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体验新版 GitCode,发现更多精彩内容 >>
提交
88ee56d0
编写于
1月 18, 2019
作者:
J
jerrywgz
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
enhance nms for mask rcnn
上级
3f815e07
变更
3
展开全部
隐藏空白更改
内联
并排
Showing
3 changed file
with
371 addition
and
112 deletion
+371
-112
paddle/fluid/operators/detection/bbox_util.h
paddle/fluid/operators/detection/bbox_util.h
+20
-0
paddle/fluid/operators/detection/multiclass_nms_op.cc
paddle/fluid/operators/detection/multiclass_nms_op.cc
+202
-88
python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
...on/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
+149
-24
未找到文件。
paddle/fluid/operators/detection/bbox_util.h
浏览文件 @
88ee56d0
...
@@ -93,5 +93,25 @@ void BboxOverlaps(const framework::Tensor& r_boxes,
...
@@ -93,5 +93,25 @@ void BboxOverlaps(const framework::Tensor& r_boxes,
}
}
}
}
template
<
class
T
>
void
SliceOneClass
(
const
platform
::
DeviceContext
&
ctx
,
const
framework
::
Tensor
&
items
,
const
int
class_id
,
framework
::
Tensor
*
one_class_item
)
{
T
*
item_data
=
one_class_item
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
const
T
*
items_data
=
items
.
data
<
T
>
();
const
int64_t
num_item
=
items
.
dims
()[
0
];
const
int
class_num
=
items
.
dims
()[
1
];
int
item_size
=
1
;
if
(
items
.
dims
().
size
()
==
3
)
{
item_size
=
items
.
dims
()[
2
];
}
for
(
int
i
=
0
;
i
<
num_item
;
++
i
)
{
for
(
int
j
=
0
;
j
<
item_size
;
++
j
)
{
item_data
[
i
*
item_size
+
j
]
=
items_data
[
i
*
class_num
*
item_size
+
class_id
*
item_size
+
j
];
}
}
}
}
// namespace operators
}
// namespace operators
}
// namespace paddle
}
// namespace paddle
paddle/fluid/operators/detection/multiclass_nms_op.cc
浏览文件 @
88ee56d0
此差异已折叠。
点击以展开。
python/paddle/fluid/tests/unittests/test_multiclass_nms_op.py
浏览文件 @
88ee56d0
...
@@ -19,7 +19,7 @@ import copy
...
@@ -19,7 +19,7 @@ import copy
from
op_test
import
OpTest
from
op_test
import
OpTest
def
iou
(
box_a
,
box_b
):
def
iou
(
box_a
,
box_b
,
normalized
):
"""Apply intersection-over-union overlap between box_a and box_b
"""Apply intersection-over-union overlap between box_a and box_b
"""
"""
xmin_a
=
min
(
box_a
[
0
],
box_a
[
2
])
xmin_a
=
min
(
box_a
[
0
],
box_a
[
2
])
...
@@ -32,8 +32,10 @@ def iou(box_a, box_b):
...
@@ -32,8 +32,10 @@ def iou(box_a, box_b):
xmax_b
=
max
(
box_b
[
0
],
box_b
[
2
])
xmax_b
=
max
(
box_b
[
0
],
box_b
[
2
])
ymax_b
=
max
(
box_b
[
1
],
box_b
[
3
])
ymax_b
=
max
(
box_b
[
1
],
box_b
[
3
])
area_a
=
(
ymax_a
-
ymin_a
)
*
(
xmax_a
-
xmin_a
)
area_a
=
(
ymax_a
-
ymin_a
+
(
normalized
==
False
))
*
\
area_b
=
(
ymax_b
-
ymin_b
)
*
(
xmax_b
-
xmin_b
)
(
xmax_a
-
xmin_a
+
(
normalized
==
False
))
area_b
=
(
ymax_b
-
ymin_b
+
(
normalized
==
False
))
*
\
(
xmax_b
-
xmin_b
+
(
normalized
==
False
))
if
area_a
<=
0
and
area_b
<=
0
:
if
area_a
<=
0
and
area_b
<=
0
:
return
0.0
return
0.0
...
@@ -42,17 +44,21 @@ def iou(box_a, box_b):
...
@@ -42,17 +44,21 @@ def iou(box_a, box_b):
xb
=
min
(
xmax_a
,
xmax_b
)
xb
=
min
(
xmax_a
,
xmax_b
)
yb
=
min
(
ymax_a
,
ymax_b
)
yb
=
min
(
ymax_a
,
ymax_b
)
inter_area
=
max
(
xb
-
xa
,
0.0
)
*
max
(
yb
-
ya
,
0.0
)
inter_area
=
max
(
xb
-
xa
+
(
normalized
==
False
),
0.0
)
*
\
max
(
yb
-
ya
+
(
normalized
==
False
),
0.0
)
box_a_area
=
(
box_a
[
2
]
-
box_a
[
0
])
*
(
box_a
[
3
]
-
box_a
[
1
])
box_b_area
=
(
box_b
[
2
]
-
box_b
[
0
])
*
(
box_b
[
3
]
-
box_b
[
1
])
iou_ratio
=
inter_area
/
(
area_a
+
area_b
-
inter_area
)
iou_ratio
=
inter_area
/
(
area_a
+
area_b
-
inter_area
)
return
iou_ratio
return
iou_ratio
def
nms
(
boxes
,
scores
,
score_threshold
,
nms_threshold
,
top_k
=
200
,
eta
=
1.0
):
def
nms
(
boxes
,
scores
,
score_threshold
,
nms_threshold
,
top_k
=
200
,
normalized
=
True
,
eta
=
1.0
):
"""Apply non-maximum suppression at test time to avoid detecting too many
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
overlapping bounding boxes for a given object.
Args:
Args:
...
@@ -87,7 +93,7 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
...
@@ -87,7 +93,7 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
for
k
in
range
(
len
(
selected_indices
)):
for
k
in
range
(
len
(
selected_indices
)):
if
keep
:
if
keep
:
kept_idx
=
selected_indices
[
k
]
kept_idx
=
selected_indices
[
k
]
overlap
=
iou
(
boxes
[
idx
],
boxes
[
kept_idx
])
overlap
=
iou
(
boxes
[
idx
],
boxes
[
kept_idx
]
,
normalized
)
keep
=
True
if
overlap
<=
adaptive_threshold
else
False
keep
=
True
if
overlap
<=
adaptive_threshold
else
False
else
:
else
:
break
break
...
@@ -99,16 +105,24 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
...
@@ -99,16 +105,24 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
def
multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
def
multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
):
nms_top_k
,
keep_top_k
,
normalized
,
shared
):
class_num
=
scores
.
shape
[
0
]
if
shared
:
priorbox_num
=
scores
.
shape
[
1
]
class_num
=
scores
.
shape
[
0
]
priorbox_num
=
scores
.
shape
[
1
]
else
:
box_num
=
scores
.
shape
[
0
]
class_num
=
scores
.
shape
[
1
]
selected_indices
=
{}
selected_indices
=
{}
num_det
=
0
num_det
=
0
for
c
in
range
(
class_num
):
for
c
in
range
(
class_num
):
if
c
==
background
:
continue
if
c
==
background
:
continue
indices
=
nms
(
boxes
,
scores
[
c
],
score_threshold
,
nms_threshold
,
if
shared
:
nms_top_k
)
indices
=
nms
(
boxes
,
scores
[
c
],
score_threshold
,
nms_threshold
,
nms_top_k
,
normalized
)
else
:
indices
=
nms
(
boxes
[:,
c
,
:],
scores
[:,
c
],
score_threshold
,
nms_threshold
,
nms_top_k
,
normalized
)
selected_indices
[
c
]
=
indices
selected_indices
[
c
]
=
indices
num_det
+=
len
(
indices
)
num_det
+=
len
(
indices
)
...
@@ -116,7 +130,10 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
...
@@ -116,7 +130,10 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
score_index
=
[]
score_index
=
[]
for
c
,
indices
in
selected_indices
.
items
():
for
c
,
indices
in
selected_indices
.
items
():
for
idx
in
indices
:
for
idx
in
indices
:
score_index
.
append
((
scores
[
c
][
idx
],
c
,
idx
))
if
shared
:
score_index
.
append
((
scores
[
c
][
idx
],
c
,
idx
))
else
:
score_index
.
append
((
scores
[
idx
][
c
],
c
,
idx
))
sorted_score_index
=
sorted
(
sorted_score_index
=
sorted
(
score_index
,
key
=
lambda
tup
:
tup
[
0
],
reverse
=
True
)
score_index
,
key
=
lambda
tup
:
tup
[
0
],
reverse
=
True
)
...
@@ -127,24 +144,74 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
...
@@ -127,24 +144,74 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
selected_indices
[
c
]
=
[]
selected_indices
[
c
]
=
[]
for
s
,
c
,
idx
in
sorted_score_index
:
for
s
,
c
,
idx
in
sorted_score_index
:
selected_indices
[
c
].
append
(
idx
)
selected_indices
[
c
].
append
(
idx
)
if
not
shared
:
for
labels
in
selected_indices
:
selected_indices
[
labels
].
sort
()
num_det
=
keep_top_k
num_det
=
keep_top_k
return
selected_indices
,
num_det
return
selected_indices
,
num_det
def
batched_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
def
lod_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
):
nms_threshold
,
nms_top_k
,
keep_top_k
,
box_lod
,
normalized
):
det_outs
=
[]
lod
=
[]
head
=
0
for
n
in
range
(
len
(
box_lod
[
0
])):
box
=
boxes
[
head
:
head
+
box_lod
[
0
][
n
]]
score
=
scores
[
head
:
head
+
box_lod
[
0
][
n
]]
head
=
head
+
box_lod
[
0
][
n
]
nmsed_outs
,
nmsed_num
=
multiclass_nms
(
box
,
score
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
,
shared
=
False
)
if
nmsed_num
==
0
:
lod
.
append
(
1
)
continue
lod
.
append
(
nmsed_num
)
for
c
,
indices
in
nmsed_outs
.
items
():
for
idx
in
indices
:
xmin
,
ymin
,
xmax
,
ymax
=
box
[
idx
,
c
,
:]
det_outs
.
append
([
c
,
score
[
idx
][
c
],
xmin
,
ymin
,
xmax
,
ymax
])
return
det_outs
,
lod
def
batched_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
=
True
):
batch_size
=
scores
.
shape
[
0
]
batch_size
=
scores
.
shape
[
0
]
det_outs
=
[]
det_outs
=
[]
lod
=
[]
lod
=
[]
for
n
in
range
(
batch_size
):
for
n
in
range
(
batch_size
):
nmsed_outs
,
nmsed_num
=
multiclass_nms
(
boxes
[
n
],
scores
[
n
],
background
,
nmsed_outs
,
nmsed_num
=
multiclass_nms
(
score_threshold
,
nms_threshold
,
boxes
[
n
],
nms_top_k
,
keep_top_k
)
scores
[
n
],
lod
.
append
(
nmsed_num
)
background
,
if
nmsed_num
==
0
:
continue
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
,
shared
=
True
)
if
nmsed_num
==
0
:
lod
.
append
(
1
)
continue
lod
.
append
(
nmsed_num
)
tmp_det_out
=
[]
tmp_det_out
=
[]
for
c
,
indices
in
nmsed_outs
.
items
():
for
c
,
indices
in
nmsed_outs
.
items
():
for
idx
in
indices
:
for
idx
in
indices
:
...
@@ -168,7 +235,6 @@ class TestMulticlassNMSOp(OpTest):
...
@@ -168,7 +235,6 @@ class TestMulticlassNMSOp(OpTest):
M
=
1200
M
=
1200
C
=
21
C
=
21
BOX_SIZE
=
4
BOX_SIZE
=
4
background
=
0
background
=
0
nms_threshold
=
0.3
nms_threshold
=
0.3
nms_top_k
=
400
nms_top_k
=
400
...
@@ -193,6 +259,7 @@ class TestMulticlassNMSOp(OpTest):
...
@@ -193,6 +259,7 @@ class TestMulticlassNMSOp(OpTest):
nmsed_outs
,
lod
=
batched_multiclass_nms
(
boxes
,
scores
,
background
,
nmsed_outs
,
lod
=
batched_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
)
nms_top_k
,
keep_top_k
)
print
(
'python lod: '
,
lod
)
nmsed_outs
=
[
-
1
]
if
not
nmsed_outs
else
nmsed_outs
nmsed_outs
=
[
-
1
]
if
not
nmsed_outs
else
nmsed_outs
nmsed_outs
=
np
.
array
(
nmsed_outs
).
astype
(
'float32'
)
nmsed_outs
=
np
.
array
(
nmsed_outs
).
astype
(
'float32'
)
...
@@ -206,6 +273,7 @@ class TestMulticlassNMSOp(OpTest):
...
@@ -206,6 +273,7 @@ class TestMulticlassNMSOp(OpTest):
'keep_top_k'
:
keep_top_k
,
'keep_top_k'
:
keep_top_k
,
'score_threshold'
:
score_threshold
,
'score_threshold'
:
score_threshold
,
'nms_eta'
:
1.0
,
'nms_eta'
:
1.0
,
'normalized'
:
True
,
}
}
def
test_check_output
(
self
):
def
test_check_output
(
self
):
...
@@ -219,13 +287,70 @@ class TestMulticlassNMSOpNoOutput(TestMulticlassNMSOp):
...
@@ -219,13 +287,70 @@ class TestMulticlassNMSOpNoOutput(TestMulticlassNMSOp):
self
.
score_threshold
=
2.0
self
.
score_threshold
=
2.0
class
TestMulticlassNMSLoDInput
(
OpTest
):
def
set_argument
(
self
):
self
.
score_threshold
=
0.01
def
setUp
(
self
):
self
.
set_argument
()
M
=
1200
C
=
21
BOX_SIZE
=
4
box_lod
=
[[
1200
]]
background
=
0
nms_threshold
=
0.3
nms_top_k
=
400
keep_top_k
=
200
score_threshold
=
self
.
score_threshold
normalized
=
False
scores
=
np
.
random
.
random
((
M
,
C
)).
astype
(
'float32'
)
def
softmax
(
x
):
shiftx
=
x
-
np
.
max
(
x
).
clip
(
-
64.
)
exps
=
np
.
exp
(
shiftx
)
return
exps
/
np
.
sum
(
exps
)
scores
=
np
.
apply_along_axis
(
softmax
,
1
,
scores
)
boxes
=
np
.
random
.
random
((
M
,
C
,
BOX_SIZE
)).
astype
(
'float32'
)
boxes
[:,
:,
0
]
=
boxes
[:,
:,
0
]
*
10
boxes
[:,
:,
1
]
=
boxes
[:,
:,
1
]
*
10
boxes
[:,
:,
2
]
=
boxes
[:,
:,
2
]
*
10
+
10
boxes
[:,
:,
3
]
=
boxes
[:,
:,
3
]
*
10
+
10
nmsed_outs
,
lod
=
lod_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
box_lod
,
normalized
)
nmsed_outs
=
[
-
1
]
if
not
nmsed_outs
else
nmsed_outs
nmsed_outs
=
np
.
array
(
nmsed_outs
).
astype
(
'float32'
)
self
.
op_type
=
'multiclass_nms'
self
.
inputs
=
{
'BBoxes'
:
(
boxes
,
box_lod
),
'Scores'
:
(
scores
,
box_lod
),
}
self
.
outputs
=
{
'Out'
:
(
nmsed_outs
,
[
lod
])}
self
.
attrs
=
{
'background_label'
:
0
,
'nms_threshold'
:
nms_threshold
,
'nms_top_k'
:
nms_top_k
,
'keep_top_k'
:
keep_top_k
,
'score_threshold'
:
score_threshold
,
'nms_eta'
:
1.0
,
'normalized'
:
normalized
,
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestIOU
(
unittest
.
TestCase
):
class
TestIOU
(
unittest
.
TestCase
):
def
test_iou
(
self
):
def
test_iou
(
self
):
box1
=
np
.
array
([
4.0
,
3.0
,
7.0
,
5.0
]).
astype
(
'float32'
)
box1
=
np
.
array
([
4.0
,
3.0
,
7.0
,
5.0
]).
astype
(
'float32'
)
box2
=
np
.
array
([
3.0
,
4.0
,
6.0
,
8.0
]).
astype
(
'float32'
)
box2
=
np
.
array
([
3.0
,
4.0
,
6.0
,
8.0
]).
astype
(
'float32'
)
expt_output
=
np
.
array
([
2.0
/
16.0
]).
astype
(
'float32'
)
expt_output
=
np
.
array
([
2.0
/
16.0
]).
astype
(
'float32'
)
calc_output
=
np
.
array
([
iou
(
box1
,
box2
)]).
astype
(
'float32'
)
calc_output
=
np
.
array
([
iou
(
box1
,
box2
,
True
)]).
astype
(
'float32'
)
self
.
assertTrue
(
np
.
allclose
(
calc_output
,
expt_output
))
self
.
assertTrue
(
np
.
allclose
(
calc_output
,
expt_output
))
...
...
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