Weight initialization

During training, a proper initialization strategy is beneficial to speeding up the training or obtaining a higher performance. MMCV provide some commonly used methods for initializing modules like nn.Conv2d. Model initialization in MMdetection mainly uses init_cfg. Users can initialize models with following two steps:

1. Define init_cfg for a model or its components in model_cfg, but init_cfg of children components have higher priority and will override init_cfg of parents modules.

2. Build model as usual, but call model.init_weights() method explicitly, and model parameters will be initialized as configuration.

The high-level workflow of initialization in MMdetection is :

model_cfg(init_cfg) -> build_from_cfg -> model -> init_weight() -> initialize(self, self.init_cfg) -> children’s init_weight()

Description

It is dict or list[dict], and contains the following keys and values:

• type (str), containing the initializer name in INTIALIZERS, and followed by arguments of the initializer.

• layer (str or list[str]), containing the names of basic layers in Pytorch or MMCV with learnable parameters that will be initialized, e.g. 'Conv2d','DeformConv2d'.

• override (dict or list[dict]), containing the sub-modules that not inherit from BaseModule and whose initialization configuration is different from other layers’ which are in 'layer' key. Initializer defined in type will work for all layers defined in layer, so if sub-modules are not derived Classes of BaseModule but can be initialized as same ways of layers in layer, it does not need to use override. override contains:

  • type followed by arguments of initializer;

  • name to indicate sub-module which will be initialized.

Initialize parameters

Inherit a new model from mmcv.runner.BaseModule or mmdet.models Here we show an example of FooModel.

import torch.nn as nn
from mmcv.runner import BaseModule

class FooModel(BaseModule)
	def __init__(self,
                 arg1,
                 arg2,
                 init_cfg=None):
    	super(FooModel, self).__init__(init_cfg)
		...

• Initialize model by using init_cfg directly in code

  import torch.nn as nn
  from mmcv.runner import BaseModule
  # or directly inherit mmdet models
  
  class FooModel(BaseModule)
  	def __init__(self,
                  arg1,
                  arg2,
                  init_cfg=XXX):
  		super(FooModel, self).__init__(init_cfg)
  	    ...
  

• Initialize model by using init_cfg directly in mmcv.Sequential or mmcv.ModuleList code

  from mmcv.runner import BaseModule, ModuleList
  
  class FooModel(BaseModule)
  	def __init__(self,
              	arg1,
              	arg2,
              	init_cfg=None):
  		super(FooModel, self).__init__(init_cfg)
      	...
      	self.conv1 = ModuleList(init_cfg=XXX)
  

• Initialize model by using init_cfg in config file

  model = dict(
  	...
  	model = dict(
      	type='FooModel',
      	arg1=XXX,
      	arg2=XXX,
      	init_cfg=XXX),
          ...
  

Usage of init_cfg

1. Initialize model by layer key

   If we only define layer, it just initialize the layer in layer key.

   NOTE: Value of layer key is the class name with attributes weights and bias of Pytorch, (so such as MultiheadAttention layer is not supported).

• Define layer key for initializing module with same configuration.

  init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d', 'Linear'], val=1)
  # initialize whole module with same configuration
  

• Define layer key for initializing layer with different configurations.

init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
            dict(type='Constant', layer='Conv2d', val=2),
            dict(type='Constant', layer='Linear', val=3)]
# nn.Conv1d will be initialized with dict(type='Constant', val=1)
# nn.Conv2d will be initialized with dict(type='Constant', val=2)
# nn.Linear will be initialized with dict(type='Constant', val=3)

2. Initialize model by override key

• When initializing some specific part with its attribute name, we can use override key, and the value in override will ignore the value in init_cfg.

  # layers:
  # self.feat = nn.Conv1d(3, 1, 3)
  # self.reg = nn.Conv2d(3, 3, 3)
  # self.cls = nn.Linear(1,2)
  
  init_cfg = dict(type='Constant',
                  layer=['Conv1d','Conv2d'], val=1, bias=2,
                  override=dict(type='Constant', name='reg', val=3, bias=4))
  # self.feat and self.cls will be initialized with 	dict(type='Constant', val=1, bias=2)
  # The module called 'reg' will be initialized with dict(type='Constant', val=3, bias=4)
  

• If layer is None in init_cfg, only sub-module with the name in override will be initialized, and type and other args in override can be omitted.

  # layers:
  # self.feat = nn.Conv1d(3, 1, 3)
  # self.reg = nn.Conv2d(3, 3, 3)
  # self.cls = nn.Linear(1,2)
  
  init_cfg = dict(type='Constant', val=1, bias=2, 	override=dict(name='reg'))
  
  # self.feat and self.cls will be initialized by Pytorch
  # The module called 'reg' will be initialized with dict(type='Constant', val=1, bias=2)
  

• If we don’t define layer key or override key, it will not initialize anything.

• Invalid usage

  # It is invalid that override don't have name key
  init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'], val=1, bias=2,
              	override=dict(type='Constant', val=3, bias=4))
  
  # It is also invalid that override has name and other args except type
  init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'], val=1, bias=2,
                  override=dict(name='reg', val=3, bias=4))
  

3. Initialize model with the pretrained model

   init_cfg = dict(type='Pretrained',
               checkpoint='torchvision://resnet50')
   

More details can refer to the documentation in MMEngine