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Commits
b5659d11
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b5659d11
authored
7月 01, 2020
作者:
Glenn Jocher
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
module updates
上级
86784cfd
隐藏空白字符变更
内嵌
并排
正在显示
2 个修改的文件
包含
44 行增加
和
7 行删除
+44
-7
common.py
models/common.py
+10
-7
experimental.py
models/experimental.py
+34
-0
没有找到文件。
models/common.py
浏览文件 @
b5659d11
# This file contains modules common to various models
from
utils.utils
import
*
def
autopad
(
k
):
# Pad to 'same'
return
k
//
2
if
isinstance
(
k
,
int
)
else
[
x
//
2
for
x
in
k
]
# auto-pad
def
DWConv
(
c1
,
c2
,
k
=
1
,
s
=
1
,
act
=
True
):
# Depthwise convolution
return
Conv
(
c1
,
c2
,
k
,
s
,
g
=
math
.
gcd
(
c1
,
c2
),
act
=
act
)
...
...
@@ -11,10 +15,9 @@ def DWConv(c1, c2, k=1, s=1, act=True):
class
Conv
(
nn
.
Module
):
# Standard convolution
def
__init__
(
self
,
c1
,
c2
,
k
=
1
,
s
=
1
,
g
=
1
,
act
=
True
):
# ch_in, ch_out, kernel, stride
, groups
def
__init__
(
self
,
c1
,
c2
,
k
=
1
,
s
=
1
,
p
=
None
,
g
=
1
,
act
=
True
):
# ch_in, ch_out, kernel, stride, padding
, groups
super
(
Conv
,
self
)
.
__init__
()
p
=
k
//
2
if
isinstance
(
k
,
int
)
else
[
x
//
2
for
x
in
k
]
# padding
self
.
conv
=
nn
.
Conv2d
(
c1
,
c2
,
k
,
s
,
p
,
groups
=
g
,
bias
=
False
)
self
.
conv
=
nn
.
Conv2d
(
c1
,
c2
,
k
,
s
,
p
or
autopad
(
k
),
groups
=
g
,
bias
=
False
)
self
.
bn
=
nn
.
BatchNorm2d
(
c2
)
self
.
act
=
nn
.
LeakyReLU
(
0.1
,
inplace
=
True
)
if
act
else
nn
.
Identity
()
...
...
@@ -46,7 +49,7 @@ class BottleneckCSP(nn.Module):
self
.
cv1
=
Conv
(
c1
,
c_
,
1
,
1
)
self
.
cv2
=
nn
.
Conv2d
(
c1
,
c_
,
1
,
1
,
bias
=
False
)
self
.
cv3
=
nn
.
Conv2d
(
c_
,
c_
,
1
,
1
,
bias
=
False
)
self
.
cv4
=
Conv
(
c2
,
c2
,
1
,
1
)
self
.
cv4
=
Conv
(
2
*
c_
,
c2
,
1
,
1
)
self
.
bn
=
nn
.
BatchNorm2d
(
2
*
c_
)
# applied to cat(cv2, cv3)
self
.
act
=
nn
.
LeakyReLU
(
0.1
,
inplace
=
True
)
self
.
m
=
nn
.
Sequential
(
*
[
Bottleneck
(
c_
,
c_
,
shortcut
,
g
,
e
=
1.0
)
for
_
in
range
(
n
)])
...
...
@@ -79,9 +82,9 @@ class Flatten(nn.Module):
class
Focus
(
nn
.
Module
):
# Focus wh information into c-space
def
__init__
(
self
,
c1
,
c2
,
k
=
1
):
def
__init__
(
self
,
c1
,
c2
,
k
=
1
,
s
=
1
,
p
=
None
,
g
=
1
,
act
=
True
):
# ch_in, ch_out, kernel, stride, padding, groups
super
(
Focus
,
self
)
.
__init__
()
self
.
conv
=
Conv
(
c1
*
4
,
c2
,
k
,
1
)
self
.
conv
=
Conv
(
c1
*
4
,
c2
,
k
,
s
,
p
,
g
,
act
)
def
forward
(
self
,
x
):
# x(b,c,w,h) -> y(b,4c,w/2,h/2)
return
self
.
conv
(
torch
.
cat
([
x
[
...
,
::
2
,
::
2
],
x
[
...
,
1
::
2
,
::
2
],
x
[
...
,
::
2
,
1
::
2
],
x
[
...
,
1
::
2
,
1
::
2
]],
1
))
...
...
models/experimental.py
浏览文件 @
b5659d11
# This file contains experimental modules
from
models.common
import
*
class
CrossConv
(
nn
.
Module
):
# Cross Convolution
def
__init__
(
self
,
c1
,
c2
,
shortcut
=
True
,
g
=
1
,
e
=
0.5
):
# ch_in, ch_out, shortcut, groups, expansion
super
(
CrossConv
,
self
)
.
__init__
()
c_
=
int
(
c2
*
e
)
# hidden channels
self
.
cv1
=
Conv
(
c1
,
c_
,
(
1
,
3
),
1
)
self
.
cv2
=
Conv
(
c_
,
c2
,
(
3
,
1
),
1
,
g
=
g
)
self
.
add
=
shortcut
and
c1
==
c2
def
forward
(
self
,
x
):
return
x
+
self
.
cv2
(
self
.
cv1
(
x
))
if
self
.
add
else
self
.
cv2
(
self
.
cv1
(
x
))
class
C3
(
nn
.
Module
):
# Cross Convolution CSP
def
__init__
(
self
,
c1
,
c2
,
n
=
1
,
shortcut
=
True
,
g
=
1
,
e
=
0.5
):
# ch_in, ch_out, number, shortcut, groups, expansion
super
(
C3
,
self
)
.
__init__
()
c_
=
int
(
c2
*
e
)
# hidden channels
self
.
cv1
=
Conv
(
c1
,
c_
,
1
,
1
)
self
.
cv2
=
nn
.
Conv2d
(
c1
,
c_
,
1
,
1
,
bias
=
False
)
self
.
cv3
=
nn
.
Conv2d
(
c_
,
c_
,
1
,
1
,
bias
=
False
)
self
.
cv4
=
Conv
(
2
*
c_
,
c2
,
1
,
1
)
self
.
bn
=
nn
.
BatchNorm2d
(
2
*
c_
)
# applied to cat(cv2, cv3)
self
.
act
=
nn
.
LeakyReLU
(
0.1
,
inplace
=
True
)
self
.
m
=
nn
.
Sequential
(
*
[
CrossConv
(
c_
,
c_
,
shortcut
,
g
,
e
=
1.0
)
for
_
in
range
(
n
)])
def
forward
(
self
,
x
):
y1
=
self
.
cv3
(
self
.
m
(
self
.
cv1
(
x
)))
y2
=
self
.
cv2
(
x
)
return
self
.
cv4
(
self
.
act
(
self
.
bn
(
torch
.
cat
((
y1
,
y2
),
dim
=
1
))))
class
Sum
(
nn
.
Module
):
# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
def
__init__
(
self
,
n
,
weight
=
False
):
# n: number of inputs
...
...
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