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体验新版 GitCode,发现更多精彩内容 >>
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bec68fa0
编写于
2月 03, 2019
作者:
T
Tao Luo
提交者:
GitHub
2月 03, 2019
浏览文件
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差异文件
Merge pull request #15637 from jerrywgz/refine_box_coder
speed up box_coder in CPU
上级
7ddf4e2c
ceb412b0
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
60 addition
and
88 deletion
+60
-88
paddle/fluid/operators/detection/box_coder_op.cc
paddle/fluid/operators/detection/box_coder_op.cc
+6
-14
paddle/fluid/operators/detection/box_coder_op.cu
paddle/fluid/operators/detection/box_coder_op.cu
+2
-8
paddle/fluid/operators/detection/box_coder_op.h
paddle/fluid/operators/detection/box_coder_op.h
+44
-33
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+4
-4
python/paddle/fluid/tests/unittests/test_box_coder_op.py
python/paddle/fluid/tests/unittests/test_box_coder_op.py
+4
-29
未找到文件。
paddle/fluid/operators/detection/box_coder_op.cc
浏览文件 @
bec68fa0
...
...
@@ -38,20 +38,12 @@ class BoxCoderOp : public framework::OperatorWithKernel {
"The shape of PriorBox is [N, 4]"
);
if
(
ctx
->
HasInput
(
"PriorBoxVar"
))
{
auto
prior_box_var_dims
=
ctx
->
GetInputDim
(
"PriorBoxVar"
);
PADDLE_ENFORCE
(
prior_box_var_dims
.
size
()
==
1
||
prior_box_var_dims
.
size
()
==
2
,
"Input(PriorBoxVar) of BoxCoderOp should be 1 or 2."
);
if
(
prior_box_var_dims
.
size
()
==
1
)
{
PADDLE_ENFORCE_EQ
(
prior_box_var_dims
[
0
],
4
,
"The 1st dimension of Input(PriorBoxVar) should be 4"
"when the rank is 1."
);
}
else
{
PADDLE_ENFORCE_EQ
(
prior_box_dims
,
prior_box_var_dims
,
"The dimension of Input(PriorBoxVar) should be equal to"
"the dimension of Input(PriorBox when the rank is 2.)"
);
}
PADDLE_ENFORCE
(
prior_box_var_dims
.
size
()
==
2
,
"Input(PriorBoxVar) of BoxCoderOp should be 2."
);
PADDLE_ENFORCE_EQ
(
prior_box_dims
,
prior_box_var_dims
,
"The dimension of Input(PriorBoxVar) should be equal to"
"the dimension of Input(PriorBox) when the rank is 2."
);
}
}
...
...
paddle/fluid/operators/detection/box_coder_op.cu
浏览文件 @
bec68fa0
...
...
@@ -56,10 +56,7 @@ __global__ void EncodeCenterSizeKernel(
output
[
idx
*
len
+
2
]
=
log
(
fabs
(
target_box_width
/
prior_box_width
));
output
[
idx
*
len
+
3
]
=
log
(
fabs
(
target_box_height
/
prior_box_height
));
if
(
prior_box_var_data
)
{
int
prior_var_offset
=
0
;
if
(
prior_box_var_size
==
2
)
{
prior_var_offset
=
col_idx
*
len
;
}
int
prior_var_offset
=
col_idx
*
len
;
output
[
idx
*
len
]
/=
prior_box_var_data
[
prior_var_offset
];
output
[
idx
*
len
+
1
]
/=
prior_box_var_data
[
prior_var_offset
+
1
];
output
[
idx
*
len
+
2
]
/=
prior_box_var_data
[
prior_var_offset
+
2
];
...
...
@@ -99,10 +96,7 @@ __global__ void DecodeCenterSizeKernel(
T
box_var_x
=
T
(
1
),
box_var_y
=
T
(
1
);
T
box_var_w
=
T
(
1
),
box_var_h
=
T
(
1
);
if
(
prior_box_var_data
)
{
int
prior_var_offset
=
0
;
if
(
prior_box_var_size
==
2
)
{
prior_var_offset
=
axis
==
0
?
col_idx
*
len
:
row_idx
*
len
;
}
int
prior_var_offset
=
axis
==
0
?
col_idx
*
len
:
row_idx
*
len
;
box_var_x
=
prior_box_var_data
[
prior_var_offset
];
box_var_y
=
prior_box_var_data
[
prior_var_offset
+
1
];
box_var_w
=
prior_box_var_data
[
prior_var_offset
+
2
];
...
...
paddle/fluid/operators/detection/box_coder_op.h
浏览文件 @
bec68fa0
...
...
@@ -79,10 +79,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
output
[
offset
+
3
]
=
std
::
log
(
std
::
fabs
(
target_box_height
/
prior_box_height
));
if
(
prior_box_var
)
{
int
prior_var_offset
=
0
;
if
(
prior_box_var
->
dims
().
size
()
==
2
)
{
prior_var_offset
=
j
*
len
;
}
int
prior_var_offset
=
j
*
len
;
output
[
offset
]
/=
prior_box_var_data
[
prior_var_offset
];
output
[
offset
+
1
]
/=
prior_box_var_data
[
prior_var_offset
+
1
];
output
[
offset
+
2
]
/=
prior_box_var_data
[
prior_var_offset
+
2
];
...
...
@@ -95,11 +92,12 @@ class BoxCoderKernel : public framework::OpKernel<T> {
}
}
}
template
<
int
axis
,
int
var_size
>
void
DecodeCenterSize
(
const
framework
::
Tensor
*
target_box
,
const
framework
::
Tensor
*
prior_box
,
const
framework
::
Tensor
*
prior_box_var
,
const
bool
normalized
,
const
int
axis
,
const
std
::
vector
<
float
>
variance
,
T
*
output
)
const
{
const
bool
normalized
,
std
::
vector
<
float
>
variance
,
T
*
output
)
const
{
int64_t
row
=
target_box
->
dims
()[
0
];
int64_t
col
=
target_box
->
dims
()[
1
];
int64_t
len
=
target_box
->
dims
()[
2
];
...
...
@@ -107,19 +105,17 @@ class BoxCoderKernel : public framework::OpKernel<T> {
auto
*
target_box_data
=
target_box
->
data
<
T
>
();
auto
*
prior_box_data
=
prior_box
->
data
<
T
>
();
const
T
*
prior_box_var_data
=
nullptr
;
if
(
prior_box_var
)
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
if
(
var_size
==
2
)
prior_box_var_data
=
prior_box_var
->
data
<
T
>
();
int
prior_box_offset
=
0
;
T
var_data
[
4
]
=
{
1.
,
1.
,
1.
,
1.
};
T
*
var_ptr
=
var_data
;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
size_t
offset
=
i
*
col
*
len
+
j
*
len
;
if
(
axis
==
0
)
{
prior_box_offset
=
j
*
len
;
}
else
if
(
axis
==
1
)
{
prior_box_offset
=
i
*
len
;
}
prior_box_offset
=
axis
==
0
?
j
*
len
:
i
*
len
;
T
prior_box_width
=
prior_box_data
[
prior_box_offset
+
2
]
-
prior_box_data
[
prior_box_offset
]
+
(
normalized
==
false
);
...
...
@@ -133,26 +129,18 @@ class BoxCoderKernel : public framework::OpKernel<T> {
T
target_box_center_x
=
0
,
target_box_center_y
=
0
;
T
target_box_width
=
0
,
target_box_height
=
0
;
T
box_var_x
=
T
(
1
),
box_var_y
=
T
(
1
);
T
box_var_w
=
T
(
1
),
box_var_h
=
T
(
1
);
if
(
prior_box_var
)
{
int
prior_var_offset
=
0
;
if
(
prior_box_var
->
dims
().
size
()
==
2
)
{
if
(
axis
==
0
)
prior_var_offset
=
j
*
len
;
else
if
(
axis
==
1
)
prior_var_offset
=
i
*
len
;
}
box_var_x
=
prior_box_var_data
[
prior_var_offset
];
box_var_y
=
prior_box_var_data
[
prior_var_offset
+
1
];
box_var_w
=
prior_box_var_data
[
prior_var_offset
+
2
];
box_var_h
=
prior_box_var_data
[
prior_var_offset
+
3
];
}
else
if
(
!
(
variance
.
empty
()))
{
box_var_x
=
static_cast
<
T
>
(
variance
[
0
]);
box_var_y
=
static_cast
<
T
>
(
variance
[
1
]);
box_var_w
=
static_cast
<
T
>
(
variance
[
2
]);
box_var_h
=
static_cast
<
T
>
(
variance
[
3
]);
int
prior_var_offset
=
axis
==
0
?
j
*
len
:
i
*
len
;
if
(
var_size
==
2
)
{
std
::
memcpy
(
var_ptr
,
prior_box_var_data
+
prior_var_offset
,
4
*
sizeof
(
T
));
}
else
if
(
var_size
==
1
)
{
var_ptr
=
reinterpret_cast
<
T
*>
(
variance
.
data
());
}
T
box_var_x
=
*
var_ptr
;
T
box_var_y
=
*
(
var_ptr
+
1
);
T
box_var_w
=
*
(
var_ptr
+
2
);
T
box_var_h
=
*
(
var_ptr
+
3
);
target_box_center_x
=
box_var_x
*
target_box_data
[
offset
]
*
prior_box_width
+
prior_box_center_x
;
...
...
@@ -211,8 +199,31 @@ class BoxCoderKernel : public framework::OpKernel<T> {
EncodeCenterSize
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output
);
}
else
if
(
code_type
==
BoxCodeType
::
kDecodeCenterSize
)
{
DecodeCenterSize
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
axis
,
variance
,
output
);
if
(
prior_box_var
)
{
if
(
axis
==
0
)
{
DecodeCenterSize
<
0
,
2
>
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output
);
}
else
{
DecodeCenterSize
<
1
,
2
>
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output
);
}
}
else
if
(
!
(
variance
.
empty
()))
{
if
(
axis
==
0
)
{
DecodeCenterSize
<
0
,
1
>
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output
);
}
else
{
DecodeCenterSize
<
1
,
1
>
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output
);
}
}
else
{
if
(
axis
==
0
)
{
DecodeCenterSize
<
0
,
0
>
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output
);
}
else
{
DecodeCenterSize
<
1
,
0
>
(
target_box
,
prior_box
,
prior_box_var
,
normalized
,
variance
,
output
);
}
}
}
}
};
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
bec68fa0
...
...
@@ -397,10 +397,10 @@ def box_coder(prior_box,
input is image feature map, they are close to
the origin of the coordinate system. [xmax, ymax]
is the right bottom coordinate of the anchor box.
prior_box_var(Variable|list
): prior_box_var supports two types of input.
One is variable with shape [M, 4] holds M group.
The other one is list consist of 4 elements
shared by all boxes.
prior_box_var(Variable|list
|None): prior_box_var supports two types
of input. One is variable with shape [M, 4]
holds M group. The other one is list consist of
4 elements
shared by all boxes.
target_box(Variable): This input can be a 2-D LoDTensor with shape
[N, 4] when code_type is 'encode_center_size'.
This input also can be a 3-D Tensor with shape
...
...
python/paddle/fluid/tests/unittests/test_box_coder_op.py
浏览文件 @
bec68fa0
...
...
@@ -34,7 +34,9 @@ def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0):
pb_y
=
pb_y
.
reshape
(
shape
)
if
pb_v
.
ndim
==
2
:
pb_v
=
pb_v
.
reshape
(
1
,
pb_v
.
shape
[
0
],
pb_v
.
shape
[
1
])
var_shape
=
(
1
,
pb_v
.
shape
[
0
],
pb_v
.
shape
[
1
])
if
axis
==
0
else
(
pb_v
.
shape
[
0
],
1
,
pb_v
.
shape
[
1
])
pb_v
=
pb_v
.
reshape
(
var_shape
)
if
pb_v
.
ndim
==
1
:
tb_x
=
pb_v
[
0
]
*
t_box
[:,
:,
0
]
*
pb_w
+
pb_x
tb_y
=
pb_v
[
1
]
*
t_box
[:,
:,
1
]
*
pb_h
+
pb_y
...
...
@@ -125,33 +127,6 @@ class TestBoxCoderOp(OpTest):
self
.
outputs
=
{
'OutputBox'
:
output_box
}
class
TestBoxCoderOpWithOneRankVar
(
OpTest
):
def
test_check_output
(
self
):
self
.
check_output
()
def
setUp
(
self
):
self
.
op_type
=
"box_coder"
lod
=
[[
1
,
1
,
1
,
1
,
1
]]
prior_box
=
np
.
random
.
random
((
81
,
4
)).
astype
(
'float32'
)
prior_box_var
=
np
.
random
.
random
((
4
)).
astype
(
'float32'
)
target_box
=
np
.
random
.
random
((
20
,
81
,
4
)).
astype
(
'float32'
)
code_type
=
"DecodeCenterSize"
box_normalized
=
False
output_box
=
batch_box_coder
(
prior_box
,
prior_box_var
,
target_box
,
lod
[
0
],
code_type
,
box_normalized
)
self
.
inputs
=
{
'PriorBox'
:
prior_box
,
'PriorBoxVar'
:
prior_box_var
,
'TargetBox'
:
target_box
,
}
self
.
attrs
=
{
'code_type'
:
'decode_center_size'
,
'box_normalized'
:
False
}
self
.
outputs
=
{
'OutputBox'
:
output_box
}
class
TestBoxCoderOpWithoutBoxVar
(
OpTest
):
def
test_check_output
(
self
):
self
.
check_output
()
...
...
@@ -210,7 +185,7 @@ class TestBoxCoderOpWithAxis(OpTest):
self
.
op_type
=
"box_coder"
lod
=
[[
1
,
1
,
1
,
1
,
1
]]
prior_box
=
np
.
random
.
random
((
30
,
4
)).
astype
(
'float32'
)
prior_box_var
=
np
.
random
.
random
((
4
)).
astype
(
'float32'
)
prior_box_var
=
np
.
random
.
random
((
30
,
4
)).
astype
(
'float32'
)
target_box
=
np
.
random
.
random
((
30
,
81
,
4
)).
astype
(
'float32'
)
code_type
=
"DecodeCenterSize"
box_normalized
=
False
...
...
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