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63 lines
2.2 KiB
63 lines
2.2 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. |
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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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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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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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import paddle |
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import functools |
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import paddle.nn as nn |
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from .nn import Spectralnorm |
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class Identity(nn.Layer): |
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def forward(self, x): |
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return x |
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def build_norm_layer(norm_type='instance'): |
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"""Return a normalization layer |
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Args: |
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norm_type (str) -- the name of the normalization layer: batch | instance | none |
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For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev). |
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For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics. |
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""" |
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if norm_type == 'batch': |
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norm_layer = functools.partial( |
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nn.BatchNorm, |
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param_attr=paddle.ParamAttr( |
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initializer=nn.initializer.Normal(1.0, 0.02)), |
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bias_attr=paddle.ParamAttr( |
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initializer=nn.initializer.Constant(0.0)), |
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trainable_statistics=True) |
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elif norm_type == 'instance': |
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norm_layer = functools.partial( |
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nn.InstanceNorm2D, |
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weight_attr=paddle.ParamAttr( |
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initializer=nn.initializer.Constant(1.0), |
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learning_rate=0.0, |
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trainable=False), |
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bias_attr=paddle.ParamAttr( |
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initializer=nn.initializer.Constant(0.0), |
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learning_rate=0.0, |
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trainable=False)) |
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elif norm_type == 'spectral': |
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norm_layer = functools.partial(Spectralnorm) |
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elif norm_type == 'none': |
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def norm_layer(x): |
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return Identity() |
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else: |
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raise NotImplementedError('normalization layer [%s] is not found' % |
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norm_type) |
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return norm_layer
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