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体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
a84a580e
编写于
4月 04, 2018
作者:
Q
qingqing01
提交者:
GitHub
4月 04, 2018
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add CUDA kernel for prior_box_op. (#9553)
上级
d139f2ca
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
207 addition
and
68 deletion
+207
-68
paddle/fluid/operators/prior_box_op.cc
paddle/fluid/operators/prior_box_op.cc
+3
-4
paddle/fluid/operators/prior_box_op.cu
paddle/fluid/operators/prior_box_op.cu
+167
-0
paddle/fluid/operators/prior_box_op.h
paddle/fluid/operators/prior_box_op.h
+10
-35
python/paddle/fluid/tests/unittests/test_prior_box_op.py
python/paddle/fluid/tests/unittests/test_prior_box_op.py
+27
-29
未找到文件。
paddle/fluid/operators/prior_box_op.cc
浏览文件 @
a84a580e
...
...
@@ -73,7 +73,7 @@ class PriorBoxOp : public framework::OperatorWithKernel {
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"Input"
)
->
type
()),
platform
::
CPUPlace
());
ctx
.
device_context
());
}
};
...
...
@@ -171,6 +171,5 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR
(
prior_box
,
ops
::
PriorBoxOp
,
ops
::
PriorBoxOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
prior_box
,
ops
::
PriorBoxOpKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
PriorBoxOpKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
prior_box
,
ops
::
PriorBoxOpKernel
<
float
>
,
ops
::
PriorBoxOpKernel
<
double
>
);
paddle/fluid/operators/prior_box_op.cu
0 → 100644
浏览文件 @
a84a580e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/prior_box_op.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
__device__
inline
T
clip
(
T
in
)
{
return
min
(
max
(
in
,
0.
),
1.
);
}
template
<
typename
T
>
__global__
void
GenPriorBox
(
T
*
out
,
const
T
*
aspect_ratios
,
const
int
height
,
const
int
width
,
const
int
im_height
,
const
int
im_width
,
const
int
as_num
,
const
T
offset
,
const
T
step_width
,
const
T
step_height
,
const
T
*
min_sizes
,
const
T
*
max_sizes
,
const
int
min_num
,
bool
is_clip
)
{
int
num_priors
=
max_sizes
?
as_num
*
min_num
+
min_num
:
as_num
*
min_num
;
int
box_num
=
height
*
width
*
num_priors
;
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
box_num
;
i
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
h
=
i
/
(
num_priors
*
width
);
int
w
=
(
i
/
num_priors
)
%
width
;
int
p
=
i
%
num_priors
;
int
m
=
max_sizes
?
p
/
(
as_num
+
1
)
:
p
/
as_num
;
T
cx
=
(
w
+
offset
)
*
step_width
;
T
cy
=
(
h
+
offset
)
*
step_height
;
T
bw
,
bh
;
T
min_size
=
min_sizes
[
m
];
if
(
max_sizes
)
{
int
s
=
p
%
(
as_num
+
1
);
if
(
s
<
as_num
)
{
T
ar
=
aspect_ratios
[
s
];
bw
=
min_size
*
sqrt
(
ar
)
/
2.
;
bh
=
min_size
/
sqrt
(
ar
)
/
2.
;
}
else
{
T
max_size
=
max_sizes
[
m
];
bw
=
sqrt
(
min_size
*
max_size
)
/
2.
;
bh
=
bw
;
}
}
else
{
int
s
=
p
%
as_num
;
T
ar
=
aspect_ratios
[
s
];
bw
=
min_size
*
sqrt
(
ar
)
/
2.
;
bh
=
min_size
/
sqrt
(
ar
)
/
2.
;
}
T
xmin
=
(
cx
-
bw
)
/
im_width
;
T
ymin
=
(
cy
-
bh
)
/
im_height
;
T
xmax
=
(
cx
+
bw
)
/
im_width
;
T
ymax
=
(
cy
+
bh
)
/
im_height
;
out
[
i
*
4
]
=
is_clip
?
clip
<
T
>
(
xmin
)
:
xmin
;
out
[
i
*
4
+
1
]
=
is_clip
?
clip
<
T
>
(
ymin
)
:
ymin
;
out
[
i
*
4
+
2
]
=
is_clip
?
clip
<
T
>
(
xmax
)
:
xmax
;
out
[
i
*
4
+
3
]
=
is_clip
?
clip
<
T
>
(
ymax
)
:
ymax
;
}
}
template
<
typename
T
>
__global__
void
SetVariance
(
T
*
out
,
const
T
*
var
,
const
int
vnum
,
const
int
num
)
{
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
num
;
i
+=
blockDim
.
x
*
gridDim
.
x
)
{
out
[
i
]
=
var
[
i
%
vnum
];
}
}
template
<
typename
T
>
class
PriorBoxOpCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
paddle
::
framework
::
Tensor
>
(
"Input"
);
auto
*
image
=
ctx
.
Input
<
paddle
::
framework
::
Tensor
>
(
"Image"
);
auto
*
boxes
=
ctx
.
Output
<
paddle
::
framework
::
Tensor
>
(
"Boxes"
);
auto
*
vars
=
ctx
.
Output
<
paddle
::
framework
::
Tensor
>
(
"Variances"
);
auto
min_sizes
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"min_sizes"
);
auto
max_sizes
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"max_sizes"
);
auto
input_aspect_ratio
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"aspect_ratios"
);
auto
variances
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"variances"
);
auto
flip
=
ctx
.
Attr
<
bool
>
(
"flip"
);
auto
clip
=
ctx
.
Attr
<
bool
>
(
"clip"
);
std
::
vector
<
float
>
aspect_ratios
;
ExpandAspectRatios
(
input_aspect_ratio
,
flip
,
aspect_ratios
);
T
step_w
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"step_w"
));
T
step_h
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"step_h"
));
T
offset
=
static_cast
<
T
>
(
ctx
.
Attr
<
float
>
(
"offset"
));
auto
im_width
=
image
->
dims
()[
3
];
auto
im_height
=
image
->
dims
()[
2
];
auto
width
=
input
->
dims
()[
3
];
auto
height
=
input
->
dims
()[
2
];
T
step_width
,
step_height
;
if
(
step_w
==
0
||
step_h
==
0
)
{
step_width
=
static_cast
<
T
>
(
im_width
)
/
width
;
step_height
=
static_cast
<
T
>
(
im_height
)
/
height
;
}
else
{
step_width
=
step_w
;
step_height
=
step_h
;
}
int
num_priors
=
aspect_ratios
.
size
()
*
min_sizes
.
size
();
if
(
max_sizes
.
size
()
>
0
)
{
num_priors
+=
max_sizes
.
size
();
}
int
min_num
=
static_cast
<
int
>
(
min_sizes
.
size
());
int
box_num
=
width
*
height
*
num_priors
;
int
block
=
512
;
int
grid
=
(
box_num
+
block
-
1
)
/
block
;
auto
stream
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>().
stream
();
boxes
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
vars
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
framework
::
Tensor
r
;
framework
::
TensorFromVector
(
aspect_ratios
,
ctx
.
device_context
(),
&
r
);
framework
::
Tensor
min
;
framework
::
TensorFromVector
(
min_sizes
,
ctx
.
device_context
(),
&
min
);
T
*
max_data
=
nullptr
;
framework
::
Tensor
max
;
if
(
max_sizes
.
size
()
>
0
)
{
framework
::
TensorFromVector
(
max_sizes
,
ctx
.
device_context
(),
&
max
);
max_data
=
max
.
data
<
T
>
();
}
GenPriorBox
<
T
><<<
grid
,
block
,
0
,
stream
>>>
(
boxes
->
data
<
T
>
(),
r
.
data
<
T
>
(),
height
,
width
,
im_height
,
im_width
,
aspect_ratios
.
size
(),
offset
,
step_width
,
step_height
,
min
.
data
<
T
>
(),
max_data
,
min_num
,
clip
);
framework
::
Tensor
v
;
framework
::
TensorFromVector
(
variances
,
ctx
.
device_context
(),
&
v
);
grid
=
(
box_num
*
4
+
block
-
1
)
/
block
;
SetVariance
<
T
><<<
grid
,
block
,
0
,
stream
>>>
(
vars
->
data
<
T
>
(),
v
.
data
<
T
>
(),
variances
.
size
(),
box_num
*
4
);
}
};
// namespace operators
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
prior_box
,
ops
::
PriorBoxOpCUDAKernel
<
float
>
,
ops
::
PriorBoxOpCUDAKernel
<
double
>
);
paddle/fluid/operators/prior_box_op.h
浏览文件 @
a84a580e
...
...
@@ -51,7 +51,7 @@ struct ClipFunctor {
}
};
template
<
typename
Place
,
typename
T
>
template
<
typename
T
>
class
PriorBoxOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
...
...
@@ -106,49 +106,24 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
int
idx
=
0
;
for
(
size_t
s
=
0
;
s
<
min_sizes
.
size
();
++
s
)
{
auto
min_size
=
min_sizes
[
s
];
// first prior: aspect_ratio = 1, size = min_size
box_width
=
box_height
=
min_size
/
2.
;
// xmin
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
// ymin
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
// xmax
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
// ymax
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
if
(
max_sizes
.
size
()
>
0
)
{
auto
max_size
=
max_sizes
[
s
];
// second prior: aspect_ratio = 1,
// size = sqrt(min_size * max_size)
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
// xmin
// priors with different aspect ratios
for
(
size_t
r
=
0
;
r
<
aspect_ratios
.
size
();
++
r
)
{
float
ar
=
aspect_ratios
[
r
];
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
// ymin
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
// xmax
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
// ymax
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
}
// rest of priors
for
(
size_t
r
=
0
;
r
<
aspect_ratios
.
size
();
++
r
)
{
float
ar
=
aspect_ratios
[
r
];
if
(
fabs
(
ar
-
1.
)
<
1e-6
)
{
continue
;
}
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
// xmin
if
(
max_sizes
.
size
()
>
0
)
{
auto
max_size
=
max_sizes
[
s
];
// square prior with size sqrt(minSize * maxSize)
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
// ymin
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
// xmax
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
// ymax
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
}
...
...
python/paddle/fluid/tests/unittests/test_prior_box_op.py
浏览文件 @
a84a580e
...
...
@@ -28,7 +28,6 @@ class TestPriorBoxOp(OpTest):
self
.
attrs
=
{
'min_sizes'
:
self
.
min_sizes
,
'max_sizes'
:
self
.
max_sizes
,
'aspect_ratios'
:
self
.
aspect_ratios
,
'variances'
:
self
.
variances
,
'flip'
:
self
.
flip
,
...
...
@@ -37,25 +36,28 @@ class TestPriorBoxOp(OpTest):
'step_h'
:
self
.
step_h
,
'offset'
:
self
.
offset
}
if
len
(
self
.
max_sizes
)
>
0
:
self
.
attrs
[
'max_sizes'
]
=
self
.
max_sizes
self
.
outputs
=
{
'Boxes'
:
self
.
out_boxes
,
'Variances'
:
self
.
out_var
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
return
def
setUp
(
self
):
self
.
op_type
=
"prior_box"
self
.
set_data
()
def
set_max_sizes
(
self
):
max_sizes
=
[
5
,
10
]
self
.
max_sizes
=
np
.
array
(
max_sizes
).
astype
(
'float32'
).
tolist
()
def
init_test_params
(
self
):
self
.
layer_w
=
4
self
.
layer_h
=
4
self
.
layer_w
=
32
self
.
layer_h
=
32
self
.
image_w
=
2
0
self
.
image_h
=
2
0
self
.
image_w
=
4
0
self
.
image_h
=
4
0
self
.
step_w
=
float
(
self
.
image_w
)
/
float
(
self
.
layer_w
)
self
.
step_h
=
float
(
self
.
image_h
)
/
float
(
self
.
layer_h
)
...
...
@@ -66,8 +68,7 @@ class TestPriorBoxOp(OpTest):
self
.
min_sizes
=
[
2
,
4
]
self
.
min_sizes
=
np
.
array
(
self
.
min_sizes
).
astype
(
'float32'
).
tolist
()
self
.
max_sizes
=
[
5
,
10
]
self
.
max_sizes
=
np
.
array
(
self
.
max_sizes
).
astype
(
'float32'
).
tolist
()
self
.
set_max_sizes
()
self
.
aspect_ratios
=
[
2.0
,
3.0
]
self
.
flip
=
True
self
.
real_aspect_ratios
=
[
1
,
2.0
,
1.0
/
2.0
,
3.0
,
1.0
/
3.0
]
...
...
@@ -79,7 +80,7 @@ class TestPriorBoxOp(OpTest):
self
.
clip
=
True
self
.
num_priors
=
len
(
self
.
real_aspect_ratios
)
*
len
(
self
.
min_sizes
)
if
len
(
self
.
max_sizes
)
>
1
:
if
len
(
self
.
max_sizes
)
>
0
:
self
.
num_priors
+=
len
(
self
.
max_sizes
)
self
.
offset
=
0.5
...
...
@@ -105,35 +106,27 @@ class TestPriorBoxOp(OpTest):
idx
=
0
for
s
in
range
(
len
(
self
.
min_sizes
)):
min_size
=
self
.
min_sizes
[
s
]
c_w
=
c_h
=
min_size
/
2.
out_boxes
[
h
,
w
,
idx
,
:]
=
[
(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
if
len
(
self
.
max_sizes
)
>
0
:
max_size
=
self
.
max_sizes
[
s
]
# second prior: aspect_ratio = 1,
c_w
=
c_h
=
math
.
sqrt
(
min_size
*
max_size
)
/
2
# rest of priors
for
r
in
range
(
len
(
self
.
real_aspect_ratios
)):
ar
=
self
.
real_aspect_ratios
[
r
]
c_w
=
min_size
*
math
.
sqrt
(
ar
)
/
2
c_h
=
(
min_size
/
math
.
sqrt
(
ar
))
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
# rest of priors
for
r
in
range
(
len
(
self
.
real_aspect_ratios
)):
ar
=
self
.
real_aspect_ratios
[
r
]
if
math
.
fabs
(
ar
-
1.
)
<
1e-6
:
continue
c_w
=
min_size
*
math
.
sqrt
(
ar
)
/
2
c_h
=
(
min_size
/
math
.
sqrt
(
ar
))
/
2
if
len
(
self
.
max_sizes
)
>
0
:
max_size
=
self
.
max_sizes
[
s
]
# second prior: aspect_ratio = 1,
c_w
=
c_h
=
math
.
sqrt
(
min_size
*
max_size
)
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
# clip the prior's coordidate such that it is within[0, 1]
if
self
.
clip
:
out_boxes
=
np
.
clip
(
out_boxes
,
0.0
,
1.0
)
...
...
@@ -144,5 +137,10 @@ class TestPriorBoxOp(OpTest):
self
.
out_var
=
out_var
.
astype
(
'float32'
)
class
TestPriorBoxOpWithMaxSize
(
TestPriorBoxOp
):
def
set_max_sizes
(
self
):
self
.
max_sizes
=
[]
if
__name__
==
'__main__'
:
unittest
.
main
()
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