[Refactoring] Revise init_weight in BaseModule (#905)

* [Refactoring] Add deprecated API warning * revise test * fix lint * fix lint
open-mmlab · Apr 10, 2021 · 47825b1 · 47825b1
1 parent 89efc60
commit 47825b1
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Showing 2 changed files with 10 additions and 4 deletions.
diff --git a/mmcv/runner/base_module.py b/mmcv/runner/base_module.py
@@ -42,9 +42,9 @@ def init_weight(self):
         if not self._is_init:
             if hasattr(self, 'init_cfg'):
                 initialize(self, self.init_cfg)
-            for module in self.children():
-                if 'init_weight' in dir(module):
-                    module.init_weight()
+            for m in self.children():
+                if hasattr(m, 'init_weight'):
+                    m.init_weight()
             self._is_init = True
         else:
             warnings.warn(f'init_weight of {self.__class__.__name__} has '

diff --git a/tests/test_runner/test_basemodule.py b/tests/test_runner/test_basemodule.py
@@ -336,10 +336,12 @@ def test_sequential_model_weight_init():
     assert torch.equal(seq_model[1].conv2d.bias,
                        torch.full(seq_model[1].conv2d.bias.shape, 3.))
     # inner init_cfg has highter 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_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,
@@ -371,8 +373,12 @@ def test_modulelist_weight_init():
     assert torch.equal(modellist[1].conv2d.bias,
                        torch.full(modellist[1].conv2d.bias.shape, 3.))
     # inner init_cfg has highter priority
+    layers = [build_from_cfg(cfg, COMPONENTS) for cfg in models_cfg]
     modellist = ModuleList(
-        layers, init_cfg=dict(type='Constant', val=4., bias=5.))
+        layers,
+        init_cfg=dict(
+            type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.))
+    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,