MMEngine implements an abstract configuration class (Config) to provide a unified configuration access interface for users. Config supports different type of configuration file, including python, json and yaml, and you can choose the type according to your preference. Config overrides some magic method, which could help you access the data stored in Config just like getting values from dict, or getting attributes from instances. Besides, Config also provides an inheritance mechanism, which could help you better organize and manage the configuration files.

Before starting the tutorial, let’s download the configuration files needed in the tutorial (it is recommended to execute them in a temporary directory to facilitate deleting these files latter.):


Read the configuration file

Config provides a uniform interface Config.fromfile() to read and parse configuration files.

A valid configuration file should define a set of key-value pairs, and here are a few examples:


test_int = 1
test_list = [1, 2, 3]
test_dict = dict(key1='value1', key2=0.1)


  "test_int": 1,
  "test_list": [1, 2, 3],
  "test_dict": {"key1": "value1", "key2": 0.1}


test_int: 1
test_list: [1, 2, 3]
  key1: "value1"
  key2: 0.1

For the above three formats, assuming the file names are, config.json, and config.yml. Loading these files with Config.fromfile('') will return the same result, which contain test_int, test_list and test_dict 3 variables.

Let’s take as an example:

from mmengine.config import Config

cfg = Config.fromfile('')
Config (path: {'test_int': 1, 'test_list': [1, 2, 3], 'test_dict': {'key1': 'value1', 'key2': 0.1}}

How to use Config

After loading the configuration file, we can access the data stored in Config instance just like getting/setting values from dict, or getting/setting attributes from instances.

cfg.test_int = 2

cfg['test_list'][1] = 3
[1, 2, 3]
{'key1': 'value1', 'key2': 0.1}
[1, 2, 3]
{'key1': 'value1', 'key2': 0.1}
[1, 3, 3]


The dict object parsed by Config will be converted to ConfigDict, and then we can access the value of the dict the same as accessing the attribute of an instance.

We can use the Config combination with the Registry to build registered instance easily.

Here is an example of defining optimizers in a configuration file.

optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)

Suppose we have defined a registry OPTIMIZERS, which includes various optimizers. Then we can build the optimizer as below

from mmengine import Config, optim
from mmengine.registry import OPTIMIZERS

import torch.nn as nn

cfg = Config.fromfile('')

model = nn.Conv2d(1, 1, 1)
cfg.optimizer.params = model.parameters()
optimizer =
Parameter Group 0
    dampening: 0
    foreach: None
    lr: 0.1
    maximize: False
    momentum: 0.9
    nesterov: False
    weight_decay: 0.0001

Inheritance between configuration files

Sometimes, the difference between two different configuration files is so small that only one field may be changed. Therefore, it’s unwise to copy and paste everything only to modify one line, which makes it hard for us to locate the specific difference after a long time.

In another case, multiple configuration files may have the same batch of fields, and we have to copy and paste them in different configuration files. It will also be hard to maintain these fields in a long time.

We address these issues with inheritance mechanism, detailed as below.

Overview of inheritance mechanism

Here is an example to illustrate the inheritance mechanism.

optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)

_base_ = ['']
model = dict(type='ResNet', depth=50)

Although we don’t define optimizer in, since we wrote _base_ = [''], it will inherit the fields defined in

cfg = Config.fromfile('')
{'type': 'SGD', 'lr': 0.02, 'momentum': 0.9, 'weight_decay': 0.0001}

_base_ is a reserved field for the configuration file. It specifies the inherited base files for the current file. Inheriting multiple files will get all the fields at the same time, but it requires that there are no repeated fields defined in all base files.

gpu_ids = [0, 1]

_base_ = ['', '']
model = dict(type='ResNet', depth=50)

In this case, reading the will give you 3 fields model, optimizer, and gpu_ids.

cfg = Config.fromfile('')
{'type': 'SGD', 'lr': 0.02, 'momentum': 0.9, 'weight_decay': 0.0001}

By this way, we can disassemble the configuration file, define some general configuration files, and inherit them in the specific configuration file. This could avoid defining a lot of duplicated contents in multiple configuration files.

Modify the inherited fields

Sometimes, we want to modify some of the fields in the inherited files. For example we want to modify the learning rate from 0.02 to 0.01 after inheriting

In this case, you can simply redefine the fields in the new configuration file. Note that since the optimizer field is a dictionary, we only need to redefine the modified fields. This rule also applies to adding fields.

_base_ = ['', '']
model = dict(type='ResNet', depth=50)
optimizer = dict(lr=0.01)

After reading this configuration file, you can get the desired result.

cfg = Config.fromfile('')
{'type': 'SGD', 'lr': 0.01, 'momentum': 0.9, 'weight_decay': 0.0001}

For non-dictionary fields, such as integers, strings, lists, etc., they can be completely overwritten by redefining them. For example, the code block below will change the value of the gpu_ids to [0].

_base_ = ['', '']
model = dict(type='ResNet', depth=50)
gpu_ids = [0]

Delete key in dict

Sometimes we not only want to modify or add the keys, but also want to delete them. In this case, we need to set _delete_=True in the target field(dict) to delete all the keys that do not appear in the newly defined dictionary.

_base_ = ['', '']
model = dict(type='ResNet', depth=50)
optimizer = dict(_delete_=True, type='SGD', lr=0.01)

At this point, optimizer will only have the keys type and lr. momentum and weight_decay will no longer exist.

cfg = Config.fromfile('')
{'type': 'SGD', 'lr': 0.01}

Reference of the inherited file

Sometimes we want to reuse the field defined in _base_, we can get a copy of the corresponding variable by using {{_base_.xxxx}}:

_base_ = ['']
a = {{_base_.model}}

After parsing, the value of a becomes model defined in

cfg = Config.fromfile('')
{'type': 'ResNet', 'depth': 50}

We can use this way to get the variables defined in _base_ in the json, yaml, and python configuration files.

Although this way is general for all types of files, there are some syntactic limitations that prevent us from taking full advantage of the dynamic nature of the python configuration file. For example, if we want to modify a variable defined in _base_:

_base_ = ['']
a = {{_base_.model}}
a['type'] = 'MobileNet'

The Config is not able to parse such a configuration file (it will raise an error when parsing). The Config provides a more pythonic way to modify base variables for python configuration files.

_base_ = ['']
a = _base_.model
a.type = 'MobileNet'
cfg = Config.fromfile('')
{'type': 'MobileNet', 'depth': 50}

Dump the configuration file

The user may pass some parameters to modify some fields of the configuration file at the entry point of the training script. Therefore, we provide the dump method to export the changed configuration file.

Similar to reading the configuration file, the user can choose the format of the dumped file by using cfg.dump(''). dump can also export configuration files with inheritance relationships, and the dumped files can be used independently without the files defined in _base_.

Based on the defined above, we can load and dump it like this:

cfg = Config.fromfile('')

optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
model = dict(type='ResNet', depth=50)

Similarly, we can dump configuration files in json, yaml format:


  depth: 50
  type: ResNet
  lr: 0.02
  momentum: 0.9
  type: SGD
  weight_decay: 0.0001


{"optimizer": {"type": "SGD", "lr": 0.02, "momentum": 0.9, "weight_decay": 0.0001}, "model": {"type": "ResNet", "depth": 50}}

In addition, dump can also dump cfg loaded from a dictionary.

cfg = Config(dict(a=1, b=2))


Advanced usage

In this section, we’ll introduce some advanced usage of the Config, and some tips that could make it easier for users to develop and use downstream repositories.

Predefined fields

Sometimes we need some fields in the configuration file, which are related to the path to the workspace. For example, we define a working directory in the configuration file that holds the models and logs for this set of experimental configurations. We expect to have different working directories for different configuration files. A common choice is to use the configuration file name directly as part of the working directory name. Taking as an example:

work_dir = './work_dir/{{fileBasenameNoExtension}}'

Here {{fileBasenameNoExtension}} means the filename without suffix .py of the config file, and the variable in {{}} will be interpreted as predefined_var

cfg = Config.fromfile('./')

Currently, there are 4 predefined fields referenced from the relevant fields defined in VS Code.

  • {{fileDirname}} - the directory name of the current file, e.g. /home/your-username/your-project/folder

  • {{fileBasename}} - the filename of the current file, e.g.

  • {{fileBasenameNoExtension}} - the filename of the current file without the extension, e.g. file

  • {{fileExtname}} - the extension of the current file, e.g. .py

Modify the fields in command line

Sometimes we only want to modify part of the configuration and do not want to modify the configuration file itself. For example, if we want to change the learning rate during the experiment but do not want to write a new configuration file, the common practice is to pass the parameters at the command line to override the relevant configuration.

If we want to modify some internal parameters, such as the learning rate of the optimizer, the number of channels in the convolution layer etc., Config provides a standard procedure that allows us to modify the parameters at any level easily from the command line.

Training script:

import argparse

from mmengine.config import Config, DictAction

def parse_args():
    parser = argparse.ArgumentParser(description='Train a model')
    parser.add_argument('config', help='train config file path')
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')

    args = parser.parse_args()
    return args

def main():
    args = parse_args()
    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:

if __name__ == '__main__':

The sample configuration file is as follows.

model = dict(type='CustomModel', in_channels=[1, 2, 3])
optimizer = dict(type='SGD', lr=0.01)

We can modify the internal fields from the command line by . For example, if we want to modify the learning rate, we only need to execute the script like this:

python ./ --cfg-options
Config (path: ./ {'model': {'type': 'CustomModel', 'in_channels': [1, 2, 3]}, 'optimizer': {'type': 'SGD', 'lr': 0.1}}

We successfully modified the learning rate from 0.01 to 0.1. If we want to change a list or a tuple, such as in_channels in the above example. We need to put double quotes around (), [] when assigning the value on the command line.

python ./ --cfg-options model.in_channels="[1, 1, 1]"
Config (path: ./ {'model': {'type': 'CustomModel', 'in_channels': [1, 1, 1]}, 'optimizer': {'type': 'SGD', 'lr': 0.01}}


The standard procedure only supports modifying String, Integer, Floating Point, Boolean, None, List, and Tuple fields from the command line. For the elements of list and tuple instance, each of them must be one of the above seven types.


The behavior of DictAction is similar with "extend". It stores a list, and extends each argument value to the list, like:

python ./ --cfg-options optimizer.type="Adam" --cfg-options model.in_channels="[1, 1, 1]"
Config (path: ./ {'model': {'type': 'CustomModel', 'in_channels': [1, 1, 1]}, 'optimizer': {'type': 'Adam', 'lr': 0.01}}

Replace fields with environment variables

When a field is deeply nested, we need to add a long prefix at the command line to locate it. To alleviate this problem, MMEngine allows users to substitute fields in configuration with environment variables.

Before parsing the configuration file, the program will search all {{$ENV_VAR:DEF_VAL}} fields and substitute those sections with environment variables. Here, ENV_VAR is the name of the environment variable used to replace this section, DEF_VAL is the default value if ENV_VAR is not set.

When we want to modify the dataset path at the command line, we can take as an example:

dataset_type = 'CocoDataset'
data_root = '{{$DATASET:/data/coco/}}'
dataset=dict(ann_file= data_root + 'train.json')

If we run to parse this configuration file.

Config (path: {'dataset_type': 'CocoDataset', 'data_root': '/data/coco/', 'dataset': {'ann_file': '/data/coco/train.json'}}

Here, we don’t set the environment variable DATASET. Thus, the program directly replaces {{$DATASET:/data/coco/}} with the default value /data/coco/. If we set DATASET at the command line:

DATASET=/new/dataset/path/ python
Config (path: {'dataset_type': 'CocoDataset', 'data_root': '/new/dataset/path/', 'dataset': {'ann_file': '/new/dataset/path/train.json'}}

The value of data_root has been substituted with the value of DATASET as /new/dataset/path.

It is noteworthy that both --cfg-options and {{$ENV_VAR:DEF_VAL}} allow users to modify fields in command line. But there is a small difference between those two methods. Environment variable substitution occurs before the configuration parsing. If the replaced field is also involved in other fields assignment, the environment variable substitution will also affect the other fields.

We take and for example. If we replace data_root by setting --cfg-options data_root='/new/dataset/path':

python --cfg-options data_root='/new/dataset/path/'
Config (path: {'dataset_type': 'CocoDataset', 'data_root': '/new/dataset/path/', 'dataset': {'ann_file': '/data/coco/train.json'}}

As we can see, only data_root has been modified. dataset.ann_file is still the default value.

In contrast, if we replace data_root by setting DATASET=/new/dataset/path:

DATASET=/new/dataset/path/ python
Config (path: {'dataset_type': 'CocoDataset', 'data_root': '/new/dataset/path/', 'dataset': {'ann_file': '/new/dataset/path/train.json'}}

Both data_root and dataset.ann_file have been modified.

Environment variables can also be used to replace other types of fields. We can use {{'$ENV_VAR:DEF_VAL'}} or {{"$ENV_VAR:DEF_VAL"}} format to ensure the configuration file conforms to python syntax.

We can take as an example:


If we run to parse this configuration file.

Config (path: {'model': {'bbox_head': {'num_classes': 80}}}

Let us set the environment variable NUM_CLASSES

NUM_CLASSES=20 python
Config (path: {'model': {'bbox_head': {'num_classes': 20}}}

import the custom module

If we customize a module and register it into the corresponding registry, could we directly build it from the configuration file as the previous section does? The answer is “I don’t know” since I’m not sure the registration process has been triggered. To solve this “unknown” case, Config provides the custom_imports function, to make sure your module could be registered as expected.

For example, we customize an optimizer:

from mmengine.registry import OPTIMIZERS

class CustomOptim:

A matched config file:

optimizer = dict(type='CustomOptim')

To make sure CustomOptim will be registered, we should set the custom_imports field like this:

custom_imports = dict(imports=['my_module'], allow_failed_imports=False)
optimizer = dict(type='CustomOptim')

And then, once the custom_imports can be loaded successfully, we can build the CustomOptim from the

cfg = Config.fromfile('')

from mmengine.registry import OPTIMIZERS

custom_optim =
<my_module.CustomOptim object at 0x7f6983a87970>

Inherit configuration files across repository

It is annoying to copy a large number of configuration files when developing a new repository based on some existing repositories. To address this issue, Config support inherit configuration files from other repositories. For example, based on MMDetection, we want to develop a repository, we can use the MMDetection configuration file like this:

_base_ = [
cfg = Config.fromfile('')
{'type': 'EpochBasedTrainLoop', 'max_epochs': 12, 'val_interval': 1, '_scope_': 'mmdet'}

Config will parse mmdet:: to find mmdet package and inherits the specified configuration file. Actually, as long as the of the repository(package) conforms to MMEngine Installation specification, Config can use {package_name}:: to inherit the specific configuration file.

Get configuration files across repository

Config also provides get_config and get_model to get the configuration file and the trained model from the downstream repositories.

The usage of get_config and get_model are similar to the previous section:

An example of get_config:

from mmengine.hub import get_config

cfg = get_config(
    'mmdet::faster_rcnn/', pretrained=True)

An example of get_model:

from mmengine.hub import get_model

model = get_model(
    'mmdet::faster_rcnn/', pretrained=True)
http loads checkpoint from path:
<class 'mmdet.models.detectors.faster_rcnn.FasterRCNN'>
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