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[Refactoring] Revise init_weight in BaseModule #905

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merged 5 commits into from
Apr 10, 2021
Merged

[Refactoring] Revise init_weight in BaseModule #905

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1f44411
[Refactoring] Add deprecated API warning
MeowZheng Mar 24, 2021
2629c57
revise test
MeowZheng Mar 24, 2021
5ee0cc0
del warning
MeowZheng Apr 2, 2021
24085d6
fix lint
MeowZheng Apr 2, 2021
6a413da
fix lint
MeowZheng Apr 2, 2021
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6 changes: 3 additions & 3 deletions mmcv/runner/base_module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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 '
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8 changes: 7 additions & 1 deletion tests/test_runner/test_basemodule.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand Down Expand Up @@ -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,
Expand Down