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4b3c6612
编写于
1月 30, 2019
作者:
L
lidanqing-intel
浏览文件
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差异文件
optimize density_prior_box_op.h for cpu
test=develop
上级
67e4450c
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
38 addition
and
26 deletion
+38
-26
paddle/fluid/operators/detection/density_prior_box_op.h
paddle/fluid/operators/detection/density_prior_box_op.h
+38
-26
未找到文件。
paddle/fluid/operators/detection/density_prior_box_op.h
浏览文件 @
4b3c6612
...
@@ -52,6 +52,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -52,6 +52,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
step_height
=
step_h
;
step_height
=
step_h
;
}
}
int
num_priors
=
0
;
int
num_priors
=
0
;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for reduction(+ : num_priors)
#endif
for
(
size_t
i
=
0
;
i
<
densities
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
densities
.
size
();
++
i
)
{
num_priors
+=
(
fixed_ratios
.
size
())
*
(
pow
(
densities
[
i
],
2
));
num_priors
+=
(
fixed_ratios
.
size
())
*
(
pow
(
densities
[
i
],
2
));
}
}
...
@@ -64,6 +68,17 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -64,6 +68,17 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
auto
e_boxes
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
boxes
).
setConstant
(
0.0
);
auto
e_boxes
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
boxes
).
setConstant
(
0.0
);
int
step_average
=
static_cast
<
int
>
((
step_width
+
step_height
)
*
0.5
);
int
step_average
=
static_cast
<
int
>
((
step_width
+
step_height
)
*
0.5
);
std
::
vector
<
float
>
sqrt_fixed_ratios
;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
for
(
int
i
=
0
;
i
<
fixed_ratios
.
size
();
i
++
)
{
sqrt_fixed_ratios
.
push_back
(
sqrt
(
fixed_ratios
[
i
]));
}
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for
(
int
h
=
0
;
h
<
feature_height
;
++
h
)
{
for
(
int
h
=
0
;
h
<
feature_height
;
++
h
)
{
for
(
int
w
=
0
;
w
<
feature_width
;
++
w
)
{
for
(
int
w
=
0
;
w
<
feature_width
;
++
w
)
{
T
center_x
=
(
w
+
offset
)
*
step_width
;
T
center_x
=
(
w
+
offset
)
*
step_width
;
...
@@ -73,34 +88,25 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -73,34 +88,25 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
for
(
size_t
s
=
0
;
s
<
fixed_sizes
.
size
();
++
s
)
{
for
(
size_t
s
=
0
;
s
<
fixed_sizes
.
size
();
++
s
)
{
auto
fixed_size
=
fixed_sizes
[
s
];
auto
fixed_size
=
fixed_sizes
[
s
];
int
density
=
densities
[
s
];
int
density
=
densities
[
s
];
int
shift
=
step_average
/
density
;
// Generate density prior boxes with fixed ratios.
// Generate density prior boxes with fixed ratios.
for
(
size_t
r
=
0
;
r
<
fixed_ratios
.
size
();
++
r
)
{
for
(
size_t
r
=
0
;
r
<
fixed_ratios
.
size
();
++
r
)
{
float
ar
=
fixed_ratios
[
r
];
float
box_width_ratio
=
fixed_size
*
sqrt_
fixed_ratios
[
r
];
int
shift
=
step_average
/
density
;
float
box_height_ratio
=
fixed_size
/
sqrt_fixed_ratios
[
r
]
;
float
box_width_ratio
=
fixed_size
*
sqrt
(
ar
)
;
float
density_center_x
=
center_x
-
step_average
/
2.
+
shift
/
2.
;
float
box_height_ratio
=
fixed_size
/
sqrt
(
ar
)
;
float
density_center_y
=
center_y
-
step_average
/
2.
+
shift
/
2.
;
for
(
int
di
=
0
;
di
<
density
;
++
di
)
{
for
(
int
di
=
0
;
di
<
density
;
++
di
)
{
for
(
int
dj
=
0
;
dj
<
density
;
++
dj
)
{
for
(
int
dj
=
0
;
dj
<
density
;
++
dj
)
{
float
center_x_temp
=
float
center_x_temp
=
density_center_x
+
dj
*
shift
;
center_x
-
step_average
/
2.
+
shift
/
2.
+
dj
*
shift
;
float
center_y_temp
=
density_center_y
+
di
*
shift
;
float
center_y_temp
=
e_boxes
(
h
,
w
,
idx
,
0
)
=
std
::
max
(
center_y
-
step_average
/
2.
+
shift
/
2.
+
di
*
shift
;
(
center_x_temp
-
box_width_ratio
/
2.
)
/
img_width
,
0.
);
e_boxes
(
h
,
w
,
idx
,
0
)
=
e_boxes
(
h
,
w
,
idx
,
1
)
=
std
::
max
(
(
center_x_temp
-
box_width_ratio
/
2.
)
/
img_width
>=
0
(
center_y_temp
-
box_height_ratio
/
2.
)
/
img_height
,
0.
);
?
(
center_x_temp
-
box_width_ratio
/
2.
)
/
img_width
e_boxes
(
h
,
w
,
idx
,
2
)
=
std
::
min
(
:
0
;
(
center_x_temp
+
box_width_ratio
/
2.
)
/
img_width
,
1.
);
e_boxes
(
h
,
w
,
idx
,
1
)
=
e_boxes
(
h
,
w
,
idx
,
3
)
=
std
::
min
(
(
center_y_temp
-
box_height_ratio
/
2.
)
/
img_height
>=
0
(
center_y_temp
+
box_height_ratio
/
2.
)
/
img_height
,
1.
);
?
(
center_y_temp
-
box_height_ratio
/
2.
)
/
img_height
:
0
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x_temp
+
box_width_ratio
/
2.
)
/
img_width
<=
1
?
(
center_x_temp
+
box_width_ratio
/
2.
)
/
img_width
:
1
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y_temp
+
box_height_ratio
/
2.
)
/
img_height
<=
1
?
(
center_y_temp
+
box_height_ratio
/
2.
)
/
img_height
:
1
;
idx
++
;
idx
++
;
}
}
}
}
...
@@ -131,8 +137,14 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -131,8 +137,14 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
vars
->
Resize
({
box_num
,
static_cast
<
int
>
(
variances
.
size
())});
vars
->
Resize
({
box_num
,
static_cast
<
int
>
(
variances
.
size
())});
auto
e_vars
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
>::
From
(
*
vars
);
auto
e_vars
=
framework
::
EigenMatrix
<
T
,
Eigen
::
RowMajor
>::
From
(
*
vars
);
#ifdef PADDLE_WITH_MKLML
e_vars
=
var_et
.
broadcast
(
Eigen
::
DSizes
<
int
,
2
>
(
box_num
,
1
));
#pragma omp parallel for collapse(2)
#endif
for
(
int
i
=
0
;
i
<
box_num
;
++
i
)
{
for
(
int
j
=
0
;
j
<
variances
.
size
();
++
j
)
{
e_vars
(
i
,
j
)
=
variances
[
j
];
}
}
vars
->
Resize
(
var_dim
);
vars
->
Resize
(
var_dim
);
boxes
->
Resize
(
box_dim
);
boxes
->
Resize
(
box_dim
);
...
...
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