| |
| import torch |
| from torch.nn.modules import GroupNorm |
| from torch.nn.modules.batchnorm import _BatchNorm |
|
|
| from mmseg.models.backbones.resnet import BasicBlock, Bottleneck |
| from mmseg.models.backbones.resnext import Bottleneck as BottleneckX |
|
|
|
|
| def is_block(modules): |
| """Check if is ResNet building block.""" |
| if isinstance(modules, (BasicBlock, Bottleneck, BottleneckX)): |
| return True |
| return False |
|
|
|
|
| def is_norm(modules): |
| """Check if is one of the norms.""" |
| if isinstance(modules, (GroupNorm, _BatchNorm)): |
| return True |
| return False |
|
|
|
|
| def all_zeros(modules): |
| """Check if the weight(and bias) is all zero.""" |
| weight_zero = torch.allclose(modules.weight.data, |
| torch.zeros_like(modules.weight.data)) |
| if hasattr(modules, 'bias'): |
| bias_zero = torch.allclose(modules.bias.data, |
| torch.zeros_like(modules.bias.data)) |
| else: |
| bias_zero = True |
|
|
| return weight_zero and bias_zero |
|
|
|
|
| def check_norm_state(modules, train_state): |
| """Check if norm layer is in correct train state.""" |
| for mod in modules: |
| if isinstance(mod, _BatchNorm): |
| if mod.training != train_state: |
| return False |
| return True |
|
|
