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931572e2
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PaddleDetection
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931572e2
编写于
10月 13, 2017
作者:
Q
qijun
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
SelectedRowsAddTensor method
上级
7b183433
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
124 addition
and
35 deletion
+124
-35
paddle/framework/selected_rows.h
paddle/framework/selected_rows.h
+3
-0
paddle/operators/math/math_function.cc
paddle/operators/math/math_function.cc
+53
-15
paddle/operators/math/math_function.h
paddle/operators/math/math_function.h
+9
-0
paddle/operators/math/math_function_test.cc
paddle/operators/math/math_function_test.cc
+59
-20
未找到文件。
paddle/framework/selected_rows.h
浏览文件 @
931572e2
...
...
@@ -45,6 +45,9 @@ class SelectedRows {
}
private:
// Notice: rows can be duplicate. We can have {0, 4, 7, 0, 5, 7, 9} here.
// SelectedRows are simplely concated when adding together. Until a
// SelectedRows add a Tensor, will the duplicate rows be handled.
std
::
vector
<
int64_t
>
rows_
;
std
::
unique_ptr
<
Tensor
>
value_
{
nullptr
};
int64_t
height_
;
...
...
paddle/operators/math/math_function.cc
浏览文件 @
931572e2
...
...
@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/math/math_function.h"
#include "paddle/framework/eigen.h"
#include "paddle/memory/memcpy.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -151,11 +153,17 @@ struct SelectedRowsAdd<platform::CPUPlace, T> {
framework
::
SelectedRows
*
output
)
{
auto
in1_height
=
input1
.
height
();
PADDLE_ENFORCE_EQ
(
in1_height
,
input2
.
height
());
PADDLE_ENFORCE_EQ
(
in1_height
,
output
->
height
()
);
output
->
set_height
(
in1_height
);
auto
&
in1_rows
=
input1
.
rows
();
auto
&
in2_rows
=
input2
.
rows
();
auto
&
out_rows
=
output
->
rows
();
std
::
vector
<
int64_t
>
out_rows
;
out_rows
.
reserve
(
in1_rows
.
size
()
+
in2_rows
.
size
());
// concat rows
out_rows
.
insert
(
out_rows
.
end
(),
in1_rows
.
begin
(),
in1_rows
.
end
());
out_rows
.
insert
(
out_rows
.
end
(),
in2_rows
.
begin
(),
in2_rows
.
end
());
output
->
set_rows
(
out_rows
);
auto
*
out_value
=
output
->
mutable_value
();
auto
&
in1_value
=
input1
.
value
();
...
...
@@ -165,29 +173,59 @@ struct SelectedRowsAdd<platform::CPUPlace, T> {
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
in2_value
.
numel
()
/
in2_rows
.
size
());
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
out_value
->
numel
()
/
out_rows
.
size
());
SetConstant
<
platform
::
CPUPlace
,
T
>
functor
;
functor
(
context
,
out_value
,
0.0
);
auto
*
out_data
=
out_value
->
data
<
T
>
();
auto
*
in1_data
=
in1_value
.
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
in1_rows
.
size
();
i
++
)
{
auto
row
=
detail
::
FindPos
(
out_rows
,
in1_rows
[
i
]);
for
(
size_t
j
=
0
;
j
<
in1_row_numel
;
j
++
)
{
out_data
[
row
*
in1_row_numel
+
j
]
+=
in1_data
[
i
*
in1_row_numel
+
j
];
}
}
memory
::
Copy
(
platform
::
CPUPlace
(),
out_data
,
platform
::
CPUPlace
(),
in1_data
,
in1_value
.
numel
()
*
sizeof
(
T
));
auto
*
in2_data
=
in2_value
.
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
in2_rows
.
size
();
i
++
)
{
auto
row
=
detail
::
FindPos
(
out_rows
,
in2_rows
[
i
]);
for
(
size_t
j
=
0
;
j
<
in1_row_numel
;
j
++
)
{
out_data
[
row
*
in1_row_numel
+
j
]
+=
in2_data
[
i
*
in1_row_numel
+
j
];
memory
::
Copy
(
platform
::
CPUPlace
(),
out_data
+
in1_value
.
numel
(),
platform
::
CPUPlace
(),
in2_data
,
in2_value
.
numel
()
*
sizeof
(
T
));
}
};
template
struct
SelectedRowsAdd
<
platform
::
CPUPlace
,
float
>;
template
<
typename
T
>
struct
SelectedRowsAddTensor
<
platform
::
CPUPlace
,
T
>
{
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
framework
::
Tensor
&
input2
,
framework
::
Tensor
*
output
)
{
auto
in1_height
=
input1
.
height
();
auto
in2_dims
=
input2
.
dims
();
auto
out_dims
=
output
->
dims
();
PADDLE_ENFORCE_EQ
(
in1_height
,
in2_dims
[
0
]);
PADDLE_ENFORCE_EQ
(
in1_height
,
out_dims
[
0
]);
auto
&
in1_value
=
input1
.
value
();
auto
&
in1_rows
=
input1
.
rows
();
int64_t
in1_row_numel
=
in1_value
.
numel
()
/
in1_rows
.
size
();
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
input2
.
numel
()
/
in1_height
);
PADDLE_ENFORCE_EQ
(
in1_row_numel
,
output
->
numel
()
/
in1_height
);
SetConstant
<
platform
::
CPUPlace
,
T
>
functor
;
functor
(
context
,
output
,
0.0
);
auto
*
in1_data
=
in1_value
.
data
<
T
>
();
auto
*
out_data
=
output
->
data
<
T
>
();
for
(
size_t
i
=
0
;
i
<
in1_rows
.
size
();
i
++
)
{
for
(
int64_t
j
=
0
;
j
<
in1_row_numel
;
j
++
)
{
out_data
[
in1_rows
[
i
]
*
in1_row_numel
+
j
]
+=
in1_data
[
i
*
in1_row_numel
+
j
];
}
}
auto
out_eigen
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
output
);
auto
in2_eigen
=
framework
::
EigenVector
<
T
>::
Flatten
(
input2
);
out_eigen
.
device
(
*
context
.
GetEigenDevice
<
platform
::
CPUPlace
>
())
=
out_eigen
+
in2_eigen
;
}
};
template
struct
SelectedRowsAdd
<
platform
::
CPUPlace
,
float
>;
template
struct
SelectedRowsAdd
Tensor
<
platform
::
CPUPlace
,
float
>;
}
// namespace math
}
// namespace operators
...
...
paddle/operators/math/math_function.h
浏览文件 @
931572e2
...
...
@@ -96,6 +96,8 @@ struct SetConstant {
}
};
// SelectedRows + SelectedRows will simplely concat value and rows.
// The real computation happens in dealing with LoDTensor.
template
<
typename
Place
,
typename
T
>
struct
SelectedRowsAdd
{
void
operator
()(
const
platform
::
DeviceContext
&
context
,
...
...
@@ -104,6 +106,13 @@ struct SelectedRowsAdd {
framework
::
SelectedRows
*
output
);
};
template
<
typename
Place
,
typename
T
>
struct
SelectedRowsAddTensor
{
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
SelectedRows
&
input1
,
const
framework
::
Tensor
&
input2
,
framework
::
Tensor
*
output
);
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/math_function_test.cc
浏览文件 @
931572e2
...
...
@@ -286,37 +286,76 @@ TEST(math_function, selected_rows_add) {
auto
*
in1_value
=
selected_rows1
->
mutable_value
();
in1_value
->
mutable_data
<
float
>
(
make_ddim
({
static_cast
<
int64_t
>
(
rows1
.
size
()),
row_numel
}),
cpu_place
);
functor
(
ctx
,
in1_value
,
2
.0
);
functor
(
ctx
,
in1_value
,
1
.0
);
std
::
vector
<
int64_t
>
rows2
{
0
,
5
,
7
,
9
};
std
::
unique_ptr
<
SelectedRows
>
selected_rows2
{
new
SelectedRows
(
rows2
,
height
)};
auto
*
in2_value
=
selected_rows2
->
mutable_value
();
in2_value
->
mutable_data
<
float
>
(
make_ddim
({
static_cast
<
int64_t
>
(
rows2
.
size
()),
row_numel
}),
cpu_place
);
functor
(
ctx
,
in2_value
,
1
.0
);
functor
(
ctx
,
in2_value
,
2
.0
);
std
::
unique_ptr
<
SelectedRows
>
output
{
new
SelectedRows
()};
output
->
set_height
(
height
);
std
::
vector
<
int64_t
>
out_rows
=
{
0
,
4
,
5
,
7
,
9
};
output
->
set_rows
(
out_rows
);
auto
*
out_value
=
output
->
mutable_value
();
out_value
->
mutable_data
<
float
>
(
make_ddim
({
5
,
10
}),
cpu_place
);
// simplely concat two SelectedRows
out_value
->
mutable_data
<
float
>
(
make_ddim
({
7
,
10
}),
cpu_place
);
SelectedRowsAdd
<
CPUPlace
,
float
>
add_functor
;
add_functor
(
ctx
,
*
selected_rows1
,
*
selected_rows2
,
output
.
get
());
auto
*
data
=
output
->
value
().
data
<
float
>
();
// out_rows[0] = 0
EXPECT_EQ
(
data
[
0
*
row_numel
+
0
],
3.0
);
EXPECT_EQ
(
data
[
0
*
row_numel
+
8
],
3.0
);
// out_rows[1] = 4
EXPECT_EQ
(
data
[
1
*
row_numel
+
1
],
2.0
);
// out_rows[2] = 5
EXPECT_EQ
(
data
[
2
*
row_numel
+
6
],
1.0
);
// out_rows[3] = 7
EXPECT_EQ
(
data
[
3
*
row_numel
+
3
],
3.0
);
EXPECT_EQ
(
data
[
3
*
row_numel
+
8
],
3.0
);
// out_rows[4] = 9
EXPECT_EQ
(
data
[
4
*
row_numel
+
4
],
1.0
);
auto
out_height
=
output
->
height
();
EXPECT_EQ
(
out_height
,
height
);
auto
&
out_rows
=
output
->
rows
();
// input1 rows
EXPECT_EQ
(
out_rows
[
0
],
0
);
EXPECT_EQ
(
out_rows
[
1
],
4
);
EXPECT_EQ
(
out_rows
[
2
],
7
);
// input2 rows
EXPECT_EQ
(
out_rows
[
3
],
0
);
EXPECT_EQ
(
out_rows
[
4
],
5
);
EXPECT_EQ
(
out_rows
[
5
],
7
);
EXPECT_EQ
(
out_rows
[
6
],
9
);
auto
*
out_data
=
output
->
value
().
data
<
float
>
();
// input1 value
EXPECT_EQ
(
out_data
[
0
*
row_numel
+
0
],
1.0
);
EXPECT_EQ
(
out_data
[
0
*
row_numel
+
8
],
1.0
);
EXPECT_EQ
(
out_data
[
1
*
row_numel
+
1
],
1.0
);
EXPECT_EQ
(
out_data
[
2
*
row_numel
+
6
],
1.0
);
// input2 value
EXPECT_EQ
(
out_data
[
3
*
row_numel
+
3
],
2.0
);
EXPECT_EQ
(
out_data
[
3
*
row_numel
+
8
],
2.0
);
EXPECT_EQ
(
out_data
[
4
*
row_numel
+
4
],
2.0
);
EXPECT_EQ
(
out_data
[
5
*
row_numel
+
7
],
2.0
);
EXPECT_EQ
(
out_data
[
6
*
row_numel
+
9
],
2.0
);
std
::
unique_ptr
<
Tensor
>
tensor1
{
new
Tensor
()};
tensor1
->
mutable_data
<
float
>
(
make_ddim
({
height
,
row_numel
}),
cpu_place
);
SetConstant
<
CPUPlace
,
float
>
constant_functor
;
constant_functor
(
ctx
,
tensor1
.
get
(),
3.0
);
std
::
unique_ptr
<
Tensor
>
tensor2
{
new
Tensor
()};
tensor2
->
mutable_data
<
float
>
(
make_ddim
({
height
,
row_numel
}),
cpu_place
);
SelectedRowsAddTensor
<
CPUPlace
,
float
>
add_tensor_functor
;
add_tensor_functor
(
ctx
,
*
output
,
*
tensor1
,
tensor2
.
get
());
auto
*
tensor2_data
=
tensor2
->
data
<
float
>
();
// row0: 1.0 + 2.0 + 3.0
EXPECT_EQ
(
tensor2_data
[
0
*
row_numel
+
0
],
6.0
);
// row1: 3.0
EXPECT_EQ
(
tensor2_data
[
1
*
row_numel
+
1
],
3.0
);
// row4 : 1.0 + 3.0
EXPECT_EQ
(
tensor2_data
[
4
*
row_numel
+
6
],
4.0
);
// row5: 2.0 + 3.0
EXPECT_EQ
(
tensor2_data
[
5
*
row_numel
+
7
],
5.0
);
// row6: 3.0
EXPECT_EQ
(
tensor2_data
[
6
*
row_numel
+
1
],
3.0
);
// row7: 1.0 + 2.0 + 3.0
EXPECT_EQ
(
tensor2_data
[
7
*
row_numel
+
3
],
6.0
);
// row9: 2.0 + 3.0
EXPECT_EQ
(
tensor2_data
[
9
*
row_numel
+
6
],
5.0
);
}
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