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体验新版 GitCode,发现更多精彩内容 >>
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e77af4ae
编写于
1月 10, 2018
作者:
L
Li Peng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
MVN layer ocl implementation
Signed-off-by:
N
Li Peng
<
peng.li@intel.com
>
上级
7bc01760
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
201 addition
and
0 deletion
+201
-0
modules/dnn/src/layers/mvn_layer.cpp
modules/dnn/src/layers/mvn_layer.cpp
+84
-0
modules/dnn/src/opencl/mvn.cl
modules/dnn/src/opencl/mvn.cl
+112
-0
modules/dnn/test/test_layers.cpp
modules/dnn/test/test_layers.cpp
+5
-0
未找到文件。
modules/dnn/src/layers/mvn_layer.cpp
浏览文件 @
e77af4ae
...
...
@@ -43,6 +43,8 @@
#include "../precomp.hpp"
#include "layers_common.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include "math_functions.hpp"
#include "opencl_kernels_dnn.hpp"
namespace
cv
{
...
...
@@ -60,11 +62,93 @@ public:
eps
=
params
.
get
<
double
>
(
"eps"
,
1e-9
);
}
#ifdef HAVE_OPENCL
bool
forward_ocl
(
InputArrayOfArrays
inputs_
,
OutputArrayOfArrays
outputs_
,
OutputArrayOfArrays
internals_
)
{
std
::
vector
<
UMat
>
inputs
;
std
::
vector
<
UMat
>
outputs
;
inputs_
.
getUMatVector
(
inputs
);
outputs_
.
getUMatVector
(
outputs
);
for
(
size_t
inpIdx
=
0
;
inpIdx
<
inputs
.
size
();
inpIdx
++
)
{
UMat
&
inpBlob
=
inputs
[
inpIdx
];
UMat
&
outBlob
=
outputs
[
inpIdx
];
int
splitDim
=
(
acrossChannels
)
?
1
:
2
;
int
i
,
newRows
=
1
;
for
(
i
=
0
;
i
<
splitDim
;
i
++
)
newRows
*=
inpBlob
.
size
[
i
];
MatShape
s
=
shape
(
newRows
,
inpBlob
.
total
()
/
newRows
);
UMat
&
inpMat
=
inpBlob
;
UMat
&
outMat
=
outBlob
;
UMat
oneMat
=
UMat
::
ones
(
s
[
1
],
1
,
CV_32F
);
UMat
meanMat
=
UMat
(
s
[
0
],
1
,
CV_32F
);
UMat
devMat
=
UMat
(
s
[
0
],
1
,
CV_32F
);
UMat
tmpMat
=
UMat
(
s
[
0
],
s
[
1
],
CV_32F
);
float
alpha
=
1.0
f
/
s
[
1
];
bool
ret
=
ocl4dnn
::
ocl4dnnGEMV
<
float
>
(
ocl4dnn
::
CblasNoTrans
,
s
[
0
],
s
[
1
],
alpha
,
inpMat
,
0
,
oneMat
,
0
,
0.0
f
,
meanMat
,
0
);
if
(
!
ret
)
return
false
;
int
number
=
(
s
[
1
]
%
8
==
0
)
?
8
:
((
s
[
1
]
%
4
==
0
)
?
4
:
1
);
String
buildopt
=
format
(
"-DNUM=%d "
,
number
);
String
kname
=
format
(
"calc_mean%d"
,
number
);
ocl
::
Kernel
kernel
(
kname
.
c_str
(),
ocl
::
dnn
::
mvn_oclsrc
,
buildopt
);
if
(
kernel
.
empty
())
return
false
;
size_t
global
[]
=
{
(
size_t
)
s
[
0
],
(
size_t
)(
s
[
1
]
/
number
)
};
kernel
.
set
(
0
,
ocl
::
KernelArg
::
PtrReadOnly
(
inpMat
));
kernel
.
set
(
1
,
(
int
)
s
[
0
]);
kernel
.
set
(
2
,
(
int
)
s
[
1
]);
kernel
.
set
(
3
,
ocl
::
KernelArg
::
PtrReadOnly
(
meanMat
));
kernel
.
set
(
4
,
ocl
::
KernelArg
::
PtrWriteOnly
(
tmpMat
));
ret
=
kernel
.
run
(
2
,
global
,
NULL
,
false
);
if
(
!
ret
)
return
false
;
if
(
normVariance
)
{
ret
=
ocl4dnn
::
ocl4dnnGEMV
<
float
>
(
ocl4dnn
::
CblasNoTrans
,
s
[
0
],
s
[
1
],
alpha
,
tmpMat
,
0
,
oneMat
,
0
,
0.0
f
,
devMat
,
0
);
if
(
!
ret
)
return
false
;
}
kname
=
format
(
"mvn%d"
,
number
);
if
(
normVariance
)
buildopt
+=
"-DNORM_VARIANCE"
;
ocl
::
Kernel
kernel1
(
kname
.
c_str
(),
ocl
::
dnn
::
mvn_oclsrc
,
buildopt
);
if
(
kernel1
.
empty
())
return
false
;
kernel1
.
set
(
0
,
ocl
::
KernelArg
::
PtrReadOnly
(
inpMat
));
kernel1
.
set
(
1
,
(
int
)
s
[
0
]);
kernel1
.
set
(
2
,
(
int
)
s
[
1
]);
kernel1
.
set
(
3
,
(
float
)
eps
);
kernel1
.
set
(
4
,
ocl
::
KernelArg
::
PtrReadOnly
(
meanMat
));
kernel1
.
set
(
5
,
ocl
::
KernelArg
::
PtrReadOnly
(
devMat
));
kernel1
.
set
(
6
,
ocl
::
KernelArg
::
PtrWriteOnly
(
outMat
));
ret
=
kernel1
.
run
(
2
,
global
,
NULL
,
false
);
if
(
!
ret
)
return
false
;
}
return
true
;
}
#endif
void
forward
(
InputArrayOfArrays
inputs_arr
,
OutputArrayOfArrays
outputs_arr
,
OutputArrayOfArrays
internals_arr
)
{
CV_TRACE_FUNCTION
();
CV_TRACE_ARG_VALUE
(
name
,
"name"
,
name
.
c_str
());
CV_OCL_RUN
((
preferableTarget
==
DNN_TARGET_OPENCL
)
&&
OCL_PERFORMANCE_CHECK
(
ocl
::
Device
::
getDefault
().
isIntel
()),
forward_ocl
(
inputs_arr
,
outputs_arr
,
internals_arr
))
Layer
::
forward_fallback
(
inputs_arr
,
outputs_arr
,
internals_arr
);
}
...
...
modules/dnn/src/opencl/mvn.cl
0 → 100644
浏览文件 @
e77af4ae
/*M///////////////////////////////////////////////////////////////////////////////////////
//
//
IMPORTANT:
READ
BEFORE
DOWNLOADING,
COPYING,
INSTALLING
OR
USING.
//
//
By
downloading,
copying,
installing
or
using
the
software
you
agree
to
this
license.
//
If
you
do
not
agree
to
this
license,
do
not
download,
install,
//
copy
or
use
the
software.
//
//
//
License
Agreement
//
For
Open
Source
Computer
Vision
Library
//
//
Copyright
(
C
)
2017
,
Intel
Corporation,
all
rights
reserved.
//
Copyright
(
c
)
2016-2017
Fabian
David
Tschopp,
all
rights
reserved.
//
Third
party
copyrights
are
property
of
their
respective
owners.
//
//
Redistribution
and
use
in
source
and
binary
forms,
with
or
without
modification,
//
are
permitted
provided
that
the
following
conditions
are
met:
//
//
*
Redistribution
's
of
source
code
must
retain
the
above
copyright
notice,
//
this
list
of
conditions
and
the
following
disclaimer.
//
//
*
Redistribution
's
in
binary
form
must
reproduce
the
above
copyright
notice,
//
this
list
of
conditions
and
the
following
disclaimer
in
the
documentation
//
and/or
other
materials
provided
with
the
distribution.
//
//
*
The
name
of
the
copyright
holders
may
not
be
used
to
endorse
or
promote
products
//
derived
from
this
software
without
specific
prior
written
permission.
//
//
This
software
is
provided
by
the
copyright
holders
and
contributors
"as is"
and
//
any
express
or
implied
warranties,
including,
but
not
limited
to,
the
implied
//
warranties
of
merchantability
and
fitness
for
a
particular
purpose
are
disclaimed.
//
In
no
event
shall
the
Intel
Corporation
or
contributors
be
liable
for
any
direct,
//
indirect,
incidental,
special,
exemplary,
or
consequential
damages
//
(
including,
but
not
limited
to,
procurement
of
substitute
goods
or
services
;
//
loss
of
use,
data,
or
profits
; or business interruption) however caused
//
and
on
any
theory
of
liability,
whether
in
contract,
strict
liability,
//
or
tort
(
including
negligence
or
otherwise
)
arising
in
any
way
out
of
//
the
use
of
this
software,
even
if
advised
of
the
possibility
of
such
damage.
//
//M*/
#
define
Dtype
float
#
define
Dtype4
float4
#
define
Dtype8
float8
#
if
NUM
==
8
#
define
load
(
src,
index
)
vload8
(
0
,
src
+
index
)
#
define
store
(
vec,
dst,
index
)
vstore8
(
vec,
0
,
dst
+
index
)
#
define
vec_type
Dtype8
#
define
CALC_MEAN
calc_mean8
#
define
MVN
mvn8
#
elif
NUM
==
4
#
define
load
(
src,
index
)
vload4
(
0
,
src
+
index
)
#
define
store
(
vec,
dst,
index
)
vstore4
(
vec,
0
,
dst
+
index
)
#
define
vec_type
Dtype4
#
define
CALC_MEAN
calc_mean4
#
define
MVN
mvn4
#
elif
NUM
==
1
#
define
load
(
src,
index
)
src[index]
#
define
store
(
vec,
dst,
index
)
dst[index]
=
vec
#
define
vec_type
Dtype
#
define
CALC_MEAN
calc_mean1
#
define
MVN
mvn1
#
endif
__kernel
void
CALC_MEAN
(
__global
const
Dtype*
src,
const
int
rows,
const
int
cols,
__global
Dtype*
mean,
__global
Dtype*
dst
)
{
int
x
=
get_global_id
(
0
)
;
int
y
=
get_global_id
(
1
)
*
NUM
;
int
index
=
x
*
cols
+
y
;
if
(
x
>=
rows
|
| y >= cols)
return;
Dtype mean_val = mean[x];
vec_type src_vec = load(src, index);
vec_type dst_vec = pow(src_vec - (vec_type)mean_val, 2);
store(dst_vec, dst, index);
}
__kernel void MVN(__global const Dtype* src,
const int rows,
const int cols,
const Dtype eps,
__global const Dtype* mean,
__global const Dtype* dev,
__global Dtype* dst)
{
int x = get_global_id(0);
int y = get_global_id(1) * NUM;
int index = x * cols + y;
if (x >= rows |
|
y
>=
cols
)
return
;
Dtype
mean_val
=
mean[x]
;
Dtype
dev_val
=
sqrt
(
dev[x]
)
;
Dtype
alpha
;
#
ifdef
NORM_VARIANCE
alpha
=
1
/
(
eps
+
dev_val
)
;
#
else
alpha
=
1
;
#
endif
vec_type
src_vec
=
load
(
src,
index
)
-
(
vec_type
)
mean_val
;
vec_type
dst_vec
=
src_vec
*
alpha
;
store
(
dst_vec,
dst,
index
)
;
}
modules/dnn/test/test_layers.cpp
浏览文件 @
e77af4ae
...
...
@@ -202,6 +202,11 @@ TEST(Layer_Test_MVN, Accuracy)
testLayerUsingCaffeModels
(
"layer_mvn"
);
}
OCL_TEST
(
Layer_Test_MVN
,
Accuracy
)
{
testLayerUsingCaffeModels
(
"layer_mvn"
,
DNN_TARGET_OPENCL
);
}
void
testReshape
(
const
MatShape
&
inputShape
,
const
MatShape
&
targetShape
,
int
axis
=
0
,
int
num_axes
=
-
1
,
MatShape
mask
=
MatShape
())
...
...
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