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pytorch-image-models
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前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
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2e955cfd
编写于
1月 05, 2020
作者:
R
Ross Wightman
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电子邮件补丁
差异文件
Update RandomErasing with some improved arg names, tweak to aspect range
上级
3cc0f91e
变更
1
隐藏空白更改
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并排
Showing
1 changed file
with
14 addition
and
14 deletion
+14
-14
timm/data/random_erasing.py
timm/data/random_erasing.py
+14
-14
未找到文件。
timm/data/random_erasing.py
浏览文件 @
2e955cfd
...
...
@@ -23,13 +23,13 @@ class RandomErasing:
This variant of RandomErasing is intended to be applied to either a batch
or single image tensor after it has been normalized by dataset mean and std.
Args:
probability:
The p
robability that the Random Erasing operation will be performed.
sl: Minimum proportion of erased area against input image
.
sh: Maximum proportion of erased area against input image
.
probability:
P
robability that the Random Erasing operation will be performed.
min_area: Minimum percentage of erased area wrt input image area
.
max_area: Maximum percentage of erased area wrt input image area
.
min_aspect: Minimum aspect ratio of erased area.
mode: pixel color mode, one of 'const', 'rand', or 'pixel'
'const' - erase block is constant color of 0 for all channels
'rand' - erase block is same per-cannel random (normal) color
'rand' - erase block is same per-c
h
annel random (normal) color
'pixel' - erase block is per-pixel random (normal) color
max_count: maximum number of erasing blocks per image, area per box is scaled by count.
per-image count is randomly chosen between 1 and this value.
...
...
@@ -37,14 +37,15 @@ class RandomErasing:
def
__init__
(
self
,
probability
=
0.5
,
sl
=
0.02
,
sh
=
1
/
3
,
min_aspect
=
0.3
,
mode
=
'const'
,
m
ax_count
=
1
,
device
=
'cuda'
):
probability
=
0.5
,
min_area
=
0.02
,
max_area
=
1
/
3
,
min_aspect
=
0.3
,
max_aspect
=
None
,
mode
=
'const'
,
m
in_count
=
1
,
max_count
=
None
,
device
=
'cuda'
):
self
.
probability
=
probability
self
.
sl
=
sl
self
.
sh
=
sh
self
.
min_aspect
=
min_aspect
self
.
min_count
=
1
self
.
max_count
=
max_count
self
.
min_area
=
min_area
self
.
max_area
=
max_area
max_aspect
=
max_aspect
or
1
/
min_aspect
self
.
log_aspect_ratio
=
(
math
.
log
(
min_aspect
),
math
.
log
(
max_aspect
))
self
.
min_count
=
min_count
self
.
max_count
=
max_count
or
min_count
mode
=
mode
.
lower
()
self
.
rand_color
=
False
self
.
per_pixel
=
False
...
...
@@ -64,9 +65,8 @@ class RandomErasing:
random
.
randint
(
self
.
min_count
,
self
.
max_count
)
for
_
in
range
(
count
):
for
attempt
in
range
(
10
):
target_area
=
random
.
uniform
(
self
.
sl
,
self
.
sh
)
*
area
/
count
log_ratio
=
(
math
.
log
(
self
.
min_aspect
),
math
.
log
(
1
/
self
.
min_aspect
))
aspect_ratio
=
math
.
exp
(
random
.
uniform
(
*
log_ratio
))
target_area
=
random
.
uniform
(
self
.
min_area
,
self
.
max_area
)
*
area
/
count
aspect_ratio
=
math
.
exp
(
random
.
uniform
(
*
self
.
log_aspect_ratio
))
h
=
int
(
round
(
math
.
sqrt
(
target_area
*
aspect_ratio
)))
w
=
int
(
round
(
math
.
sqrt
(
target_area
/
aspect_ratio
)))
if
w
<
img_w
and
h
<
img_h
:
...
...
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