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from torch.nn.modules import GroupNorm |
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from torch.nn.modules.batchnorm import _BatchNorm |
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from mmdet.models.backbones.res2net import Bottle2neck |
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from mmdet.models.backbones.resnet import BasicBlock, Bottleneck |
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from mmdet.models.backbones.resnext import Bottleneck as BottleneckX |
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from mmdet.models.utils import SimplifiedBasicBlock |
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def is_block(modules): |
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"""Check if is ResNet building block.""" |
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if isinstance(modules, (BasicBlock, Bottleneck, BottleneckX, Bottle2neck, |
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SimplifiedBasicBlock)): |
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return True |
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return False |
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def is_norm(modules): |
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"""Check if is one of the norms.""" |
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if isinstance(modules, (GroupNorm, _BatchNorm)): |
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return True |
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return False |
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def check_norm_state(modules, train_state): |
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"""Check if norm layer is in correct train state.""" |
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for mod in modules: |
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if isinstance(mod, _BatchNorm): |
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if mod.training != train_state: |
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return False |
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return True |
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