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78d96bbf
编写于
4月 07, 2021
作者:
S
SunAhong1993
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add instance_norm.py
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x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/instance_norm.py
...raph/pytorch2paddle/pytorch_custom_layer/instance_norm.py
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x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/instance_norm.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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
from
paddle.nn.functional
import
instance_norm
from
paddle.fluid.initializer
import
Constant
class
InstanceNorm
(
paddle
.
nn
.
Layer
):
"""
This class is based class for InstanceNorm1D, 2d, 3d.
See InstaceNorm1D, InstanceNorm2D or InstanceNorm3D for more details.
"""
def
__init__
(
self
,
num_features
,
epsilon
=
1e-5
,
momentum
=
0.9
,
weight_attr
=
None
,
bias_attr
=
None
,
data_format
=
"NCHW"
,
name
=
None
):
super
(
InstanceNorm
,
self
).
__init__
()
if
weight_attr
==
False
or
bias_attr
==
False
:
assert
weight_attr
==
bias_attr
,
"weight_attr and bias_attr must be set to Fasle at the same time in InstanceNorm"
self
.
_epsilon
=
epsilon
self
.
_weight_attr
=
weight_attr
self
.
_bias_attr
=
bias_attr
if
weight_attr
!=
False
and
bias_attr
!=
False
:
self
.
scale
=
self
.
create_parameter
(
attr
=
self
.
_weight_attr
,
shape
=
[
num_features
],
default_initializer
=
Constant
(
1.0
),
is_bias
=
False
)
self
.
bias
=
self
.
create_parameter
(
attr
=
self
.
_bias_attr
,
shape
=
[
num_features
],
default_initializer
=
Constant
(
0.0
),
is_bias
=
True
)
else
:
self
.
scale
=
None
self
.
bias
=
None
def
forward
(
self
,
input
):
return
instance_norm
(
input
,
weight
=
self
.
scale
,
bias
=
self
.
bias
,
eps
=
self
.
_epsilon
)
def
extra_repr
(
self
):
return
'num_features={}, epsilon={}'
.
format
(
self
.
scale
.
shape
[
0
],
self
.
_epsilon
)
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