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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
操作
浏览文件
下载
电子邮件补丁
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 {
...
@@ -43,9 +43,12 @@ class YoloBoxOp : public framework::OperatorWithKernel {
"+ class_num))."
);
"+ class_num))."
);
PADDLE_ENFORCE_EQ
(
dim_imgsize
.
size
(),
2
,
PADDLE_ENFORCE_EQ
(
dim_imgsize
.
size
(),
2
,
"Input(ImgSize) should be a 2-D tensor."
);
"Input(ImgSize) should be a 2-D tensor."
);
PADDLE_ENFORCE_EQ
(
if
((
dim_imgsize
[
0
]
>
0
&&
dim_x
[
0
]
>
0
)
||
ctx
->
IsRuntime
())
{
dim_imgsize
[
0
],
dim_x
[
0
],
PADDLE_ENFORCE_EQ
(
"Input(ImgSize) dim[0] and Input(X) dim[0] should be same."
);
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_EQ
(
dim_imgsize
[
1
],
2
,
"Input(ImgSize) dim[1] should be 2."
);
PADDLE_ENFORCE_GT
(
anchors
.
size
(),
0
,
PADDLE_ENFORCE_GT
(
anchors
.
size
(),
0
,
"Attr(anchors) length should be greater than 0."
);
"Attr(anchors) length should be greater than 0."
);
...
@@ -110,6 +113,10 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
...
@@ -110,6 +113,10 @@ class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
"Boxes with confidence scores under threshold should "
"Boxes with confidence scores under threshold should "
"be ignored."
)
"be ignored."
)
.
SetDefault
(
0.01
);
.
SetDefault
(
0.01
);
AddAttr
<
bool
>
(
"clip_bbox"
,
"Whether clip output bonding box in Input(ImgSize) "
"boundary. Default true."
)
.
SetDefault
(
true
);
AddComment
(
R"DOC(
AddComment
(
R"DOC(
This operator generates YOLO detection boxes from output of YOLOv3 network.
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,
...
@@ -26,7 +26,7 @@ __global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
T
*
scores
,
const
float
conf_thresh
,
T
*
scores
,
const
float
conf_thresh
,
const
int
*
anchors
,
const
int
n
,
const
int
h
,
const
int
*
anchors
,
const
int
n
,
const
int
h
,
const
int
w
,
const
int
an_num
,
const
int
class_num
,
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
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
int
stride
=
blockDim
.
x
*
gridDim
.
x
;
T
box
[
4
];
T
box
[
4
];
...
@@ -53,7 +53,7 @@ __global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
...
@@ -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
,
GetYoloBox
<
T
>
(
box
,
input
,
anchors
,
l
,
k
,
j
,
h
,
input_size
,
box_idx
,
grid_num
,
img_height
,
img_width
);
grid_num
,
img_height
,
img_width
);
box_idx
=
(
i
*
box_num
+
j
*
grid_num
+
k
*
w
+
l
)
*
4
;
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
=
int
label_idx
=
GetEntryIndex
(
i
,
j
,
k
*
w
+
l
,
an_num
,
an_stride
,
grid_num
,
5
);
GetEntryIndex
(
i
,
j
,
k
*
w
+
l
,
an_num
,
an_stride
,
grid_num
,
5
);
...
@@ -76,6 +76,7 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
...
@@ -76,6 +76,7 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
float
conf_thresh
=
ctx
.
Attr
<
float
>
(
"conf_thresh"
);
float
conf_thresh
=
ctx
.
Attr
<
float
>
(
"conf_thresh"
);
int
downsample_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio"
);
int
downsample_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio"
);
bool
clip_bbox
=
ctx
.
Attr
<
bool
>
(
"clip_bbox"
);
const
int
n
=
input
->
dims
()[
0
];
const
int
n
=
input
->
dims
()[
0
];
const
int
h
=
input
->
dims
()[
2
];
const
int
h
=
input
->
dims
()[
2
];
...
@@ -107,7 +108,8 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
...
@@ -107,7 +108,8 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
KeYoloBoxFw
<
T
><<<
grid_dim
,
512
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
KeYoloBoxFw
<
T
><<<
grid_dim
,
512
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
input_data
,
imgsize_data
,
boxes_data
,
scores_data
,
conf_thresh
,
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,
...
@@ -47,21 +47,23 @@ HOSTDEVICE inline int GetEntryIndex(int batch, int an_idx, int hw_idx,
template
<
typename
T
>
template
<
typename
T
>
HOSTDEVICE
inline
void
CalcDetectionBox
(
T
*
boxes
,
T
*
box
,
const
int
box_idx
,
HOSTDEVICE
inline
void
CalcDetectionBox
(
T
*
boxes
,
T
*
box
,
const
int
box_idx
,
const
int
img_height
,
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
]
=
box
[
0
]
-
box
[
2
]
/
2
;
boxes
[
box_idx
+
1
]
=
box
[
1
]
-
box
[
3
]
/
2
;
boxes
[
box_idx
+
1
]
=
box
[
1
]
-
box
[
3
]
/
2
;
boxes
[
box_idx
+
2
]
=
box
[
0
]
+
box
[
2
]
/
2
;
boxes
[
box_idx
+
2
]
=
box
[
0
]
+
box
[
2
]
/
2
;
boxes
[
box_idx
+
3
]
=
box
[
1
]
+
box
[
3
]
/
2
;
boxes
[
box_idx
+
3
]
=
box
[
1
]
+
box
[
3
]
/
2
;
boxes
[
box_idx
]
=
boxes
[
box_idx
]
>
0
?
boxes
[
box_idx
]
:
static_cast
<
T
>
(
0
);
if
(
clip_bbox
)
{
boxes
[
box_idx
+
1
]
=
boxes
[
box_idx
]
=
boxes
[
box_idx
]
>
0
?
boxes
[
box_idx
]
:
static_cast
<
T
>
(
0
);
boxes
[
box_idx
+
1
]
>
0
?
boxes
[
box_idx
+
1
]
:
static_cast
<
T
>
(
0
);
boxes
[
box_idx
+
1
]
=
boxes
[
box_idx
+
2
]
=
boxes
[
box_idx
+
2
]
<
img_width
-
1
boxes
[
box_idx
+
1
]
>
0
?
boxes
[
box_idx
+
1
]
:
static_cast
<
T
>
(
0
);
?
boxes
[
box_idx
+
2
]
boxes
[
box_idx
+
2
]
=
boxes
[
box_idx
+
2
]
<
img_width
-
1
:
static_cast
<
T
>
(
img_width
-
1
);
?
boxes
[
box_idx
+
2
]
boxes
[
box_idx
+
3
]
=
boxes
[
box_idx
+
3
]
<
img_height
-
1
:
static_cast
<
T
>
(
img_width
-
1
);
?
boxes
[
box_idx
+
3
]
boxes
[
box_idx
+
3
]
=
boxes
[
box_idx
+
3
]
<
img_height
-
1
:
static_cast
<
T
>
(
img_height
-
1
);
?
boxes
[
box_idx
+
3
]
:
static_cast
<
T
>
(
img_height
-
1
);
}
}
}
template
<
typename
T
>
template
<
typename
T
>
...
@@ -86,6 +88,7 @@ class YoloBoxKernel : public framework::OpKernel<T> {
...
@@ -86,6 +88,7 @@ class YoloBoxKernel : public framework::OpKernel<T> {
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
int
class_num
=
ctx
.
Attr
<
int
>
(
"class_num"
);
float
conf_thresh
=
ctx
.
Attr
<
float
>
(
"conf_thresh"
);
float
conf_thresh
=
ctx
.
Attr
<
float
>
(
"conf_thresh"
);
int
downsample_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio"
);
int
downsample_ratio
=
ctx
.
Attr
<
int
>
(
"downsample_ratio"
);
bool
clip_bbox
=
ctx
.
Attr
<
bool
>
(
"clip_bbox"
);
const
int
n
=
input
->
dims
()[
0
];
const
int
n
=
input
->
dims
()[
0
];
const
int
h
=
input
->
dims
()[
2
];
const
int
h
=
input
->
dims
()[
2
];
...
@@ -130,8 +133,8 @@ class YoloBoxKernel : public framework::OpKernel<T> {
...
@@ -130,8 +133,8 @@ class YoloBoxKernel : public framework::OpKernel<T> {
GetYoloBox
<
T
>
(
box
,
input_data
,
anchors_data
,
l
,
k
,
j
,
h
,
input_size
,
GetYoloBox
<
T
>
(
box
,
input_data
,
anchors_data
,
l
,
k
,
j
,
h
,
input_size
,
box_idx
,
stride
,
img_height
,
img_width
);
box_idx
,
stride
,
img_height
,
img_width
);
box_idx
=
(
i
*
box_num
+
j
*
stride
+
k
*
w
+
l
)
*
4
;
box_idx
=
(
i
*
box_num
+
j
*
stride
+
k
*
w
+
l
)
*
4
;
CalcDetectionBox
<
T
>
(
boxes_data
,
box
,
box_idx
,
img_height
,
CalcDetectionBox
<
T
>
(
boxes_data
,
box
,
box_idx
,
img_height
,
img_width
,
img_width
);
clip_bbox
);
int
label_idx
=
int
label_idx
=
GetEntryIndex
(
i
,
j
,
k
*
w
+
l
,
an_num
,
an_stride
,
stride
,
5
);
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,
...
@@ -1023,6 +1023,7 @@ def yolo_box(x,
class_num
,
class_num
,
conf_thresh
,
conf_thresh
,
downsample_ratio
,
downsample_ratio
,
clip_bbox
=
True
,
name
=
None
):
name
=
None
):
"""
"""
${comment}
${comment}
...
@@ -1034,6 +1035,7 @@ def yolo_box(x,
...
@@ -1034,6 +1035,7 @@ def yolo_box(x,
class_num (int): ${class_num_comment}
class_num (int): ${class_num_comment}
conf_thresh (float): ${conf_thresh_comment}
conf_thresh (float): ${conf_thresh_comment}
downsample_ratio (int): ${downsample_ratio_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
name (string): The default value is None. Normally there is no need
for user to set this property. For more information,
for user to set this property. For more information,
please refer to :ref:`api_guide_Name`
please refer to :ref:`api_guide_Name`
...
@@ -1081,6 +1083,7 @@ def yolo_box(x,
...
@@ -1081,6 +1083,7 @@ def yolo_box(x,
"class_num"
:
class_num
,
"class_num"
:
class_num
,
"conf_thresh"
:
conf_thresh
,
"conf_thresh"
:
conf_thresh
,
"downsample_ratio"
:
downsample_ratio
,
"downsample_ratio"
:
downsample_ratio
,
"clip_bbox"
:
clip_bbox
,
}
}
helper
.
append_op
(
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):
...
@@ -32,6 +32,7 @@ def YoloBox(x, img_size, attrs):
class_num
=
attrs
[
'class_num'
]
class_num
=
attrs
[
'class_num'
]
conf_thresh
=
attrs
[
'conf_thresh'
]
conf_thresh
=
attrs
[
'conf_thresh'
]
downsample
=
attrs
[
'downsample'
]
downsample
=
attrs
[
'downsample'
]
clip_bbox
=
attrs
[
'clip_bbox'
]
input_size
=
downsample
*
h
input_size
=
downsample
*
h
x
=
x
.
reshape
((
n
,
an_num
,
5
+
class_num
,
h
,
w
)).
transpose
((
0
,
1
,
3
,
4
,
2
))
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):
...
@@ -64,13 +65,14 @@ def YoloBox(x, img_size, attrs):
pred_box
[:,
:,
2
]
=
pred_box
[:,
:,
2
]
*
img_size
[:,
1
][:,
np
.
newaxis
]
pred_box
[:,
:,
2
]
=
pred_box
[:,
:,
2
]
*
img_size
[:,
1
][:,
np
.
newaxis
]
pred_box
[:,
:,
3
]
=
pred_box
[:,
:,
3
]
*
img_size
[:,
0
][:,
np
.
newaxis
]
pred_box
[:,
:,
3
]
=
pred_box
[:,
:,
3
]
*
img_size
[:,
0
][:,
np
.
newaxis
]
for
i
in
range
(
len
(
pred_box
)):
if
clip_bbox
:
pred_box
[
i
,
:,
0
]
=
np
.
clip
(
pred_box
[
i
,
:,
0
],
0
,
np
.
inf
)
for
i
in
range
(
len
(
pred_box
)):
pred_box
[
i
,
:,
1
]
=
np
.
clip
(
pred_box
[
i
,
:,
1
],
0
,
np
.
inf
)
pred_box
[
i
,
:,
0
]
=
np
.
clip
(
pred_box
[
i
,
:,
0
],
0
,
np
.
inf
)
pred_box
[
i
,
:,
2
]
=
np
.
clip
(
pred_box
[
i
,
:,
2
],
-
np
.
inf
,
pred_box
[
i
,
:,
1
]
=
np
.
clip
(
pred_box
[
i
,
:,
1
],
0
,
np
.
inf
)
img_size
[
i
,
1
]
-
1
)
pred_box
[
i
,
:,
2
]
=
np
.
clip
(
pred_box
[
i
,
:,
2
],
-
np
.
inf
,
pred_box
[
i
,
:,
3
]
=
np
.
clip
(
pred_box
[
i
,
:,
3
],
-
np
.
inf
,
img_size
[
i
,
1
]
-
1
)
img_size
[
i
,
0
]
-
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
))
return
pred_box
,
pred_score
.
reshape
((
n
,
-
1
,
class_num
))
...
@@ -87,6 +89,7 @@ class TestYoloBoxOp(OpTest):
...
@@ -87,6 +89,7 @@ class TestYoloBoxOp(OpTest):
"class_num"
:
self
.
class_num
,
"class_num"
:
self
.
class_num
,
"conf_thresh"
:
self
.
conf_thresh
,
"conf_thresh"
:
self
.
conf_thresh
,
"downsample"
:
self
.
downsample
,
"downsample"
:
self
.
downsample
,
"clip_bbox"
:
self
.
clip_bbox
,
}
}
self
.
inputs
=
{
self
.
inputs
=
{
...
@@ -109,6 +112,20 @@ class TestYoloBoxOp(OpTest):
...
@@ -109,6 +112,20 @@ class TestYoloBoxOp(OpTest):
self
.
class_num
=
2
self
.
class_num
=
2
self
.
conf_thresh
=
0.5
self
.
conf_thresh
=
0.5
self
.
downsample
=
32
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
.
x_shape
=
(
self
.
batch_size
,
an_num
*
(
5
+
self
.
class_num
),
13
,
13
)
self
.
imgsize_shape
=
(
self
.
batch_size
,
2
)
self
.
imgsize_shape
=
(
self
.
batch_size
,
2
)
...
...
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