diff --git a/mmengine/model/__init__.py b/mmengine/model/__init__.py
index 47e2356c270d1b181bec01910107ddb79d65acdd..f1aedde257853fec2c47fe60db78a34329561f45 100644
--- a/mmengine/model/__init__.py
+++ b/mmengine/model/__init__.py
@@ -2,7 +2,7 @@
from .averaged_model import (ExponentialMovingAverage, MomentumAnnealingEMA,
StochasticWeightAverage)
from .base_model import BaseDataPreprocessor, BaseModel, ImgDataPreprocessor
-from .base_module import BaseModule
+from .base_module import BaseModule, ModuleDict, ModuleList, Sequential
from .utils import detect_anomalous_params, merge_dict, stack_batch
from .wrappers import (MMDistributedDataParallel,
MMSeparateDistributedDataParallel, is_model_wrapper)
@@ -12,5 +12,6 @@ __all__ = [
'ExponentialMovingAverage', 'MomentumAnnealingEMA', 'BaseModel',
'BaseDataPreprocessor', 'ImgDataPreprocessor',
'MMSeparateDistributedDataParallel', 'BaseModule', 'stack_batch',
- 'merge_dict', 'detect_anomalous_params'
+ 'merge_dict', 'detect_anomalous_params', 'ModuleList', 'ModuleDict',
+ 'Sequential'
]
diff --git a/mmengine/model/base_module.py b/mmengine/model/base_module.py
index 89f140dccfba742c57b4ae1427d50d5a5d468166..380a804cd6a7b4ce262ef997650f6f9862002d25 100644
--- a/mmengine/model/base_module.py
+++ b/mmengine/model/base_module.py
@@ -5,6 +5,7 @@ import warnings
from abc import ABCMeta
from collections import defaultdict
from logging import FileHandler
+from typing import Iterable, Optional
import torch.nn as nn
@@ -165,3 +166,55 @@ class BaseModule(nn.Module, metaclass=ABCMeta):
if self.init_cfg:
s += f'\ninit_cfg={self.init_cfg}'
return s
+
+
+class Sequential(BaseModule, nn.Sequential):
+ """Sequential module in openmmlab.
+
+ Ensures that all modules in ``Sequential`` have a different initialization
+ strategy than the outer model
+
+ Args:
+ init_cfg (dict, optional): Initialization config dict.
+ """
+
+ def __init__(self, *args, init_cfg: Optional[dict] = None):
+ BaseModule.__init__(self, init_cfg)
+ nn.Sequential.__init__(self, *args)
+
+
+class ModuleList(BaseModule, nn.ModuleList):
+ """ModuleList in openmmlab.
+
+ Ensures that all modules in ``ModuleList`` have a different initialization
+ strategy than the outer model
+
+ Args:
+ modules (iterable, optional): An iterable of modules to add.
+ init_cfg (dict, optional): Initialization config dict.
+ """
+
+ def __init__(self,
+ modules: Optional[Iterable] = None,
+ init_cfg: Optional[dict] = None):
+ BaseModule.__init__(self, init_cfg)
+ nn.ModuleList.__init__(self, modules)
+
+
+class ModuleDict(BaseModule, nn.ModuleDict):
+ """ModuleDict in openmmlab.
+
+ Ensures that all modules in ``ModuleDict`` have a different initialization
+ strategy than the outer model
+
+ Args:
+ modules (dict, optional): A mapping (dictionary) of (string: module)
+ or an iterable of key-value pairs of type (string, module).
+ init_cfg (dict, optional): Initialization config dict.
+ """
+
+ def __init__(self,
+ modules: Optional[dict] = None,
+ init_cfg: Optional[dict] = None):
+ BaseModule.__init__(self, init_cfg)
+ nn.ModuleDict.__init__(self, modules)
diff --git a/tests/test_model/test_base_module.py b/tests/test_model/test_base_module.py
index a253e0b3810b7a7fcd2e2684005fff1af8e433fd..b9d3c4f76e4dbc84b298f16bc814b16d4fbdc42d 100644
--- a/tests/test_model/test_base_module.py
+++ b/tests/test_model/test_base_module.py
@@ -5,7 +5,7 @@ import torch
from torch import nn
from mmengine.logging.logger import MMLogger
-from mmengine.model.base_module import BaseModule
+from mmengine.model import BaseModule, ModuleDict, ModuleList, Sequential
from mmengine.registry import Registry, build_from_cfg
COMPONENTS = Registry('component')
@@ -195,3 +195,164 @@ class TestBaseModule(TestCase):
assert len(os.listdir(dump_dir)) == 1
assert os.stat(log_path).st_size != 0
shutil.rmtree(dump_dir)
+
+
+class TestModuleList(TestCase):
+
+ def test_modulelist_weight_init(self):
+ models_cfg = [
+ dict(
+ type='FooConv1d',
+ init_cfg=dict(
+ type='Constant', layer='Conv1d', val=0., bias=1.)),
+ dict(
+ type='FooConv2d',
+ init_cfg=dict(
+ type='Constant', layer='Conv2d', val=2., bias=3.)),
+ ]
+ layers = [build_from_cfg(cfg, COMPONENTS) for cfg in models_cfg]
+ modellist = ModuleList(layers)
+ modellist.init_weights()
+ self.assertTrue(
+ torch.equal(modellist[0].conv1d.weight,
+ torch.full(modellist[0].conv1d.weight.shape, 0.)))
+ self.assertTrue(
+ torch.equal(modellist[0].conv1d.bias,
+ torch.full(modellist[0].conv1d.bias.shape, 1.)))
+ self.assertTrue(
+ torch.equal(modellist[1].conv2d.weight,
+ torch.full(modellist[1].conv2d.weight.shape, 2.)))
+ self.assertTrue(
+ torch.equal(modellist[1].conv2d.bias,
+ torch.full(modellist[1].conv2d.bias.shape, 3.)))
+ # inner init_cfg has higher priority
+ layers = [build_from_cfg(cfg, COMPONENTS) for cfg in models_cfg]
+ modellist = ModuleList(
+ layers,
+ init_cfg=dict(
+ type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.))
+ modellist.init_weights()
+ self.assertTrue(
+ torch.equal(modellist[0].conv1d.weight,
+ torch.full(modellist[0].conv1d.weight.shape, 0.)))
+ self.assertTrue(
+ torch.equal(modellist[0].conv1d.bias,
+ torch.full(modellist[0].conv1d.bias.shape, 1.)))
+ self.assertTrue(
+ torch.equal(modellist[1].conv2d.weight,
+ torch.full(modellist[1].conv2d.weight.shape, 2.)))
+ self.assertTrue(
+ torch.equal(modellist[1].conv2d.bias,
+ torch.full(modellist[1].conv2d.bias.shape, 3.)))
+
+
+class TestModuleDict(TestCase):
+
+ def test_moduledict_weight_init(self):
+ models_cfg = dict(
+ foo_conv_1d=dict(
+ type='FooConv1d',
+ init_cfg=dict(
+ type='Constant', layer='Conv1d', val=0., bias=1.)),
+ foo_conv_2d=dict(
+ type='FooConv2d',
+ init_cfg=dict(
+ type='Constant', layer='Conv2d', val=2., bias=3.)),
+ )
+ layers = {
+ name: build_from_cfg(cfg, COMPONENTS)
+ for name, cfg in models_cfg.items()
+ }
+ modeldict = ModuleDict(layers)
+ modeldict.init_weights()
+ self.assertTrue(
+ torch.equal(
+ modeldict['foo_conv_1d'].conv1d.weight,
+ torch.full(modeldict['foo_conv_1d'].conv1d.weight.shape, 0.)))
+ self.assertTrue(
+ torch.equal(
+ modeldict['foo_conv_1d'].conv1d.bias,
+ torch.full(modeldict['foo_conv_1d'].conv1d.bias.shape, 1.)))
+ self.assertTrue(
+ torch.equal(
+ modeldict['foo_conv_2d'].conv2d.weight,
+ torch.full(modeldict['foo_conv_2d'].conv2d.weight.shape, 2.)))
+ self.assertTrue(
+ torch.equal(
+ modeldict['foo_conv_2d'].conv2d.bias,
+ torch.full(modeldict['foo_conv_2d'].conv2d.bias.shape, 3.)))
+ # inner init_cfg has higher priority
+ layers = {
+ name: build_from_cfg(cfg, COMPONENTS)
+ for name, cfg in models_cfg.items()
+ }
+ modeldict = ModuleDict(
+ layers,
+ init_cfg=dict(
+ type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.))
+ modeldict.init_weights()
+ self.assertTrue(
+ torch.equal(
+ modeldict['foo_conv_1d'].conv1d.weight,
+ torch.full(modeldict['foo_conv_1d'].conv1d.weight.shape, 0.)))
+ self.assertTrue(
+ torch.equal(
+ modeldict['foo_conv_1d'].conv1d.bias,
+ torch.full(modeldict['foo_conv_1d'].conv1d.bias.shape, 1.)))
+ self.assertTrue(
+ torch.equal(
+ modeldict['foo_conv_2d'].conv2d.weight,
+ torch.full(modeldict['foo_conv_2d'].conv2d.weight.shape, 2.)))
+ self.assertTrue(
+ torch.equal(
+ modeldict['foo_conv_2d'].conv2d.bias,
+ torch.full(modeldict['foo_conv_2d'].conv2d.bias.shape, 3.)))
+
+
+class TestSequential(TestCase):
+
+ def test_sequential_model_weight_init(self):
+ seq_model_cfg = [
+ dict(
+ type='FooConv1d',
+ init_cfg=dict(
+ type='Constant', layer='Conv1d', val=0., bias=1.)),
+ dict(
+ type='FooConv2d',
+ init_cfg=dict(
+ type='Constant', layer='Conv2d', val=2., bias=3.)),
+ ]
+ layers = [build_from_cfg(cfg, COMPONENTS) for cfg in seq_model_cfg]
+ seq_model = Sequential(*layers)
+ seq_model.init_weights()
+ self.assertTrue(
+ torch.equal(seq_model[0].conv1d.weight,
+ torch.full(seq_model[0].conv1d.weight.shape, 0.)))
+ self.assertTrue(
+ torch.equal(seq_model[0].conv1d.bias,
+ torch.full(seq_model[0].conv1d.bias.shape, 1.)))
+ self.assertTrue(
+ torch.equal(seq_model[1].conv2d.weight,
+ torch.full(seq_model[1].conv2d.weight.shape, 2.)))
+ self.assertTrue(
+ torch.equal(seq_model[1].conv2d.bias,
+ torch.full(seq_model[1].conv2d.bias.shape, 3.)))
+ # inner init_cfg has higher priority
+ layers = [build_from_cfg(cfg, COMPONENTS) for cfg in seq_model_cfg]
+ seq_model = Sequential(
+ *layers,
+ init_cfg=dict(
+ type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.))
+ seq_model.init_weights()
+ self.assertTrue(
+ torch.equal(seq_model[0].conv1d.weight,
+ torch.full(seq_model[0].conv1d.weight.shape, 0.)))
+ self.assertTrue(
+ torch.equal(seq_model[0].conv1d.bias,
+ torch.full(seq_model[0].conv1d.bias.shape, 1.)))
+ self.assertTrue(
+ torch.equal(seq_model[1].conv2d.weight,
+ torch.full(seq_model[1].conv2d.weight.shape, 2.)))
+ self.assertTrue(
+ torch.equal(seq_model[1].conv2d.bias,
+ torch.full(seq_model[1].conv2d.bias.shape, 3.)))