[Refactoring]Add Sequential with init_weight (#884)

* [Refactoring]Add BaseSequtial with init_weight

* revise according to comments

* revise comments

* minors

* baseseq2seq

* add modulelist

* revise minors

* fix isort

* format

mmcv/runner/__init__.py

 # Copyright (c) Open-MMLab. All rights reserved.
-from .base_module import BaseModule
+from .base_module import BaseModule, ModuleList, Sequential
 from .base_runner import BaseRunner
 from .builder import RUNNERS, build_runner
 from .checkpoint import (CheckpointLoader, _load_checkpoint,
@@ -36,5 +36,6 @@ __all__ = [
     'set_random_seed', 'auto_fp16', 'force_fp32', 'wrap_fp16_model',
     'Fp16OptimizerHook', 'SyncBuffersHook', 'EMAHook', 'build_runner',
     'RUNNERS', 'allreduce_grads', 'allreduce_params', 'LossScaler',
-    'CheckpointLoader', 'BaseModule', '_load_checkpoint_with_prefix'
+    'CheckpointLoader', 'BaseModule', '_load_checkpoint_with_prefix',
+    'Sequential', 'ModuleList'
 ]

mmcv/runner/base_module.py

@@ -51,3 +51,34 @@ class BaseModule(nn.Module, metaclass=ABCMeta):
         else:
             warnings.warn('This module has bee initialized, \
                 please call initialize(module, init_cfg) to reinitialize it')
+
+    def __repr__(self):
+        s = super().__repr__()
+        if hasattr(self, 'init_cfg'):
+            s += f'\ninit_cfg={self.init_cfg}'
+        return s
+
+
+class Sequential(BaseModule, nn.Sequential):
+    """Sequential module in openmmlab.
+
+    Args:
+        init_cfg (dict, optional): Initialization config dict.
+    """
+
+    def __init__(self, *args, init_cfg=None):
+        BaseModule.__init__(self, init_cfg)
+        nn.Sequential.__init__(self, *args)
+
+
+class ModuleList(BaseModule, nn.ModuleList):
+    """ModuleList in openmmlab.
+
+    Args:
+        modules (iterable, optional): an iterable of modules to add.
+        init_cfg (dict, optional): Initialization config dict.
+    """
+
+    def __init__(self, modules=None, init_cfg=None):
+        BaseModule.__init__(self, init_cfg)
+        nn.ModuleList.__init__(self, modules)

tests/test_runner/test_basemodule.py

 import torch
 from torch import nn
 
-from mmcv.runner import BaseModule
+from mmcv.runner import BaseModule, ModuleList, Sequential
 from mmcv.utils import Registry, build_from_cfg
 
 COMPONENTS = Registry('component')
@@ -226,3 +226,65 @@ def test_nest_components_weight_init():
     assert torch.equal(model.reg.weight,
                        torch.full(model.reg.weight.shape, 13.0))
     assert torch.equal(model.reg.bias, torch.full(model.reg.bias.shape, 14.0))
+
+
+def test_sequential_model_weight_init():
+    seq_model_cfg = [
+        dict(
+            type='FooConv1d', init_cfg=dict(type='Constant', val=0., bias=1.)),
+        dict(
+            type='FooConv2d', init_cfg=dict(type='Constant', val=2., bias=3.)),
+    ]
+    layers = [build_from_cfg(cfg, COMPONENTS) for cfg in seq_model_cfg]
+    seq_model = Sequential(*layers)
+    seq_model.init_weight()
+    assert torch.equal(seq_model[0].conv1d.weight,
+                       torch.full(seq_model[0].conv1d.weight.shape, 0.))
+    assert torch.equal(seq_model[0].conv1d.bias,
+                       torch.full(seq_model[0].conv1d.bias.shape, 1.))
+    assert torch.equal(seq_model[1].conv2d.weight,
+                       torch.full(seq_model[1].conv2d.weight.shape, 2.))
+    assert torch.equal(seq_model[1].conv2d.bias,
+                       torch.full(seq_model[1].conv2d.bias.shape, 3.))
+    # inner init_cfg has highter priority
+    seq_model = Sequential(
+        *layers, init_cfg=dict(type='Constant', val=4., bias=5.))
+    assert torch.equal(seq_model[0].conv1d.weight,
+                       torch.full(seq_model[0].conv1d.weight.shape, 0.))
+    assert torch.equal(seq_model[0].conv1d.bias,
+                       torch.full(seq_model[0].conv1d.bias.shape, 1.))
+    assert torch.equal(seq_model[1].conv2d.weight,
+                       torch.full(seq_model[1].conv2d.weight.shape, 2.))
+    assert torch.equal(seq_model[1].conv2d.bias,
+                       torch.full(seq_model[1].conv2d.bias.shape, 3.))
+
+
+def test_modulelist_weight_init():
+    models_cfg = [
+        dict(
+            type='FooConv1d', init_cfg=dict(type='Constant', val=0., bias=1.)),
+        dict(
+            type='FooConv2d', init_cfg=dict(type='Constant', val=2., bias=3.)),
+    ]
+    layers = [build_from_cfg(cfg, COMPONENTS) for cfg in models_cfg]
+    modellist = ModuleList(layers)
+    modellist.init_weight()
+    assert torch.equal(modellist[0].conv1d.weight,
+                       torch.full(modellist[0].conv1d.weight.shape, 0.))
+    assert torch.equal(modellist[0].conv1d.bias,
+                       torch.full(modellist[0].conv1d.bias.shape, 1.))
+    assert torch.equal(modellist[1].conv2d.weight,
+                       torch.full(modellist[1].conv2d.weight.shape, 2.))
+    assert torch.equal(modellist[1].conv2d.bias,
+                       torch.full(modellist[1].conv2d.bias.shape, 3.))
+    # inner init_cfg has highter priority
+    modellist = ModuleList(
+        layers, init_cfg=dict(type='Constant', val=4., bias=5.))
+    assert torch.equal(modellist[0].conv1d.weight,
+                       torch.full(modellist[0].conv1d.weight.shape, 0.))
+    assert torch.equal(modellist[0].conv1d.bias,
+                       torch.full(modellist[0].conv1d.bias.shape, 1.))
+    assert torch.equal(modellist[1].conv2d.weight,
+                       torch.full(modellist[1].conv2d.weight.shape, 2.))
+    assert torch.equal(modellist[1].conv2d.bias,
+                       torch.full(modellist[1].conv2d.bias.shape, 3.))