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48a5cccb
编写于
2月 15, 2019
作者:
Q
qingqing01
提交者:
GitHub
2月 15, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Fix debug mode in prior_box_op (#15702)
* Fix debug mode in prior_box_op * Refine code
上级
4c8feae4
变更
2
显示空白变更内容
内联
并排
Showing
2 changed file
with
36 addition
and
46 deletion
+36
-46
paddle/fluid/operators/detection/density_prior_box_op.h
paddle/fluid/operators/detection/density_prior_box_op.h
+6
-7
paddle/fluid/operators/detection/prior_box_op.h
paddle/fluid/operators/detection/prior_box_op.h
+30
-39
未找到文件。
paddle/fluid/operators/detection/density_prior_box_op.h
浏览文件 @
48a5cccb
...
@@ -72,7 +72,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -72,7 +72,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_MKLML
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#pragma omp parallel for
#endif
#endif
for
(
in
t
i
=
0
;
i
<
fixed_ratios
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
fixed_ratios
.
size
();
i
++
)
{
sqrt_fixed_ratios
.
push_back
(
sqrt
(
fixed_ratios
[
i
]));
sqrt_fixed_ratios
.
push_back
(
sqrt
(
fixed_ratios
[
i
]));
}
}
...
@@ -115,11 +115,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -115,11 +115,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
}
}
}
}
if
(
clip
)
{
if
(
clip
)
{
platform
::
Transform
<
platform
::
CPUDeviceContext
>
trans
;
T
*
dt
=
boxes
->
data
<
T
>
();
ClipFunctor
<
T
>
clip_func
;
std
::
transform
(
dt
,
dt
+
boxes
->
numel
(),
dt
,
[](
T
v
)
->
T
{
trans
(
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>(),
return
std
::
min
<
T
>
(
std
::
max
<
T
>
(
v
,
0.
),
1.
);
boxes
->
data
<
T
>
(),
boxes
->
data
<
T
>
()
+
boxes
->
numel
(),
});
boxes
->
data
<
T
>
(),
clip_func
);
}
}
framework
::
Tensor
var_t
;
framework
::
Tensor
var_t
;
var_t
.
mutable_data
<
T
>
(
var_t
.
mutable_data
<
T
>
(
...
@@ -141,7 +140,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -141,7 +140,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
#pragma omp parallel for collapse(2)
#pragma omp parallel for collapse(2)
#endif
#endif
for
(
int
i
=
0
;
i
<
box_num
;
++
i
)
{
for
(
int
i
=
0
;
i
<
box_num
;
++
i
)
{
for
(
in
t
j
=
0
;
j
<
variances
.
size
();
++
j
)
{
for
(
size_
t
j
=
0
;
j
<
variances
.
size
();
++
j
)
{
e_vars
(
i
,
j
)
=
variances
[
j
];
e_vars
(
i
,
j
)
=
variances
[
j
];
}
}
}
}
...
...
paddle/fluid/operators/detection/prior_box_op.h
浏览文件 @
48a5cccb
...
@@ -46,13 +46,6 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
...
@@ -46,13 +46,6 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
}
}
}
}
template
<
typename
T
>
struct
ClipFunctor
{
HOSTDEVICE
inline
T
operator
()(
T
in
)
const
{
return
std
::
min
<
T
>
(
std
::
max
<
T
>
(
in
,
0.
),
1.
);
}
};
template
<
typename
T
>
template
<
typename
T
>
class
PriorBoxOpKernel
:
public
framework
::
OpKernel
<
T
>
{
class
PriorBoxOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
public:
...
@@ -101,31 +94,30 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -101,31 +94,30 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
boxes
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
boxes
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
vars
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
vars
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
e_boxes
=
framework
::
EigenTensor
<
T
,
4
>::
From
(
*
boxes
);
T
*
b_t
=
boxes
->
data
<
T
>
(
);
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
;
T
center_y
=
(
h
+
offset
)
*
step_height
;
T
center_y
=
(
h
+
offset
)
*
step_height
;
T
box_width
,
box_height
;
T
box_width
,
box_height
;
int
idx
=
0
;
for
(
size_t
s
=
0
;
s
<
min_sizes
.
size
();
++
s
)
{
for
(
size_t
s
=
0
;
s
<
min_sizes
.
size
();
++
s
)
{
auto
min_size
=
min_sizes
[
s
];
auto
min_size
=
min_sizes
[
s
];
if
(
min_max_aspect_ratios_order
)
{
if
(
min_max_aspect_ratios_order
)
{
box_width
=
box_height
=
min_size
/
2.
;
box_width
=
box_height
=
min_size
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
b_t
[
0
]
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
b_t
[
1
]
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
b_t
[
2
]
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
b_t
[
3
]
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
b_t
+=
4
;
if
(
max_sizes
.
size
()
>
0
)
{
if
(
max_sizes
.
size
()
>
0
)
{
auto
max_size
=
max_sizes
[
s
];
auto
max_size
=
max_sizes
[
s
];
// square prior with size sqrt(minSize * maxSize)
// square prior with size sqrt(minSize * maxSize)
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
b_t
[
0
]
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
b_t
[
1
]
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
b_t
[
2
]
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
b_t
[
3
]
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
b_t
+=
4
;
}
}
// priors with different aspect ratios
// priors with different aspect ratios
for
(
size_t
r
=
0
;
r
<
aspect_ratios
.
size
();
++
r
)
{
for
(
size_t
r
=
0
;
r
<
aspect_ratios
.
size
();
++
r
)
{
...
@@ -135,11 +127,11 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -135,11 +127,11 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
}
}
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
b_t
[
0
]
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
b_t
[
1
]
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
b_t
[
2
]
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
b_t
[
3
]
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
b_t
+=
4
;
}
}
}
else
{
}
else
{
// priors with different aspect ratios
// priors with different aspect ratios
...
@@ -147,21 +139,21 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -147,21 +139,21 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
float
ar
=
aspect_ratios
[
r
];
float
ar
=
aspect_ratios
[
r
];
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
b_t
[
0
]
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
b_t
[
1
]
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
b_t
[
2
]
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
b_t
[
3
]
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
b_t
+=
4
;
}
}
if
(
max_sizes
.
size
()
>
0
)
{
if
(
max_sizes
.
size
()
>
0
)
{
auto
max_size
=
max_sizes
[
s
];
auto
max_size
=
max_sizes
[
s
];
// square prior with size sqrt(minSize * maxSize)
// square prior with size sqrt(minSize * maxSize)
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
b_t
[
0
]
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
b_t
[
1
]
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
b_t
[
2
]
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
b_t
[
3
]
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
b_t
+=
4
;
}
}
}
}
}
}
...
@@ -169,11 +161,10 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
...
@@ -169,11 +161,10 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
}
}
if
(
clip
)
{
if
(
clip
)
{
platform
::
Transform
<
platform
::
CPUDeviceContext
>
trans
;
T
*
dt
=
boxes
->
data
<
T
>
();
ClipFunctor
<
T
>
clip_func
;
std
::
transform
(
dt
,
dt
+
boxes
->
numel
(),
dt
,
[](
T
v
)
->
T
{
trans
(
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>(),
return
std
::
min
<
T
>
(
std
::
max
<
T
>
(
v
,
0.
),
1.
);
boxes
->
data
<
T
>
(),
boxes
->
data
<
T
>
()
+
boxes
->
numel
(),
});
boxes
->
data
<
T
>
(),
clip_func
);
}
}
framework
::
Tensor
var_t
;
framework
::
Tensor
var_t
;
...
...
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