[Feature] Add ModuleDict inherited from BaseModule and ModuleDict (#1542)

* improve: add module dict

* Update __init__.py

* docs: add module dict zh

* docs: add module dict

* Update test_basemodule.py

* Update __init__.py

* lint

* lint

* docs

* docs

* Update base_module.py

* lint

docs/community/contributing.md

-../../CONTRIBUTING.md
+../../CONTRIBUTING.md
\ No newline at end of file

docs/understand_mmcv/cnn.md

@@ -366,7 +366,7 @@ Let us introduce the usage of `initialize` in detail.
     initialize(model, init_cfg)
     ```
 
-4. Initialize model inherited from BaseModule, Sequential, ModuleList
+4. Initialize model inherited from BaseModule, Sequential, ModuleList, ModuleDict
 
     `BaseModule` is inherited from `torch.nn.Module`, and the only different between them is that `BaseModule` implements `init_weight`.
 
@@ -374,9 +374,11 @@ Let us introduce the usage of `initialize` in detail.
 
     `ModuleList` is inherited from `BaseModule` and `torch.nn.ModuleList`.
 
+    `ModuleDict` is inherited from `BaseModule` and `torch.nn.ModuleDict`.
+
     `````python
     import torch.nn as nn
-    from mmcv.runner import BaseModule, Sequential, ModuleList
+    from mmcv.runner import BaseModule, Sequential, ModuleList, ModuleDict
 
     class FooConv1d(BaseModule):
 
@@ -494,6 +496,49 @@ Let us introduce the usage of `initialize` in detail.
     #          [[2., 2., 2.],
     #           [2., 2., 2.],
     #           [2., 2., 2.]]]], requires_grad=True)
+
+    # ModuleDict
+    model1 = FooConv1d(init_cfg1)
+    model2 = FooConv2d(init_cfg2)
+    modeldict = ModuleDict(dict(model1=model1, model2=model2))
+    modeldict.init_weights()
+    # modeldict['model1'].conv1d.weight
+    # Parameter containing:
+    # tensor([[[0., 0., 0., 0.],
+    #         [0., 0., 0., 0.],
+    #         [0., 0., 0., 0.],
+    #         [0., 0., 0., 0.]]], requires_grad=True)
+    # modeldict['model2'].conv2d.weight
+    # Parameter containing:
+    # tensor([[[[2., 2., 2.],
+    #           [2., 2., 2.],
+    #           [2., 2., 2.]],
+    #         ...,
+    #          [[2., 2., 2.],
+    #           [2., 2., 2.],
+    #           [2., 2., 2.]]]], requires_grad=True)
+
+    # inner init_cfg has higher priority
+    model1 = FooConv1d(init_cfg1)
+    model2 = FooConv2d(init_cfg2)
+    init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)
+    modeldict = ModuleDict(dict(model1=model1, model2=model2), init_cfg=init_cfg)
+    modeldict.init_weights()
+    # modeldict['model1'].conv1d.weight
+    # Parameter containing:
+    # tensor([[[0., 0., 0., 0.],
+    #         [0., 0., 0., 0.],
+    #         [0., 0., 0., 0.],
+    #         [0., 0., 0., 0.]]], requires_grad=True)
+    # modeldict['model2'].conv2d.weight
+    # Parameter containing:
+    # tensor([[[[2., 2., 2.],
+    #           [2., 2., 2.],
+    #           [2., 2., 2.]],
+    #         ...,
+    #          [[2., 2., 2.],
+    #           [2., 2., 2.],
+    #           [2., 2., 2.]]]], requires_grad=True)
     `````
 
 ### Model Zoo

docs_zh_CN/understand_mmcv/cnn.md

@@ -355,7 +355,7 @@ conv = ConvModule(
     initialize(model, init_cfg)
     ```
 
-4. 初始化继承自BaseModule、Sequential、ModuleList的模型
+4. 初始化继承自BaseModule、Sequential、ModuleList、ModuleDict的模型
 
     `BaseModule` 继承自 `torch.nn.Module`, 它们之间唯一的不同是 `BaseModule` 实现了 `init_weight`
 
@@ -363,9 +363,11 @@ conv = ConvModule(
 
     `ModuleList` 继承自 `BaseModule` 和 `torch.nn.ModuleList`
 
+    `ModuleDict` 继承自 `BaseModule` 和 `torch.nn.ModuleDict`
+
     `````python
     import torch.nn as nn
-    from mmcv.runner import BaseModule, Sequential, ModuleList
+    from mmcv.runner import BaseModule, Sequential, ModuleList, ModuleDict
 
     class FooConv1d(BaseModule):
 
@@ -483,6 +485,49 @@ conv = ConvModule(
     #          [[2., 2., 2.],
     #           [2., 2., 2.],
     #           [2., 2., 2.]]]], requires_grad=True)
+
+    # ModuleDict
+    model1 = FooConv1d(init_cfg1)
+    model2 = FooConv2d(init_cfg2)
+    modeldict = ModuleDict(dict(model1=model1, model2=model2))
+    modeldict.init_weights()
+    # modeldict['model1'].conv1d.weight
+    # Parameter containing:
+    # tensor([[[0., 0., 0., 0.],
+    #         [0., 0., 0., 0.],
+    #         [0., 0., 0., 0.],
+    #         [0., 0., 0., 0.]]], requires_grad=True)
+    # modeldict['model2'].conv2d.weight
+    # Parameter containing:
+    # tensor([[[[2., 2., 2.],
+    #           [2., 2., 2.],
+    #           [2., 2., 2.]],
+    #         ...,
+    #          [[2., 2., 2.],
+    #           [2., 2., 2.],
+    #           [2., 2., 2.]]]], requires_grad=True)
+
+    # inner init_cfg has higher priority
+    model1 = FooConv1d(init_cfg1)
+    model2 = FooConv2d(init_cfg2)
+    init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)
+    modeldict = ModuleDict(dict(model1=model1, model2=model2), init_cfg=init_cfg)
+    modeldict.init_weights()
+    # modeldict['model1'].conv1d.weight
+    # Parameter containing:
+    # tensor([[[0., 0., 0., 0.],
+    #         [0., 0., 0., 0.],
+    #         [0., 0., 0., 0.],
+    #         [0., 0., 0., 0.]]], requires_grad=True)
+    # modeldict['model2'].conv2d.weight
+    # Parameter containing:
+    # tensor([[[[2., 2., 2.],
+    #           [2., 2., 2.],
+    #           [2., 2., 2.]],
+    #         ...,
+    #          [[2., 2., 2.],
+    #           [2., 2., 2.],
+    #           [2., 2., 2.]]]], requires_grad=True)
     `````
 
 ### Model Zoo

mmcv/runner/__init__.py

 # Copyright (c) OpenMMLab. All rights reserved.
-from .base_module import BaseModule, ModuleList, Sequential
+from .base_module import BaseModule, ModuleDict, ModuleList, Sequential
 from .base_runner import BaseRunner
 from .builder import RUNNERS, build_runner
 from .checkpoint import (CheckpointLoader, _load_checkpoint,
@@ -42,6 +42,6 @@ __all__ = [
     'SyncBuffersHook', 'EMAHook', 'build_runner', 'RUNNERS', 'allreduce_grads',
     'allreduce_params', 'LossScaler', 'CheckpointLoader', 'BaseModule',
     '_load_checkpoint_with_prefix', 'EvalHook', 'DistEvalHook', 'Sequential',
-    'ModuleList', 'GradientCumulativeOptimizerHook',
+    'ModuleDict', 'ModuleList', 'GradientCumulativeOptimizerHook',
     'GradientCumulativeFp16OptimizerHook', 'DefaultRunnerConstructor'
 ]

mmcv/runner/base_module.py

@@ -193,3 +193,17 @@ class ModuleList(BaseModule, nn.ModuleList):
     def __init__(self, modules=None, init_cfg=None):
         BaseModule.__init__(self, init_cfg)
         nn.ModuleList.__init__(self, modules)
+
+
+class ModuleDict(BaseModule, nn.ModuleDict):
+    """ModuleDict in openmmlab.
+
+    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=None, init_cfg=None):
+        BaseModule.__init__(self, init_cfg)
+        nn.ModuleDict.__init__(self, modules)

tests/test_runner/test_basemodule.py

@@ -6,7 +6,7 @@ from torch import nn
 
 import mmcv
 from mmcv.cnn.utils.weight_init import update_init_info
-from mmcv.runner import BaseModule, ModuleList, Sequential
+from mmcv.runner import BaseModule, ModuleDict, ModuleList, Sequential
 from mmcv.utils import Registry, build_from_cfg
 
 COMPONENTS = Registry('component')
@@ -555,3 +555,54 @@ def test_modulelist_weight_init():
                        torch.full(modellist[1].conv2d.weight.shape, 2.))
     assert torch.equal(modellist[1].conv2d.bias,
                        torch.full(modellist[1].conv2d.bias.shape, 3.))
+
+
+def test_moduledict_weight_init():
+    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()
+    assert torch.equal(
+        modeldict['foo_conv_1d'].conv1d.weight,
+        torch.full(modeldict['foo_conv_1d'].conv1d.weight.shape, 0.))
+    assert torch.equal(
+        modeldict['foo_conv_1d'].conv1d.bias,
+        torch.full(modeldict['foo_conv_1d'].conv1d.bias.shape, 1.))
+    assert torch.equal(
+        modeldict['foo_conv_2d'].conv2d.weight,
+        torch.full(modeldict['foo_conv_2d'].conv2d.weight.shape, 2.))
+    assert 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()
+    assert torch.equal(
+        modeldict['foo_conv_1d'].conv1d.weight,
+        torch.full(modeldict['foo_conv_1d'].conv1d.weight.shape, 0.))
+    assert torch.equal(
+        modeldict['foo_conv_1d'].conv1d.bias,
+        torch.full(modeldict['foo_conv_1d'].conv1d.bias.shape, 1.))
+    assert torch.equal(
+        modeldict['foo_conv_2d'].conv2d.weight,
+        torch.full(modeldict['foo_conv_2d'].conv2d.weight.shape, 2.))
+    assert torch.equal(
+        modeldict['foo_conv_2d'].conv2d.bias,
+        torch.full(modeldict['foo_conv_2d'].conv2d.bias.shape, 3.))