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23d69271
编写于
9月 22, 2020
作者:
W
wuzewu
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2 changed file
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220 addition
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3 deletion
+220
-3
dygraph/paddleseg/models/danet.py
dygraph/paddleseg/models/danet.py
+217
-0
dygraph/paddleseg/models/ocrnet.py
dygraph/paddleseg/models/ocrnet.py
+3
-3
未找到文件。
dygraph/paddleseg/models/danet.py
0 → 100644
浏览文件 @
23d69271
# 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
os
import
paddle
import
paddle.nn
as
nn
import
paddle.nn.functional
as
F
from
paddleseg.utils
import
utils
from
paddleseg.cvlibs
import
manager
,
param_init
from
paddleseg.models.common.layer_libs
import
ConvBNReLU
class
PAM
(
nn
.
Layer
):
"""Position attention module"""
def
__init__
(
self
,
in_channels
):
super
(
PAM
,
self
).
__init__
()
mid_channels
=
in_channels
//
8
self
.
query_conv
=
nn
.
Conv2d
(
in_channels
,
mid_channels
,
1
,
1
)
self
.
key_conv
=
nn
.
Conv2d
(
in_channels
,
mid_channels
,
1
,
1
)
self
.
value_conv
=
nn
.
Conv2d
(
in_channels
,
in_channels
,
1
,
1
)
self
.
gamma
=
self
.
create_parameter
(
shape
=
[
1
],
dtype
=
'float32'
,
default_initializer
=
nn
.
initializer
.
Constant
(
0
))
def
forward
(
self
,
x
):
n
,
_
,
h
,
w
=
x
.
shape
# query: n, h * w, c1
query
=
self
.
query_conv
(
x
)
query
=
paddle
.
reshape
(
query
,
(
n
,
-
1
,
h
*
w
))
query
=
paddle
.
transpose
(
query
,
(
0
,
2
,
1
))
# key: n, c1, h * w
key
=
self
.
key_conv
(
x
)
key
=
paddle
.
reshape
(
key
,
(
n
,
-
1
,
h
*
w
))
# sim: n, h * w, h * w
sim
=
paddle
.
bmm
(
query
,
key
)
sim
=
F
.
softmax
(
sim
,
axis
=-
1
)
value
=
self
.
value_conv
(
x
)
value
=
paddle
.
reshape
(
value
,
(
n
,
-
1
,
h
*
w
))
sim
=
paddle
.
transpose
(
sim
,
(
0
,
2
,
1
))
# feat: from (n, c2, h * w) -> (n, c2, h, w)
feat
=
paddle
.
bmm
(
value
,
sim
)
feat
=
paddle
.
reshape
(
feat
,
(
n
,
-
1
,
h
,
w
))
out
=
self
.
gamma
*
feat
+
x
return
out
class
CAM
(
nn
.
Layer
):
"""Channel attention module"""
def
__init__
(
self
):
super
(
CAM
,
self
).
__init__
()
self
.
gamma
=
self
.
create_parameter
(
shape
=
[
1
],
dtype
=
'float32'
,
default_initializer
=
nn
.
initializer
.
Constant
(
0
))
def
forward
(
self
,
x
):
n
,
c
,
h
,
w
=
x
.
shape
# query: n, c, h * w
query
=
paddle
.
reshape
(
x
,
(
n
,
c
,
h
*
w
))
# key: n, h * w, c
key
=
paddle
.
reshape
(
x
,
(
n
,
c
,
h
*
w
))
key
=
paddle
.
transpose
(
key
,
(
0
,
2
,
1
))
# sim: n, c, c
sim
=
paddle
.
bmm
(
query
,
key
)
# The danet author claims that this can avoid gradient divergence
sim
=
paddle
.
max
(
sim
,
axis
=-
1
,
keepdim
=
True
).
expand_as
(
sim
)
-
sim
sim
=
F
.
softmax
(
sim
,
axis
=-
1
)
# feat: from (n, c, h * w) to (n, c, h, w)
value
=
paddle
.
reshape
(
x
,
(
n
,
c
,
h
*
w
))
feat
=
paddle
.
bmm
(
sim
,
value
)
feat
=
paddle
.
reshape
(
feat
,
(
n
,
c
,
h
,
w
))
out
=
self
.
gamma
*
feat
+
x
return
out
class
DAHead
(
nn
.
Layer
):
"""
The Dual attention head.
Args:
num_classes(int): the unique number of target classes.
in_channels(tuple): the number of input channels.
"""
def
__init__
(
self
,
num_classes
,
in_channels
=
None
):
super
(
DAHead
,
self
).
__init__
()
in_channels
=
in_channels
[
-
1
]
inter_channels
=
in_channels
//
4
self
.
channel_conv
=
ConvBNReLU
(
in_channels
,
inter_channels
,
3
,
padding
=
1
)
self
.
position_conv
=
ConvBNReLU
(
in_channels
,
inter_channels
,
3
,
padding
=
1
)
self
.
pam
=
PAM
(
inter_channels
)
self
.
cam
=
CAM
()
self
.
conv1
=
ConvBNReLU
(
inter_channels
,
inter_channels
,
3
,
padding
=
1
)
self
.
conv2
=
ConvBNReLU
(
inter_channels
,
inter_channels
,
3
,
padding
=
1
)
self
.
aux_head_pam
=
nn
.
Sequential
(
nn
.
Dropout2d
(
0.1
),
nn
.
Conv2d
(
inter_channels
,
num_classes
,
1
))
self
.
aux_head_cam
=
nn
.
Sequential
(
nn
.
Dropout2d
(
0.1
),
nn
.
Conv2d
(
inter_channels
,
num_classes
,
1
))
self
.
cls_head
=
nn
.
Sequential
(
nn
.
Dropout2d
(
0.1
),
nn
.
Conv2d
(
inter_channels
,
num_classes
,
1
))
self
.
init_weight
()
def
forward
(
self
,
x
,
label
=
None
):
feats
=
x
[
-
1
]
channel_feats
=
self
.
channel_conv
(
feats
)
channel_feats
=
self
.
cam
(
channel_feats
)
channel_feats
=
self
.
conv1
(
channel_feats
)
cam_head
=
self
.
aux_head_cam
(
channel_feats
)
position_feats
=
self
.
position_conv
(
feats
)
position_feats
=
self
.
pam
(
position_feats
)
position_feats
=
self
.
conv2
(
position_feats
)
pam_head
=
self
.
aux_head_pam
(
position_feats
)
feats_sum
=
position_feats
+
channel_feats
cam_logit
=
self
.
aux_head_cam
(
channel_feats
)
pam_logit
=
self
.
aux_head_cam
(
position_feats
)
logit
=
self
.
cls_head
(
feats_sum
)
return
[
logit
,
cam_logit
,
pam_logit
]
def
init_weight
(
self
):
"""Initialize the parameters of model parts."""
for
sublayer
in
self
.
sublayers
():
if
isinstance
(
sublayer
,
nn
.
Conv2d
):
param_init
.
normal_init
(
sublayer
.
weight
,
scale
=
0.001
)
elif
isinstance
(
sublayer
,
nn
.
SyncBatchNorm
):
param_init
.
constant_init
(
sublayer
.
weight
,
value
=
1
)
param_init
.
constant_init
(
sublayer
.
bias
,
value
=
0
)
@
manager
.
MODELS
.
add_component
class
DANet
(
nn
.
Layer
):
"""
The DANet implementation based on PaddlePaddle.
The original article refers to
Fu, jun, et al. "Dual Attention Network for Scene Segmentation"
(https://arxiv.org/pdf/1809.02983.pdf)
Args:
num_classes(int): the unique number of target classes.
backbone(Paddle.nn.Layer): backbone network.
pretrained(str): the path or url of pretrained model. Default to None.
backbone_indices(tuple): values in the tuple indicate the indices of output of backbone.
Only the last indice is used.
"""
def
__init__
(
self
,
num_classes
,
backbone
,
pretrained
=
None
,
backbone_indices
=
None
):
super
(
DANet
,
self
).
__init__
()
self
.
backbone
=
backbone
self
.
backbone_indices
=
backbone_indices
in_channels
=
[
self
.
backbone
.
channels
[
i
]
for
i
in
backbone_indices
]
self
.
head
=
DAHead
(
num_classes
=
num_classes
,
in_channels
=
in_channels
)
self
.
init_weight
(
pretrained
)
def
forward
(
self
,
x
,
label
=
None
):
feats
=
self
.
backbone
(
x
)
feats
=
[
feats
[
i
]
for
i
in
self
.
backbone_indices
]
preds
=
self
.
head
(
feats
,
label
)
preds
=
[
F
.
resize_bilinear
(
pred
,
x
.
shape
[
2
:])
for
pred
in
preds
]
return
preds
def
init_weight
(
self
,
pretrained
=
None
):
"""
Initialize the parameters of model parts.
Args:
pretrained ([str], optional): the path of pretrained model.. Defaults to None.
"""
if
pretrained
is
not
None
:
if
os
.
path
.
exists
(
pretrained
):
utils
.
load_pretrained_model
(
self
,
pretrained
)
else
:
raise
Exception
(
'Pretrained model is not found: {}'
.
format
(
pretrained
))
dygraph/paddleseg/models/ocrnet.py
浏览文件 @
23d69271
...
...
@@ -123,7 +123,7 @@ class ObjectAttentionBlock(nn.Layer):
class
OCRHead
(
nn
.
Layer
):
"""
The O
CR H
ead.
The O
bject contextual representation h
ead.
Args:
num_classes(int): the unique number of target classes.
...
...
@@ -180,14 +180,14 @@ class OCRNet(nn.Layer):
"""
The OCRNet implementation based on PaddlePaddle.
The or
ginal arti
le refers to
The or
iginal artic
le refers to
Yuan, Yuhui, et al. "Object-Contextual Representations for Semantic Segmentation"
(https://arxiv.org/pdf/1909.11065.pdf)
Args:
num_classes(int): the unique number of target classes.
backbone(Paddle.nn.Layer): backbone network.
pretrained(str): the path or url of pretrained model. Defaul
l
t to None.
pretrained(str): the path or url of pretrained model. Default to None.
backbone_indices(tuple): two values in the tuple indicate the indices of output of backbone.
the first index will be taken as a deep-supervision feature in auxiliary layer;
the second one will be taken as input of pixel representation.
...
...
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