Skip to content
项目
群组
代码片段
帮助
当前项目
正在载入...
登录 / 注册
切换导航面板
Y
yolov5
项目
项目
详情
活动
周期分析
仓库
仓库
文件
提交
分支
标签
贡献者
图表
比较
统计图
议题
0
议题
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
CI / CD
CI / CD
流水线
作业
日程
统计图
Wiki
Wiki
代码片段
代码片段
成员
成员
折叠边栏
关闭边栏
活动
图像
聊天
创建新问题
作业
提交
问题看板
Open sidebar
Administrator
yolov5
Commits
3373aab5
Unverified
提交
3373aab5
authored
3月 26, 2022
作者:
Glenn Jocher
提交者:
GitHub
3月 26, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
NMS unused variable fix (#7161)
* NMS unused variable fix * Update general.py
上级
e19f87eb
显示空白字符变更
内嵌
并排
正在显示
1 个修改的文件
包含
6 行增加
和
7 行删除
+6
-7
general.py
utils/general.py
+6
-7
没有找到文件。
utils/general.py
浏览文件 @
3373aab5
...
@@ -703,7 +703,7 @@ def clip_coords(boxes, shape):
...
@@ -703,7 +703,7 @@ def clip_coords(boxes, shape):
def
non_max_suppression
(
prediction
,
conf_thres
=
0.25
,
iou_thres
=
0.45
,
classes
=
None
,
agnostic
=
False
,
multi_label
=
False
,
def
non_max_suppression
(
prediction
,
conf_thres
=
0.25
,
iou_thres
=
0.45
,
classes
=
None
,
agnostic
=
False
,
multi_label
=
False
,
labels
=
(),
max_det
=
300
):
labels
=
(),
max_det
=
300
):
"""
Runs Non-Maximum Suppression (NMS) on inference result
s
"""
Non-Maximum Suppression (NMS) on inference results to reject overlapping bounding boxe
s
Returns:
Returns:
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
...
@@ -718,18 +718,19 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
...
@@ -718,18 +718,19 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
assert
0
<=
iou_thres
<=
1
,
f
'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
assert
0
<=
iou_thres
<=
1
,
f
'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
# Settings
# Settings
min_wh
,
max_wh
=
2
,
7680
# (pixels) minimum and maximum box width and height
# min_wh = 2 # (pixels) minimum box width and height
max_wh
=
7680
# (pixels) maximum box width and height
max_nms
=
30000
# maximum number of boxes into torchvision.ops.nms()
max_nms
=
30000
# maximum number of boxes into torchvision.ops.nms()
time_limit
=
0.030
*
bs
# seconds to quit after
time_limit
=
0.030
*
bs
# seconds to quit after
redundant
=
True
# require redundant detections
redundant
=
True
# require redundant detections
multi_label
&=
nc
>
1
# multiple labels per box (adds 0.5ms/img)
multi_label
&=
nc
>
1
# multiple labels per box (adds 0.5ms/img)
merge
=
False
# use merge-NMS
merge
=
False
# use merge-NMS
t
,
warn_time
=
time
.
time
(),
True
t
=
time
.
time
()
output
=
[
torch
.
zeros
((
0
,
6
),
device
=
prediction
.
device
)]
*
bs
output
=
[
torch
.
zeros
((
0
,
6
),
device
=
prediction
.
device
)]
*
bs
for
xi
,
x
in
enumerate
(
prediction
):
# image index, image inference
for
xi
,
x
in
enumerate
(
prediction
):
# image index, image inference
# Apply constraints
# Apply constraints
x
[((
x
[
...
,
2
:
4
]
<
min_wh
)
|
(
x
[
...
,
2
:
4
]
>
max_wh
))
.
any
(
1
),
4
]
=
0
# width-height
#
x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x
=
x
[
xc
[
xi
]]
# confidence
x
=
x
[
xc
[
xi
]]
# confidence
# Cat apriori labels if autolabelling
# Cat apriori labels if autolabelling
...
@@ -790,9 +791,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
...
@@ -790,9 +791,7 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
output
[
xi
]
=
x
[
i
]
output
[
xi
]
=
x
[
i
]
if
(
time
.
time
()
-
t
)
>
time_limit
:
if
(
time
.
time
()
-
t
)
>
time_limit
:
if
warn_time
:
LOGGER
.
warning
(
f
'WARNING: NMS time limit {time_limit:.3f}s exceeded'
)
LOGGER
.
warning
(
f
'WARNING: NMS time limit {time_limit:3f}s exceeded'
)
warn_time
=
False
break
# time limit exceeded
break
# time limit exceeded
return
output
return
output
...
...
编写
预览
Markdown
格式
0%
重试
或
添加新文件
添加附件
取消
您添加了
0
人
到此讨论。请谨慎行事。
请先完成此评论的编辑!
取消
请
注册
或者
登录
后发表评论