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A-Performance-Evaluation-of-CNN-for-Brain-Age-Prediction-Using-Structural-MRI-Data
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Authored by
Hyunji
2021-12-20 03:49:54 +0900
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Commit
9d3112d66279d307f556ee096d91804109a33702
9d3112d6
1 parent
e30d6c25
ResNet18, ResNet34, ResNet50
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3DCNN_VGGNet_2DResNet/model_resnet.py
3DCNN_VGGNet_2DResNet/model_resnet.py
0 → 100644
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9d3112d
import
torch.nn
as
nn
import
math
import
torch.utils.model_zoo
as
model_zoo
__all__
=
[
'ResNet'
,
'resnet18'
,
'resnet34'
,
'resnet50'
,
'resnet101'
,
'resnet152'
]
model_urls
=
{
'resnet18'
:
'https://download.pytorch.org/models/resnet18-5c106cde.pth'
,
'resnet34'
:
'https://download.pytorch.org/models/resnet34-333f7ec4.pth'
,
'resnet50'
:
'https://download.pytorch.org/models/resnet50-19c8e357.pth'
,
'resnet101'
:
'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'
,
'resnet152'
:
'https://download.pytorch.org/models/resnet152-b121ed2d.pth'
,
}
def
conv3x3
(
in_planes
,
out_planes
,
stride
=
1
):
"""3x3 convolution with padding"""
return
nn
.
Conv2d
(
in_planes
,
out_planes
,
kernel_size
=
3
,
stride
=
stride
,
padding
=
1
,
bias
=
False
)
class
BasicBlock
(
nn
.
Module
):
expansion
=
1
def
__init__
(
self
,
inplanes
,
planes
,
stride
=
1
,
downsample
=
None
):
super
(
BasicBlock
,
self
)
.
__init__
()
self
.
conv1
=
conv3x3
(
inplanes
,
planes
,
stride
)
#alter batchnorm2d to groupnorm
self
.
bn1
=
nn
.
BatchNorm2d
(
planes
)
self
.
relu
=
nn
.
ReLU
(
inplace
=
True
)
self
.
conv2
=
conv3x3
(
planes
,
planes
)
self
.
bn2
=
nn
.
BatchNorm2d
(
planes
)
self
.
downsample
=
downsample
self
.
stride
=
stride
def
forward
(
self
,
x
):
residual
=
x
out
=
self
.
conv1
(
x
)
out
=
self
.
bn1
(
out
)
out
=
self
.
relu
(
out
)
out
=
self
.
conv2
(
out
)
out
=
self
.
bn2
(
out
)
if
self
.
downsample
is
not
None
:
residual
=
self
.
downsample
(
x
)
out
+=
residual
out
=
self
.
relu
(
out
)
return
out
class
Bottleneck
(
nn
.
Module
):
expansion
=
4
def
__init__
(
self
,
inplanes
,
planes
,
stride
=
1
,
downsample
=
None
):
super
(
Bottleneck
,
self
)
.
__init__
()
self
.
conv1
=
nn
.
Conv2d
(
inplanes
,
planes
,
kernel_size
=
1
,
bias
=
False
)
self
.
bn1
=
nn
.
BatchNorm2d
(
planes
)
self
.
conv2
=
nn
.
Conv2d
(
planes
,
planes
,
kernel_size
=
3
,
stride
=
stride
,
padding
=
1
,
bias
=
False
)
self
.
bn2
=
nn
.
BatchNorm2d
(
planes
)
self
.
conv3
=
nn
.
Conv2d
(
planes
,
planes
*
4
,
kernel_size
=
1
,
bias
=
False
)
self
.
bn3
=
nn
.
BatchNorm2d
(
planes
*
4
)
self
.
relu
=
nn
.
ReLU
(
inplace
=
True
)
self
.
downsample
=
downsample
self
.
stride
=
stride
def
forward
(
self
,
x
):
residual
=
x
out
=
self
.
conv1
(
x
)
out
=
self
.
bn1
(
out
)
out
=
self
.
relu
(
out
)
out
=
self
.
conv2
(
out
)
out
=
self
.
bn2
(
out
)
out
=
self
.
relu
(
out
)
out
=
self
.
conv3
(
out
)
out
=
self
.
bn3
(
out
)
if
self
.
downsample
is
not
None
:
residual
=
self
.
downsample
(
x
)
out
+=
residual
out
=
self
.
relu
(
out
)
return
out
class
ResNet
(
nn
.
Module
):
#change num_classes to 1
def
__init__
(
self
,
block
,
layers
,
num_classes
=
1
):
self
.
inplanes
=
64
super
(
ResNet
,
self
)
.
__init__
()
#first param changed to 1
self
.
conv1
=
nn
.
Conv2d
(
2
,
64
,
kernel_size
=
7
,
stride
=
2
,
padding
=
3
,
bias
=
False
)
self
.
bn1
=
nn
.
BatchNorm2d
(
64
)
self
.
relu
=
nn
.
ReLU
(
inplace
=
True
)
self
.
maxpool
=
nn
.
MaxPool2d
(
kernel_size
=
3
,
stride
=
2
,
padding
=
1
)
self
.
layer1
=
self
.
_make_layer
(
block
,
64
,
layers
[
0
])
self
.
layer2
=
self
.
_make_layer
(
block
,
128
,
layers
[
1
],
stride
=
2
)
self
.
layer3
=
self
.
_make_layer
(
block
,
256
,
layers
[
2
],
stride
=
2
)
self
.
layer4
=
self
.
_make_layer
(
block
,
512
,
layers
[
3
],
stride
=
2
)
#change kernel size from 7 to 4
self
.
avgpool
=
nn
.
AvgPool2d
(
4
,
stride
=
1
)
#change from 512 to 1024
self
.
fc
=
nn
.
Linear
(
1024
*
block
.
expansion
,
num_classes
)
for
m
in
self
.
modules
():
if
isinstance
(
m
,
nn
.
Conv2d
):
n
=
m
.
kernel_size
[
0
]
*
m
.
kernel_size
[
1
]
*
m
.
out_channels
m
.
weight
.
data
.
normal_
(
0
,
math
.
sqrt
(
2.
/
n
))
elif
isinstance
(
m
,
nn
.
BatchNorm2d
):
m
.
weight
.
data
.
fill_
(
1
)
m
.
bias
.
data
.
zero_
()
def
_make_layer
(
self
,
block
,
planes
,
blocks
,
stride
=
1
):
downsample
=
None
if
stride
!=
1
or
self
.
inplanes
!=
planes
*
block
.
expansion
:
downsample
=
nn
.
Sequential
(
nn
.
Conv2d
(
self
.
inplanes
,
planes
*
block
.
expansion
,
kernel_size
=
1
,
stride
=
stride
,
bias
=
False
),
nn
.
BatchNorm2d
(
planes
*
block
.
expansion
),
)
layers
=
[]
layers
.
append
(
block
(
self
.
inplanes
,
planes
,
stride
,
downsample
))
self
.
inplanes
=
planes
*
block
.
expansion
for
i
in
range
(
1
,
blocks
):
layers
.
append
(
block
(
self
.
inplanes
,
planes
))
return
nn
.
Sequential
(
*
layers
)
def
forward
(
self
,
x
):
x
=
self
.
conv1
(
x
)
x
=
self
.
bn1
(
x
)
x
=
self
.
relu
(
x
)
x
=
self
.
maxpool
(
x
)
x
=
self
.
layer1
(
x
)
x
=
self
.
layer2
(
x
)
x
=
self
.
layer3
(
x
)
x
=
self
.
layer4
(
x
)
# print(x.shape) # --> [32,512,4,5]
x
=
self
.
avgpool
(
x
)
# print(x.shape) # --> [32,512,1,2], kernel = 4
x
=
x
.
view
(
x
.
size
(
0
),
-
1
)
# --> [32x1024]
x
=
self
.
fc
(
x
)
return
x
def
resnet18
(
pretrained
=
False
,
**
kwargs
):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model
=
ResNet
(
BasicBlock
,
[
2
,
2
,
2
,
2
],
**
kwargs
)
if
pretrained
:
#model.load_state_dict(model_zoo.load_url(model_urls['resnet18'])) #
state_dict
=
model_zoo
.
load_url
(
model_urls
[
'resnet18'
])
from
collections
import
OrderedDict
new_state_dict
=
OrderedDict
()
for
k
,
v
in
state_dict
.
items
():
if
'module'
not
in
k
:
k
=
'module.'
+
k
else
:
k
=
k
.
replace
(
'features.module.'
,
'module.features.'
)
new_state_dict
[
k
]
=
v
model
.
load_state_dict
(
new_state_dict
)
return
model
def
resnet34
(
pretrained
=
False
,
**
kwargs
):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model
=
ResNet
(
BasicBlock
,
[
3
,
4
,
6
,
3
],
**
kwargs
)
if
pretrained
:
model
.
load_state_dict
(
model_zoo
.
load_url
(
model_urls
[
'resnet34'
]))
return
model
def
resnet50
(
pretrained
=
False
,
**
kwargs
):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model
=
ResNet
(
Bottleneck
,
[
3
,
4
,
6
,
3
],
**
kwargs
)
if
pretrained
:
model
.
load_state_dict
(
model_zoo
.
load_url
(
model_urls
[
'resnet50'
]))
return
model
def
resnet101
(
pretrained
=
False
,
**
kwargs
):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model
=
ResNet
(
Bottleneck
,
[
3
,
4
,
23
,
3
],
**
kwargs
)
if
pretrained
:
model
.
load_state_dict
(
model_zoo
.
load_url
(
model_urls
[
'resnet101'
]))
return
model
def
resnet152
(
pretrained
=
False
,
**
kwargs
):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model
=
ResNet
(
Bottleneck
,
[
3
,
8
,
36
,
3
],
**
kwargs
)
if
pretrained
:
model
.
load_state_dict
(
model_zoo
.
load_url
(
model_urls
[
'resnet152'
]))
return
model
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