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943a4449
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体验新版 GitCode,发现更多精彩内容 >>
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943a4449
编写于
12月 16, 2019
作者:
K
Kaipeng Deng
提交者:
GitHub
12月 16, 2019
浏览文件
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差异文件
yolo_box OP add Attr(clip_bbox). (#21620)
* yolo_box OP add Attr(clip_bbox). test=develop
上级
c4f8f3bd
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
57 addition
and
25 deletion
+57
-25
paddle/fluid/operators/detection/yolo_box_op.cc
paddle/fluid/operators/detection/yolo_box_op.cc
+10
-3
paddle/fluid/operators/detection/yolo_box_op.cu
paddle/fluid/operators/detection/yolo_box_op.cu
+5
-3
paddle/fluid/operators/detection/yolo_box_op.h
paddle/fluid/operators/detection/yolo_box_op.h
+15
-12
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+3
-0
python/paddle/fluid/tests/unittests/test_yolo_box_op.py
python/paddle/fluid/tests/unittests/test_yolo_box_op.py
+24
-7
未找到文件。
paddle/fluid/operators/detection/yolo_box_op.cc
浏览文件 @
943a4449
...
...
@@ -43,9 +43,12 @@ class YoloBoxOp : public framework::OperatorWithKernel {
"+ class_num))."
);
PADDLE_ENFORCE_EQ
(
dim_imgsize
.
size
(),
2
,
"Input(ImgSize) should be a 2-D tensor."
);
PADDLE_ENFORCE_EQ
(
dim_imgsize
[
0
],
dim_x
[
0
],
"Input(ImgSize) dim[0] and Input(X) dim[0] should be same."
);
if
((
dim_imgsize
[
0
]
>
0
&&
dim_x
[
0
]
>
0
)
||
ctx
->
IsRuntime
())
{
PADDLE_ENFORCE_EQ
(
dim_imgsize
[
0
],
dim_x
[
0
],
platform
::
errors
::
InvalidArgument
(
"Input(ImgSize) dim[0] and Input(X) dim[0] should be same."
));
}
PADDLE_ENFORCE_EQ
(
dim_imgsize
[
1
],
2
,
"Input(ImgSize) dim[1] should be 2."
);
PADDLE_ENFORCE_GT
(
anchors
.
size
(),
0
,
"Attr(anchors) length should be greater than 0."
);
...
...
@@ -110,6 +113,10 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
"Boxes with confidence scores under threshold should "
"be ignored."
)
.
SetDefault
(
0.01
);
AddAttr
<
bool
>
(
"clip_bbox"
,
"Whether clip output bonding box in Input(ImgSize) "
"boundary. Default true."
)
.
SetDefault
(
true
);
AddComment
(
R"DOC(
This operator generates YOLO detection boxes from output of YOLOv3 network.
...
...
paddle/fluid/operators/detection/yolo_box_op.cu
浏览文件 @
943a4449
...
...
@@ -26,7 +26,7 @@ __global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
T
*
scores
,
const
float
conf_thresh
,
const
int
*
anchors
,
const
int
n
,
const
int
h
,
const
int
w
,
const
int
an_num
,
const
int
class_num
,
const
int
box_num
,
int
input_size
)
{
const
int
box_num
,
int
input_size
,
bool
clip_bbox
)
{
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
T
box
[
4
];
...
...
@@ -53,7 +53,7 @@ __global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
GetYoloBox
<
T
>
(
box
,
input
,
anchors
,
l
,
k
,
j
,
h
,
input_size
,
box_idx
,
grid_num
,
img_height
,
img_width
);
box_idx
=
(
i
*
box_num
+
j
*
grid_num
+
k
*
w
+
l
)
*
4
;
CalcDetectionBox
<
T
>
(
boxes
,
box
,
box_idx
,
img_height
,
img_width
);
CalcDetectionBox
<
T
>
(
boxes
,
box
,
box_idx
,
img_height
,
img_width
,
clip_bbox
);
int
label_idx
=
GetEntryIndex
(
i
,
j
,
k
*
w
+
l
,
an_num
,
an_stride
,
grid_num
,
5
);
...
...
@@ -76,6 +76,7 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
float
conf_thresh
=
ctx
.
Attr
<
float
>
(
"conf_thresh"
);
int
downsample_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio"
);
bool
clip_bbox
=
ctx
.
Attr
<
bool
>
(
"clip_bbox"
);
const
int
n
=
input
->
dims
()[
0
];
const
int
h
=
input
->
dims
()[
2
];
...
...
@@ -107,7 +108,8 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
KeYoloBoxFw
<
T
><<<
grid_dim
,
512
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
input_data
,
imgsize_data
,
boxes_data
,
scores_data
,
conf_thresh
,
anchors_data
,
n
,
h
,
w
,
an_num
,
class_num
,
box_num
,
input_size
);
anchors_data
,
n
,
h
,
w
,
an_num
,
class_num
,
box_num
,
input_size
,
clip_bbox
);
}
};
...
...
paddle/fluid/operators/detection/yolo_box_op.h
浏览文件 @
943a4449
...
...
@@ -47,21 +47,23 @@ HOSTDEVICE inline int GetEntryIndex(int batch, int an_idx, int hw_idx,
template
<
typename
T
>
HOSTDEVICE
inline
void
CalcDetectionBox
(
T
*
boxes
,
T
*
box
,
const
int
box_idx
,
const
int
img_height
,
const
int
img_width
)
{
const
int
img_width
,
bool
clip_bbox
)
{
boxes
[
box_idx
]
=
box
[
0
]
-
box
[
2
]
/
2
;
boxes
[
box_idx
+
1
]
=
box
[
1
]
-
box
[
3
]
/
2
;
boxes
[
box_idx
+
2
]
=
box
[
0
]
+
box
[
2
]
/
2
;
boxes
[
box_idx
+
3
]
=
box
[
1
]
+
box
[
3
]
/
2
;
boxes
[
box_idx
]
=
boxes
[
box_idx
]
>
0
?
boxes
[
box_idx
]
:
static_cast
<
T
>
(
0
);
boxes
[
box_idx
+
1
]
=
boxes
[
box_idx
+
1
]
>
0
?
boxes
[
box_idx
+
1
]
:
static_cast
<
T
>
(
0
);
boxes
[
box_idx
+
2
]
=
boxes
[
box_idx
+
2
]
<
img_width
-
1
?
boxes
[
box_idx
+
2
]
:
static_cast
<
T
>
(
img_width
-
1
);
boxes
[
box_idx
+
3
]
=
boxes
[
box_idx
+
3
]
<
img_height
-
1
?
boxes
[
box_idx
+
3
]
:
static_cast
<
T
>
(
img_height
-
1
);
if
(
clip_bbox
)
{
boxes
[
box_idx
]
=
boxes
[
box_idx
]
>
0
?
boxes
[
box_idx
]
:
static_cast
<
T
>
(
0
);
boxes
[
box_idx
+
1
]
=
boxes
[
box_idx
+
1
]
>
0
?
boxes
[
box_idx
+
1
]
:
static_cast
<
T
>
(
0
);
boxes
[
box_idx
+
2
]
=
boxes
[
box_idx
+
2
]
<
img_width
-
1
?
boxes
[
box_idx
+
2
]
:
static_cast
<
T
>
(
img_width
-
1
);
boxes
[
box_idx
+
3
]
=
boxes
[
box_idx
+
3
]
<
img_height
-
1
?
boxes
[
box_idx
+
3
]
:
static_cast
<
T
>
(
img_height
-
1
);
}
}
template
<
typename
T
>
...
...
@@ -86,6 +88,7 @@ class YoloBoxKernel : public framework::OpKernel<T> {
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
float
conf_thresh
=
ctx
.
Attr
<
float
>
(
"conf_thresh"
);
int
downsample_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio"
);
bool
clip_bbox
=
ctx
.
Attr
<
bool
>
(
"clip_bbox"
);
const
int
n
=
input
->
dims
()[
0
];
const
int
h
=
input
->
dims
()[
2
];
...
...
@@ -130,8 +133,8 @@ class YoloBoxKernel : public framework::OpKernel<T> {
GetYoloBox
<
T
>
(
box
,
input_data
,
anchors_data
,
l
,
k
,
j
,
h
,
input_size
,
box_idx
,
stride
,
img_height
,
img_width
);
box_idx
=
(
i
*
box_num
+
j
*
stride
+
k
*
w
+
l
)
*
4
;
CalcDetectionBox
<
T
>
(
boxes_data
,
box
,
box_idx
,
img_height
,
img_width
);
CalcDetectionBox
<
T
>
(
boxes_data
,
box
,
box_idx
,
img_height
,
img_width
,
clip_bbox
);
int
label_idx
=
GetEntryIndex
(
i
,
j
,
k
*
w
+
l
,
an_num
,
an_stride
,
stride
,
5
);
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
943a4449
...
...
@@ -1023,6 +1023,7 @@ def yolo_box(x,
class_num
,
conf_thresh
,
downsample_ratio
,
clip_bbox
=
True
,
name
=
None
):
"""
${comment}
...
...
@@ -1034,6 +1035,7 @@ def yolo_box(x,
class_num (int): ${class_num_comment}
conf_thresh (float): ${conf_thresh_comment}
downsample_ratio (int): ${downsample_ratio_comment}
clip_bbox (bool): ${clip_bbox_comment}
name (string): The default value is None. Normally there is no need
for user to set this property. For more information,
please refer to :ref:`api_guide_Name`
...
...
@@ -1081,6 +1083,7 @@ def yolo_box(x,
"class_num"
:
class_num
,
"conf_thresh"
:
conf_thresh
,
"downsample_ratio"
:
downsample_ratio
,
"clip_bbox"
:
clip_bbox
,
}
helper
.
append_op
(
...
...
python/paddle/fluid/tests/unittests/test_yolo_box_op.py
浏览文件 @
943a4449
...
...
@@ -32,6 +32,7 @@ def YoloBox(x, img_size, attrs):
class_num
=
attrs
[
'class_num'
]
conf_thresh
=
attrs
[
'conf_thresh'
]
downsample
=
attrs
[
'downsample'
]
clip_bbox
=
attrs
[
'clip_bbox'
]
input_size
=
downsample
*
h
x
=
x
.
reshape
((
n
,
an_num
,
5
+
class_num
,
h
,
w
)).
transpose
((
0
,
1
,
3
,
4
,
2
))
...
...
@@ -64,13 +65,14 @@ def YoloBox(x, img_size, attrs):
pred_box
[:,
:,
2
]
=
pred_box
[:,
:,
2
]
*
img_size
[:,
1
][:,
np
.
newaxis
]
pred_box
[:,
:,
3
]
=
pred_box
[:,
:,
3
]
*
img_size
[:,
0
][:,
np
.
newaxis
]
for
i
in
range
(
len
(
pred_box
)):
pred_box
[
i
,
:,
0
]
=
np
.
clip
(
pred_box
[
i
,
:,
0
],
0
,
np
.
inf
)
pred_box
[
i
,
:,
1
]
=
np
.
clip
(
pred_box
[
i
,
:,
1
],
0
,
np
.
inf
)
pred_box
[
i
,
:,
2
]
=
np
.
clip
(
pred_box
[
i
,
:,
2
],
-
np
.
inf
,
img_size
[
i
,
1
]
-
1
)
pred_box
[
i
,
:,
3
]
=
np
.
clip
(
pred_box
[
i
,
:,
3
],
-
np
.
inf
,
img_size
[
i
,
0
]
-
1
)
if
clip_bbox
:
for
i
in
range
(
len
(
pred_box
)):
pred_box
[
i
,
:,
0
]
=
np
.
clip
(
pred_box
[
i
,
:,
0
],
0
,
np
.
inf
)
pred_box
[
i
,
:,
1
]
=
np
.
clip
(
pred_box
[
i
,
:,
1
],
0
,
np
.
inf
)
pred_box
[
i
,
:,
2
]
=
np
.
clip
(
pred_box
[
i
,
:,
2
],
-
np
.
inf
,
img_size
[
i
,
1
]
-
1
)
pred_box
[
i
,
:,
3
]
=
np
.
clip
(
pred_box
[
i
,
:,
3
],
-
np
.
inf
,
img_size
[
i
,
0
]
-
1
)
return
pred_box
,
pred_score
.
reshape
((
n
,
-
1
,
class_num
))
...
...
@@ -87,6 +89,7 @@ class TestYoloBoxOp(OpTest):
"class_num"
:
self
.
class_num
,
"conf_thresh"
:
self
.
conf_thresh
,
"downsample"
:
self
.
downsample
,
"clip_bbox"
:
self
.
clip_bbox
,
}
self
.
inputs
=
{
...
...
@@ -109,6 +112,20 @@ class TestYoloBoxOp(OpTest):
self
.
class_num
=
2
self
.
conf_thresh
=
0.5
self
.
downsample
=
32
self
.
clip_bbox
=
True
self
.
x_shape
=
(
self
.
batch_size
,
an_num
*
(
5
+
self
.
class_num
),
13
,
13
)
self
.
imgsize_shape
=
(
self
.
batch_size
,
2
)
class
TestYoloBoxOpNoClipBbox
(
TestYoloBoxOp
):
def
initTestCase
(
self
):
self
.
anchors
=
[
10
,
13
,
16
,
30
,
33
,
23
]
an_num
=
int
(
len
(
self
.
anchors
)
//
2
)
self
.
batch_size
=
32
self
.
class_num
=
2
self
.
conf_thresh
=
0.5
self
.
downsample
=
32
self
.
clip_bbox
=
False
self
.
x_shape
=
(
self
.
batch_size
,
an_num
*
(
5
+
self
.
class_num
),
13
,
13
)
self
.
imgsize_shape
=
(
self
.
batch_size
,
2
)
...
...
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