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Mathieu Léonardon
explore
Commits
b6536178
Commit
b6536178
authored
10 months ago
by
Mathieu Léonardon
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Change resnet definition.
parent
5fd98cb8
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2 changed files
main.py
+21
-6
21 additions, 6 deletions
main.py
models/resnet.py
+19
-38
19 additions, 38 deletions
models/resnet.py
with
40 additions
and
44 deletions
main.py
+
21
−
6
View file @
b6536178
...
...
@@ -9,8 +9,7 @@ import torch
import
torch.nn
as
nn
import
torch.optim
as
optim
from
torch.utils.data
import
DataLoader
from
models.resnet
import
ResNet18
# from quick_test import ResNet
from
models.resnet
import
ResNet
import
argparse
import
wandb
import
torchinfo
...
...
@@ -35,6 +34,9 @@ def main(args):
momentum
=
wandb
.
config
[
'
momentum
'
]
weight_decay
=
wandb
.
config
[
'
weight_decay
'
]
mixup
=
wandb
.
config
[
'
mixup
'
]
depth
=
wandb
.
config
[
'
depth
'
]
width
=
wandb
.
config
[
'
width
'
]
groups
=
wandb
.
config
[
'
groups
'
]
preproc
=
v2
.
Compose
([
v2
.
PILToTensor
(),
...
...
@@ -50,11 +52,22 @@ def main(args):
train_data
=
datasets
.
CIFAR10
(
data_path
,
train
=
True
,
download
=
True
,
transform
=
preproc
)
test_data
=
datasets
.
CIFAR10
(
data_path
,
train
=
False
,
download
=
True
,
transform
=
preproc
)
model
=
ResNet18
()
if
depth
==
18
:
model
=
ResNet
([(
width
,
1
,
[
groups
,
groups
]),
(
width
,
1
,
[
groups
,
groups
]),
(
width
*
2
,
2
,
[
groups
,
groups
]),
(
width
*
2
,
1
,
[
groups
,
groups
]),
(
width
*
4
,
2
,
[
groups
,
groups
]),
(
width
*
4
,
1
,
[
groups
,
groups
]),
(
width
*
8
,
2
,
[
groups
,
groups
]),
(
width
*
8
,
1
,
[
groups
,
groups
])])
elif
depth
==
14
:
model
=
ResNet
([(
width
,
1
,
[
groups
,
groups
]),
(
width
,
1
,
[
groups
,
groups
]),
(
width
*
2
,
2
,
[
groups
,
groups
]),
(
width
*
2
,
1
,
[
groups
,
groups
]),
(
width
*
4
,
2
,
[
groups
,
groups
]),
(
width
*
4
,
1
,
[
groups
,
groups
])])
elif
depth
==
8
:
model
=
ResNet
([(
width
,
1
,
[
groups
,
groups
]),
(
width
*
2
,
2
,
[
groups
,
groups
]),
(
width
*
4
,
2
,
[
groups
,
groups
])])
else
:
raise
ValueError
(
'
Invalid depth
'
)
epochs
=
150
torchinfo
.
summary
(
model
,
input_size
=
(
32
,
3
,
32
,
32
))
summary
=
torchinfo
.
summary
(
model
,
input_size
=
(
32
,
3
,
32
,
32
))
run
.
config
[
'
total_params
'
]
=
summary
.
total_params
run
.
config
[
'
mult_add
'
]
=
summary
.
total_mult_adds
collate_fn
=
conf_collate_fn
(
mixup
)
...
...
@@ -110,8 +123,7 @@ def main(args):
print
(
f
'
Epoch:
{
epoch
}
, Test Accuracy:
{
correct
/
total
}
'
)
model
.
train
()
# save ResNet-18 model
torch
.
save
(
model
.
state_dict
(),
'
resnet18.pth
'
)
torch
.
save
(
model
.
state_dict
(),
run
.
id
+
'
.pt
'
)
if
__name__
==
"
__main__
"
:
parser
=
argparse
.
ArgumentParser
(
...
...
@@ -124,6 +136,9 @@ if __name__ == "__main__":
parser
.
add_argument
(
"
-m
"
,
"
--momentum
"
,
type
=
int
,
default
=
0.9
,
help
=
"
Momentum
"
)
parser
.
add_argument
(
"
-wd
"
,
"
--weight_decay
"
,
type
=
int
,
default
=
5e-4
,
help
=
"
Weight decay
"
)
parser
.
add_argument
(
"
--mixup
"
,
action
=
"
store_true
"
,
help
=
"
Use MixUp data augmentation
"
)
parser
.
add_argument
(
"
--depth
"
,
type
=
int
,
default
=
18
,
help
=
"
ResNet depth
"
)
parser
.
add_argument
(
"
--width
"
,
type
=
int
,
default
=
64
,
help
=
"
ResNet width
"
)
parser
.
add_argument
(
"
--groups
"
,
type
=
int
,
default
=
1
,
help
=
"
ResNet groups
"
)
args
=
parser
.
parse_args
()
main
(
args
)
\ No newline at end of file
This diff is collapsed.
Click to expand it.
models/resnet.py
+
19
−
38
View file @
b6536178
import
torch
import
torch.nn
as
nn
import
torch.nn.functional
as
F
class
ResNetBlock
(
nn
.
Module
):
expansion
=
1
def
__init__
(
self
,
i
n_planes
,
planes
,
stride
=
1
):
def
__init__
(
self
,
i
fm
,
ofm
,
stride
=
1
,
groups
=
[
1
,
1
]
):
super
(
ResNetBlock
,
self
).
__init__
()
self
.
conv1
=
nn
.
Conv2d
(
in_planes
,
planes
,
kernel_size
=
3
,
stride
=
stride
,
padding
=
1
,
bias
=
False
)
self
.
bn1
=
nn
.
BatchNorm2d
(
planes
)
self
.
conv2
=
nn
.
Conv2d
(
planes
,
planes
,
kernel_size
=
3
,
stride
=
1
,
padding
=
1
,
bias
=
False
)
self
.
bn2
=
nn
.
BatchNorm2d
(
planes
)
self
.
conv1
=
nn
.
Conv2d
(
ifm
,
ofm
,
kernel_size
=
3
,
stride
=
stride
,
padding
=
1
,
groups
=
groups
[
0
],
bias
=
False
)
self
.
bn1
=
nn
.
BatchNorm2d
(
ofm
)
self
.
conv2
=
nn
.
Conv2d
(
ofm
,
ofm
,
kernel_size
=
3
,
stride
=
1
,
padding
=
1
,
groups
=
groups
[
0
],
bias
=
False
)
self
.
bn2
=
nn
.
BatchNorm2d
(
ofm
)
self
.
shortcut
=
nn
.
Sequential
()
if
stride
!=
1
or
i
n_planes
!=
self
.
expansion
*
planes
:
if
stride
!=
1
or
i
fm
!=
ofm
:
self
.
shortcut
=
nn
.
Sequential
(
nn
.
Conv2d
(
i
n_planes
,
self
.
expansion
*
planes
,
kernel_size
=
1
,
stride
=
stride
,
bias
=
False
),
nn
.
BatchNorm2d
(
self
.
expansion
*
planes
)
nn
.
Conv2d
(
i
fm
,
ofm
,
kernel_size
=
1
,
stride
=
stride
,
bias
=
False
),
nn
.
BatchNorm2d
(
ofm
)
)
def
forward
(
self
,
x
):
...
...
@@ -28,43 +27,25 @@ class ResNetBlock(nn.Module):
return
out
class
ResNet
(
nn
.
Module
):
def
__init__
(
self
,
blocks
,
num_classes
=
10
0
,
fmaps_repeat
=
16
):
def
__init__
(
self
,
blocks
,
num_classes
=
10
):
super
(
ResNet
,
self
).
__init__
()
self
.
in_planes
=
fmaps_repeat
self
.
fmaps_repeat
=
fmaps_repeat
self
.
conv1
=
nn
.
Conv2d
(
3
,
self
.
in_planes
,
kernel_size
=
3
,
stride
=
1
,
padding
=
1
,
bias
=
False
)
self
.
bn1
=
nn
.
BatchNorm2d
(
self
.
in_planes
)
self
.
ifm
=
blocks
[
0
][
0
]
self
.
conv1
=
nn
.
Conv2d
(
3
,
self
.
ifm
,
kernel_size
=
3
,
stride
=
1
,
padding
=
1
,
bias
=
False
)
self
.
bn1
=
nn
.
BatchNorm2d
(
self
.
ifm
)
blocks_array
=
[]
previous_fmaps
=
blocks
[
0
][
1
]
for
(
num_blocks
,
fmaps
,
stride
)
in
blocks
:
for
i
in
range
(
num_blocks
):
blocks_array
.
append
(
ResNetBlock
(
previous_fmaps
,
fmaps
,
stride
if
i
==
0
else
1
))
previous_fmaps
=
fmaps
previous_fmaps
=
blocks
[
0
][
0
]
for
(
fmaps
,
stride
,
groups
)
in
blocks
:
blocks_array
.
append
(
ResNetBlock
(
previous_fmaps
,
fmaps
,
stride
,
groups
))
previous_fmaps
=
fmaps
self
.
blocks
=
nn
.
ModuleList
(
blocks_array
)
self
.
linear
=
nn
.
Linear
(
blocks
[
-
1
][
1
],
num_classes
)
self
.
linear
=
nn
.
Linear
(
blocks
[
-
1
][
0
],
num_classes
)
def
forward
(
self
,
x
):
out
=
F
.
relu
(
self
.
bn1
(
self
.
conv1
(
x
)))
for
block
in
self
.
blocks
:
out
=
block
(
out
)
out
=
F
.
avg_pool2d
(
out
,
4
)
out
=
F
.
avg_pool2d
(
out
,
out
.
shape
[
2
]
)
out
=
out
.
view
(
out
.
size
(
0
),
-
1
)
out
=
self
.
linear
(
out
)
return
out
def
ResNet18
():
return
ResNet
([(
2
,
64
,
1
),
(
2
,
128
,
2
),
(
2
,
256
,
2
),
(
2
,
512
,
2
)],
num_classes
=
10
,
fmaps_repeat
=
64
)
# def ResNet34():
# return ResNet(BasicBlock, [3,4,6,3])
# def ResNet50():
# return ResNet(Bottleneck, [3,4,6,3])
# def ResNet101():
# return ResNet(Bottleneck, [3,4,23,3])
# def ResNet152():
# return ResNet(Bottleneck, [3,8,36,3])
\ No newline at end of file
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