31 lines
857 B
Python
31 lines
857 B
Python
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# coding=utf-8
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from collections import OrderedDict
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import torch.nn as nn
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import torch.nn.init as init
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def copyStateDict(state_dict):
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if list(state_dict.keys())[0].startswith("module"):
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start_idx = 1
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else:
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start_idx = 0
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new_state_dict = OrderedDict()
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for k, v in state_dict.items():
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name = ".".join(k.split(".")[start_idx:])
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new_state_dict[name] = v
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return new_state_dict
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def init_weights(modules):
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for m in modules:
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if isinstance(m, nn.Conv2d):
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init.xavier_uniform_(m.weight.data)
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if m.bias is not None:
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m.bias.data.zero_()
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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elif isinstance(m, nn.Linear):
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m.weight.data.normal_(0, 0.01)
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m.bias.data.zero_()
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