69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
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# Copyright 2021 Samsung Electronics Co., Ltd.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =============================================================================
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import torch.nn as nn
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def init_net(net, w_type, b_type):
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if w_type == 'none':
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pass
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elif w_type == 'xavier':
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net.apply(init_weights_vs)
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elif w_type == 'kaiming':
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net.apply(init_weights_he)
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elif w_type == 'zero':
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net.apply(init_weights_zero)
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else:
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raise NotImplementedError(f'init_type={w_type} is not supported.')
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if b_type == 'none':
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pass
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elif b_type == 'xavier':
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net.apply(init_bias_vs)
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elif b_type == 'kaiming':
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net.apply(init_bias_he)
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elif b_type == 'zero':
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net.apply(init_bias_zero)
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else:
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raise NotImplementedError(f'init_type={b_type} is not supported.')
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def init_weights_vs(m):
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if type(m) == nn.Linear or type(m) == nn.Conv2d:
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nn.init.xavier_normal_(m.weight)
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def init_bias_vs(m):
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if type(m) == nn.Linear or type(m) == nn.Conv2d:
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if m.bias is not None:
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nn.init.xavier_normal_(m.bias)
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def init_weights_he(m):
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if type(m) == nn.Linear or type(m) == nn.Conv2d:
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nn.init.kaiming_normal_(m.weight)
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def init_bias_he(m):
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if type(m) == nn.Linear or type(m) == nn.Conv2d:
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if m.bias is not None:
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nn.init.kaiming_normal_(m.bias)
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def init_weights_zero(m):
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if type(m) == nn.Linear or type(m) == nn.Conv2d:
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m.weight.data.fill_(.0)
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def init_bias_zero(m):
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if type(m) == nn.Linear or type(m) == nn.Conv2d:
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if m.bias is not None:
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m.bias.data.fill_(.0)
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