and custom Optimizers in PyTorch.

  • OPTIM_WRAPPER: the wrapper for Optimizer-related operations such as OptimWrapper, and AmpOptimWrapper.

  • OPTIM_WRAPPER_CONSTRUCTORS: the constructors for optimizer wrappers.

  • PARAM_SCHEDULERS: various parameter schedulers, such as MultiStepLR.

  • METRICS: the evaluation metrics for computing model accuracy, such as Accuracy.

  • EVALUATOR: one or more evaluation metrics used to calculate the model accuracy.

  • TASK_UTILS: the task-intensive components, such as AnchorGenerator, and BboxCoder.

  • VISUALIZERS: the management drawing module that draws prediction boxes on images, such as DetVisualizer.

  • VISBACKENDS: the backend for storing training logs, such as LocalVisBackend, and TensorboardVisBackend.

  • LOG_PROCESSORS: controls the log statistics window and statistics methods, by default we use LogProcessor. You may customize LogProcessor if you have special needs.

  • FUNCTIONS: registers various functions, such as collate_fn in DataLoader.

  • INFERENCERS: registers inferencers of different tasks, such as DetInferencer, which is used to perform inference on the detection task.

Use the module of the parent node

Let’s define a RReLU module in MMEngine and register it to the MODELS root registry.

import torch.nn as nn
from mmengine import Registry, MODELS

class RReLU(nn.Module):
    def __init__(self, lower=0.125, upper=0.333, inplace=False):

    def forward(self, x):
        print('call RReLU.forward')
        return x

Now suppose there is a project called MMAlpha, which also defines a MODELS and sets its parent node to the MODELS of MMEngine, which creates a hierarchical structure.

from mmengine import Registry, MODELS as MMENGINE_MODELS

MODELS = Registry('model', parent=MMENGINE_MODELS, scope='mmalpha', locations=['mmalpha.models'])

The following figure shows the hierarchy of MMEngine and MMAlpha.

The count_registered_modules function can be used to print the modules that have been registered to MMEngine and their hierarchy.

from mmengine.registry import count_registered_modules


We define a customized LogSoftmax module in MMAlpha and register it to the MODELS in MMAlpha.

class LogSoftmax(nn.Module):
    def __init__(self, dim=None):

    def forward(self, x):
        print('call LogSoftmax.forward')
        return x

Here we use the LogSoftmax in the configuration of MMAlpha.

model ='LogSoftmax'))

We can also use the modules of the parent node MMEngine here in the MMAlpha.

model ='RReLU', lower=0.2))
# scope is optional
model ='mmengine.RReLU'))

If no prefix is added, the build method will first find out if the module exists in the current node and return it if there is one. Otherwise, it will continue to look up the parent nodes or even the ancestor node until it finds the module. If the same module exists in both the current node and the parent nodes, we need to specify the scope prefix to indicate that we want to use the module of the parent nodes.

import torch

input = torch.randn(2)
output = model(input)
# call RReLU.forward

Use the module of a sibling node

In addition to using the module of the parent nodes, users can also call the module of a sibling node.

Suppose there is another project called MMBeta, which, like MMAlpha, defines MODELS and set its parent node to MMEngine.

from mmengine import Registry, MODELS as MMENGINE_MODELS

MODELS = Registry('model', parent=MMENGINE_MODELS, scope='mmbeta')

The following figure shows the registry structure of MMAlpha and MMBeta.

Now we call the modules of MMAlpha in MMBeta.

model ='mmalpha.LogSoftmax'))
output = model(input)
# call LogSoftmax.forward

Calling a module of a sibling node requires the scope prefix to be specified in type, so the above configuration requires the prefix mmalpha.

However, if you need to call several modules of a sibling node, each with a prefix, this requires a lot of modification. Therefore, MMEngine introduces the DefaultScope, with which Registry can easily support temporary switching of the current node to the specified node.

If you need to switch the current node to the specified node temporarily, just set _scope_ to the scope of the specified node in cfg.

model ='LogSoftmax', _scope_='mmalpha'))
output = model(input)
# call LogSoftmax.forward
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