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Commits
406ee528
Unverified
提交
406ee528
authored
4月 10, 2022
作者:
Glenn Jocher
提交者:
GitHub
4月 10, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Loss and IoU speed improvements (#7361)
* Loss speed improvements * bbox_iou speed improvements * bbox_ioa speed improvements * box_iou speed improvements * box_iou speed improvements
上级
aa542ce6
隐藏空白字符变更
内嵌
并排
正在显示
3 个修改的文件
包含
32 行增加
和
34 行删除
+32
-34
loss.py
utils/loss.py
+4
-4
metrics.py
utils/metrics.py
+26
-28
val.py
val.py
+2
-2
没有找到文件。
utils/loss.py
浏览文件 @
406ee528
...
@@ -138,7 +138,7 @@ class ComputeLoss:
...
@@ -138,7 +138,7 @@ class ComputeLoss:
pxy
=
pxy
.
sigmoid
()
*
2
-
0.5
pxy
=
pxy
.
sigmoid
()
*
2
-
0.5
pwh
=
(
pwh
.
sigmoid
()
*
2
)
**
2
*
anchors
[
i
]
pwh
=
(
pwh
.
sigmoid
()
*
2
)
**
2
*
anchors
[
i
]
pbox
=
torch
.
cat
((
pxy
,
pwh
),
1
)
# predicted box
pbox
=
torch
.
cat
((
pxy
,
pwh
),
1
)
# predicted box
iou
=
bbox_iou
(
pbox
.
T
,
tbox
[
i
],
x1y1x2y2
=
False
,
CIoU
=
True
)
# iou(prediction, target)
iou
=
bbox_iou
(
pbox
,
tbox
[
i
],
CIoU
=
True
)
.
squeeze
(
)
# iou(prediction, target)
lbox
+=
(
1.0
-
iou
)
.
mean
()
# iou loss
lbox
+=
(
1.0
-
iou
)
.
mean
()
# iou loss
# Objectness
# Objectness
...
@@ -180,7 +180,7 @@ class ComputeLoss:
...
@@ -180,7 +180,7 @@ class ComputeLoss:
tcls
,
tbox
,
indices
,
anch
=
[],
[],
[],
[]
tcls
,
tbox
,
indices
,
anch
=
[],
[],
[],
[]
gain
=
torch
.
ones
(
7
,
device
=
self
.
device
)
# normalized to gridspace gain
gain
=
torch
.
ones
(
7
,
device
=
self
.
device
)
# normalized to gridspace gain
ai
=
torch
.
arange
(
na
,
device
=
self
.
device
)
.
float
()
.
view
(
na
,
1
)
.
repeat
(
1
,
nt
)
# same as .repeat_interleave(nt)
ai
=
torch
.
arange
(
na
,
device
=
self
.
device
)
.
float
()
.
view
(
na
,
1
)
.
repeat
(
1
,
nt
)
# same as .repeat_interleave(nt)
targets
=
torch
.
cat
((
targets
.
repeat
(
na
,
1
,
1
),
ai
[
:,
:
,
None
]),
2
)
# append anchor indices
targets
=
torch
.
cat
((
targets
.
repeat
(
na
,
1
,
1
),
ai
[
...
,
None
]),
2
)
# append anchor indices
g
=
0.5
# bias
g
=
0.5
# bias
off
=
torch
.
tensor
(
off
=
torch
.
tensor
(
...
@@ -199,10 +199,10 @@ class ComputeLoss:
...
@@ -199,10 +199,10 @@ class ComputeLoss:
gain
[
2
:
6
]
=
torch
.
tensor
(
p
[
i
]
.
shape
)[[
3
,
2
,
3
,
2
]]
# xyxy gain
gain
[
2
:
6
]
=
torch
.
tensor
(
p
[
i
]
.
shape
)[[
3
,
2
,
3
,
2
]]
# xyxy gain
# Match targets to anchors
# Match targets to anchors
t
=
targets
*
gain
t
=
targets
*
gain
# shape(3,n,7)
if
nt
:
if
nt
:
# Matches
# Matches
r
=
t
[
:,
:
,
4
:
6
]
/
anchors
[:,
None
]
# wh ratio
r
=
t
[
...
,
4
:
6
]
/
anchors
[:,
None
]
# wh ratio
j
=
torch
.
max
(
r
,
1
/
r
)
.
max
(
2
)[
0
]
<
self
.
hyp
[
'anchor_t'
]
# compare
j
=
torch
.
max
(
r
,
1
/
r
)
.
max
(
2
)[
0
]
<
self
.
hyp
[
'anchor_t'
]
# compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t
=
t
[
j
]
# filter
t
=
t
[
j
]
# filter
...
...
utils/metrics.py
浏览文件 @
406ee528
...
@@ -206,37 +206,36 @@ class ConfusionMatrix:
...
@@ -206,37 +206,36 @@ class ConfusionMatrix:
print
(
' '
.
join
(
map
(
str
,
self
.
matrix
[
i
])))
print
(
' '
.
join
(
map
(
str
,
self
.
matrix
[
i
])))
def
bbox_iou
(
box1
,
box2
,
x1y1x2y2
=
True
,
GIoU
=
False
,
DIoU
=
False
,
CIoU
=
False
,
eps
=
1e-7
):
def
bbox_iou
(
box1
,
box2
,
xywh
=
True
,
GIoU
=
False
,
DIoU
=
False
,
CIoU
=
False
,
eps
=
1e-7
):
# Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
# Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
box2
=
box2
.
T
# Get the coordinates of bounding boxes
# Get the coordinates of bounding boxes
if
x1y1x2y2
:
# x1, y1, x2, y2 = box1
if
xywh
:
# transform from xywh to xyxy
b1_x1
,
b1_y1
,
b1_x2
,
b1_y2
=
box1
[
0
],
box1
[
1
],
box1
[
2
],
box1
[
3
]
(
x1
,
y1
,
w1
,
h1
),
(
x2
,
y2
,
w2
,
h2
)
=
box1
.
chunk
(
4
,
1
),
box2
.
chunk
(
4
,
1
)
b2_x1
,
b2_y1
,
b2_x2
,
b2_y2
=
box2
[
0
],
box2
[
1
],
box2
[
2
],
box2
[
3
]
w1_
,
h1_
,
w2_
,
h2_
=
w1
/
2
,
h1
/
2
,
w2
/
2
,
h2
/
2
else
:
# transform from xywh to xyxy
b1_x1
,
b1_x2
,
b1_y1
,
b1_y2
=
x1
-
w1_
,
x1
+
w1_
,
y1
-
h1_
,
y1
+
h1_
b1_x1
,
b1_x2
=
box1
[
0
]
-
box1
[
2
]
/
2
,
box1
[
0
]
+
box1
[
2
]
/
2
b2_x1
,
b2_x2
,
b2_y1
,
b2_y2
=
x2
-
w2_
,
x2
+
w2_
,
y2
-
h2_
,
y2
+
h2_
b1_y1
,
b1_y2
=
box1
[
1
]
-
box1
[
3
]
/
2
,
box1
[
1
]
+
box1
[
3
]
/
2
else
:
# x1, y1, x2, y2 = box1
b2_x1
,
b2_x2
=
box2
[
0
]
-
box2
[
2
]
/
2
,
box2
[
0
]
+
box2
[
2
]
/
2
b1_x1
,
b1_y1
,
b1_x2
,
b1_y2
=
box1
.
chunk
(
4
,
1
)
b2_y1
,
b2_y2
=
box2
[
1
]
-
box2
[
3
]
/
2
,
box2
[
1
]
+
box2
[
3
]
/
2
b2_x1
,
b2_y1
,
b2_x2
,
b2_y2
=
box2
.
chunk
(
4
,
1
)
w1
,
h1
=
b1_x2
-
b1_x1
,
b1_y2
-
b1_y1
+
eps
w2
,
h2
=
b2_x2
-
b2_x1
,
b2_y2
-
b2_y1
+
eps
# Intersection area
# Intersection area
inter
=
(
torch
.
min
(
b1_x2
,
b2_x2
)
-
torch
.
max
(
b1_x1
,
b2_x1
))
.
clamp
(
0
)
*
\
inter
=
(
torch
.
min
(
b1_x2
,
b2_x2
)
-
torch
.
max
(
b1_x1
,
b2_x1
))
.
clamp
(
0
)
*
\
(
torch
.
min
(
b1_y2
,
b2_y2
)
-
torch
.
max
(
b1_y1
,
b2_y1
))
.
clamp
(
0
)
(
torch
.
min
(
b1_y2
,
b2_y2
)
-
torch
.
max
(
b1_y1
,
b2_y1
))
.
clamp
(
0
)
# Union Area
# Union Area
w1
,
h1
=
b1_x2
-
b1_x1
,
b1_y2
-
b1_y1
+
eps
w2
,
h2
=
b2_x2
-
b2_x1
,
b2_y2
-
b2_y1
+
eps
union
=
w1
*
h1
+
w2
*
h2
-
inter
+
eps
union
=
w1
*
h1
+
w2
*
h2
-
inter
+
eps
# IoU
iou
=
inter
/
union
iou
=
inter
/
union
if
CIoU
or
DIoU
or
GIoU
:
if
CIoU
or
DIoU
or
GIoU
:
cw
=
torch
.
max
(
b1_x2
,
b2_x2
)
-
torch
.
min
(
b1_x1
,
b2_x1
)
# convex (smallest enclosing box) width
cw
=
torch
.
max
(
b1_x2
,
b2_x2
)
-
torch
.
min
(
b1_x1
,
b2_x1
)
# convex (smallest enclosing box) width
ch
=
torch
.
max
(
b1_y2
,
b2_y2
)
-
torch
.
min
(
b1_y1
,
b2_y1
)
# convex height
ch
=
torch
.
max
(
b1_y2
,
b2_y2
)
-
torch
.
min
(
b1_y1
,
b2_y1
)
# convex height
if
CIoU
or
DIoU
:
# Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
if
CIoU
or
DIoU
:
# Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
c2
=
cw
**
2
+
ch
**
2
+
eps
# convex diagonal squared
c2
=
cw
**
2
+
ch
**
2
+
eps
# convex diagonal squared
rho2
=
((
b2_x1
+
b2_x2
-
b1_x1
-
b1_x2
)
**
2
+
rho2
=
((
b2_x1
+
b2_x2
-
b1_x1
-
b1_x2
)
**
2
+
(
b2_y1
+
b2_y2
-
b1_y1
-
b1_y2
)
**
2
)
/
4
# center dist ** 2
(
b2_y1
+
b2_y2
-
b1_y1
-
b1_y2
)
**
2
)
/
4
# center distance squared
if
CIoU
:
# https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
if
CIoU
:
# https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
v
=
(
4
/
math
.
pi
**
2
)
*
torch
.
pow
(
torch
.
atan
(
w2
/
h2
)
-
torch
.
atan
(
w1
/
h1
),
2
)
v
=
(
4
/
math
.
pi
**
2
)
*
torch
.
pow
(
torch
.
atan
(
w2
/
h2
)
-
torch
.
atan
(
w1
/
h1
),
2
)
with
torch
.
no_grad
():
with
torch
.
no_grad
():
...
@@ -248,6 +247,11 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=
...
@@ -248,6 +247,11 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=
return
iou
# IoU
return
iou
# IoU
def
box_area
(
box
):
# box = xyxy(4,n)
return
(
box
[
2
]
-
box
[
0
])
*
(
box
[
3
]
-
box
[
1
])
def
box_iou
(
box1
,
box2
):
def
box_iou
(
box1
,
box2
):
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
"""
"""
...
@@ -261,16 +265,12 @@ def box_iou(box1, box2):
...
@@ -261,16 +265,12 @@ def box_iou(box1, box2):
IoU values for every element in boxes1 and boxes2
IoU values for every element in boxes1 and boxes2
"""
"""
def
box_area
(
box
):
# box = 4xn
return
(
box
[
2
]
-
box
[
0
])
*
(
box
[
3
]
-
box
[
1
])
area1
=
box_area
(
box1
.
T
)
area2
=
box_area
(
box2
.
T
)
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
inter
=
(
torch
.
min
(
box1
[:,
None
,
2
:],
box2
[:,
2
:])
-
torch
.
max
(
box1
[:,
None
,
:
2
],
box2
[:,
:
2
]))
.
clamp
(
0
)
.
prod
(
2
)
(
a1
,
a2
),
(
b1
,
b2
)
=
box1
[:,
None
]
.
chunk
(
2
,
2
),
box2
.
chunk
(
2
,
1
)
return
inter
/
(
area1
[:,
None
]
+
area2
-
inter
)
# iou = inter / (area1 + area2 - inter)
inter
=
(
torch
.
min
(
a2
,
b2
)
-
torch
.
max
(
a1
,
b1
))
.
clamp
(
0
)
.
prod
(
2
)
# IoU = inter / (area1 + area2 - inter)
return
inter
/
(
box_area
(
box1
.
T
)[:,
None
]
+
box_area
(
box2
.
T
)
-
inter
)
def
bbox_ioa
(
box1
,
box2
,
eps
=
1E-7
):
def
bbox_ioa
(
box1
,
box2
,
eps
=
1E-7
):
...
@@ -280,11 +280,9 @@ def bbox_ioa(box1, box2, eps=1E-7):
...
@@ -280,11 +280,9 @@ def bbox_ioa(box1, box2, eps=1E-7):
returns: np.array of shape(n)
returns: np.array of shape(n)
"""
"""
box2
=
box2
.
transpose
()
# Get the coordinates of bounding boxes
# Get the coordinates of bounding boxes
b1_x1
,
b1_y1
,
b1_x2
,
b1_y2
=
box1
[
0
],
box1
[
1
],
box1
[
2
],
box1
[
3
]
b1_x1
,
b1_y1
,
b1_x2
,
b1_y2
=
box1
b2_x1
,
b2_y1
,
b2_x2
,
b2_y2
=
box2
[
0
],
box2
[
1
],
box2
[
2
],
box2
[
3
]
b2_x1
,
b2_y1
,
b2_x2
,
b2_y2
=
box2
.
T
# Intersection area
# Intersection area
inter_area
=
(
np
.
minimum
(
b1_x2
,
b2_x2
)
-
np
.
maximum
(
b1_x1
,
b2_x1
))
.
clip
(
0
)
*
\
inter_area
=
(
np
.
minimum
(
b1_x2
,
b2_x2
)
-
np
.
maximum
(
b1_x1
,
b2_x1
))
.
clip
(
0
)
*
\
...
...
val.py
浏览文件 @
406ee528
...
@@ -38,10 +38,10 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
...
@@ -38,10 +38,10 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
from
models.common
import
DetectMultiBackend
from
models.common
import
DetectMultiBackend
from
utils.callbacks
import
Callbacks
from
utils.callbacks
import
Callbacks
from
utils.datasets
import
create_dataloader
from
utils.datasets
import
create_dataloader
from
utils.general
import
(
LOGGER
,
box_iou
,
check_dataset
,
check_img_size
,
check_requirements
,
check_yaml
,
from
utils.general
import
(
LOGGER
,
check_dataset
,
check_img_size
,
check_requirements
,
check_yaml
,
coco80_to_coco91_class
,
colorstr
,
increment_path
,
non_max_suppression
,
print_args
,
coco80_to_coco91_class
,
colorstr
,
increment_path
,
non_max_suppression
,
print_args
,
scale_coords
,
xywh2xyxy
,
xyxy2xywh
)
scale_coords
,
xywh2xyxy
,
xyxy2xywh
)
from
utils.metrics
import
ConfusionMatrix
,
ap_per_class
from
utils.metrics
import
ConfusionMatrix
,
ap_per_class
,
box_iou
from
utils.plots
import
output_to_target
,
plot_images
,
plot_val_study
from
utils.plots
import
output_to_target
,
plot_images
,
plot_val_study
from
utils.torch_utils
import
select_device
,
time_sync
from
utils.torch_utils
import
select_device
,
time_sync
...
...
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