You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
63 lines
2.2 KiB
63 lines
2.2 KiB
3 years ago
|
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
||
|
|
#
|
||
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||
|
|
# you may not use this file except in compliance with the License.
|
||
|
|
# You may obtain a copy of the License at
|
||
|
|
#
|
||
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
#
|
||
|
|
# Unless required by applicable law or agreed to in writing, software
|
||
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||
|
|
# See the License for the specific language governing permissions and
|
||
|
|
# limitations under the License.
|
||
|
|
|
||
|
|
import paddle
|
||
|
|
import functools
|
||
|
|
import paddle.nn as nn
|
||
|
|
from .nn import Spectralnorm
|
||
|
|
|
||
|
|
|
||
|
|
class Identity(nn.Layer):
|
||
|
|
def forward(self, x):
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
def build_norm_layer(norm_type='instance'):
|
||
|
|
"""Return a normalization layer
|
||
|
|
|
||
|
|
Args:
|
||
|
|
norm_type (str) -- the name of the normalization layer: batch | instance | none
|
||
|
|
|
||
|
|
For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
|
||
|
|
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
|
||
|
|
"""
|
||
|
|
if norm_type == 'batch':
|
||
|
|
norm_layer = functools.partial(
|
||
|
|
nn.BatchNorm,
|
||
|
|
param_attr=paddle.ParamAttr(
|
||
|
|
initializer=nn.initializer.Normal(1.0, 0.02)),
|
||
|
|
bias_attr=paddle.ParamAttr(
|
||
|
|
initializer=nn.initializer.Constant(0.0)),
|
||
|
|
trainable_statistics=True)
|
||
|
|
elif norm_type == 'instance':
|
||
|
|
norm_layer = functools.partial(
|
||
|
|
nn.InstanceNorm2D,
|
||
|
|
weight_attr=paddle.ParamAttr(
|
||
|
|
initializer=nn.initializer.Constant(1.0),
|
||
|
|
learning_rate=0.0,
|
||
|
|
trainable=False),
|
||
|
|
bias_attr=paddle.ParamAttr(initializer=nn.initializer.Constant(0.0),
|
||
|
|
learning_rate=0.0,
|
||
|
|
trainable=False))
|
||
|
|
elif norm_type == 'spectral':
|
||
|
|
norm_layer = functools.partial(Spectralnorm)
|
||
|
|
elif norm_type == 'none':
|
||
|
|
|
||
|
|
def norm_layer(x):
|
||
|
|
return Identity()
|
||
|
|
else:
|
||
|
|
raise NotImplementedError('normalization layer [%s] is not found' %
|
||
|
|
norm_type)
|
||
|
|
return norm_layer
|