44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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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 mmseg.models.backbones.resnet import BasicBlock, Bottleneck
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from mmseg.models.backbones.resnext import Bottleneck as BottleneckX
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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)):
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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 all_zeros(modules):
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"""Check if the weight(and bias) is all zero."""
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weight_zero = torch.allclose(modules.weight.data,
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torch.zeros_like(modules.weight.data))
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if hasattr(modules, 'bias'):
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bias_zero = torch.allclose(modules.bias.data,
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torch.zeros_like(modules.bias.data))
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else:
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bias_zero = True
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return weight_zero and bias_zero
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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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