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体验新版 GitCode,发现更多精彩内容 >>
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56b1b701
编写于
9月 18, 2017
作者:
Y
Yancey
提交者:
GitHub
9月 18, 2017
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电子邮件补丁
差异文件
Split operator with CPU kernel (#4046)
Split Op CPU Kernel
上级
ec9a55ae
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
211 addition
and
2 deletion
+211
-2
paddle/operators/split_op.cc
paddle/operators/split_op.cc
+118
-0
paddle/operators/split_op.h
paddle/operators/split_op.h
+62
-0
python/paddle/v2/framework/tests/op_test.py
python/paddle/v2/framework/tests/op_test.py
+5
-2
python/paddle/v2/framework/tests/test_split_op.py
python/paddle/v2/framework/tests/test_split_op.py
+26
-0
未找到文件。
paddle/operators/split_op.cc
0 → 100644
浏览文件 @
56b1b701
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/split_op.h"
#include "paddle/operators/net_op.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
SplitOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
// infershape
auto
*
in
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
outs
=
ctx
.
MultiOutput
<
framework
::
LoDTensor
>
(
"Out"
);
size_t
axis
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
size_t
num
=
static_cast
<
size_t
>
(
ctx
.
Attr
<
int
>
(
"num"
));
std
::
vector
<
int
>
sections
=
static_cast
<
std
::
vector
<
int
>>
(
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"sections"
));
const
size_t
n
=
outs
.
size
();
if
(
num
>
0
)
{
int64_t
in_axis_dim
=
in
->
dims
()[
axis
];
PADDLE_ENFORCE_EQ
(
in_axis_dim
%
num
,
0
,
"tensor split does not result"
" in an equal division"
);
size_t
out_axis_dim
=
in_axis_dim
/
num
;
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
auto
dim
=
in
->
dims
();
dim
[
axis
]
=
out_axis_dim
;
outs
[
i
]
->
Resize
(
dim
);
}
}
else
if
(
sections
.
size
()
>
0
)
{
PADDLE_ENFORCE_EQ
(
sections
.
size
(),
n
,
"tensor split sections size"
"should be equal to output size."
);
for
(
size_t
i
=
0
;
i
<
n
;
++
i
)
{
auto
dim
=
in
->
dims
();
dim
[
axis
]
=
sections
[
i
];
outs
[
i
]
->
Resize
(
dim
);
}
}
else
{
PADDLE_ENFORCE_NOT_NULL
(
nullptr
,
"split operator should"
,
" specify indices or sections."
);
}
}
};
class
SplitOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SplitOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"the input tensor of split operator."
);
AddOutput
(
"Out"
,
"the output tensors of split operator."
).
AsDuplicable
();
AddComment
(
R"DOC(
Split the input tensor into multiple sub-tensors.
Example:
Input = [[1,2],
[3,4],
[5,6]]
sections = [2,1]
axis = 0
Output[0] = [[1,2],
[3,4]]
Output[1] = [[5,6]]
)DOC"
);
AddAttr
<
std
::
vector
<
int
>>
(
"sections"
,
"the length for each"
"output along with the specify axis."
)
.
SetDefault
(
std
::
vector
<
int
>
{});
AddAttr
<
int
>
(
"num"
,
"number of the sub-tensors, it must evenly divide "
"Input.dims()[axis]"
)
.
SetDefault
(
0
);
AddAttr
<
int
>
(
"axis"
,
"The axis which the input will be splited on."
)
.
SetDefault
(
0
);
}
};
class
SplitOpGrad
:
public
NetOp
{
public:
SplitOpGrad
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
NetOp
(
type
,
inputs
,
outputs
,
attrs
)
{
auto
out_grad
=
Inputs
(
framework
::
GradVarName
(
"Out"
));
auto
x_grad
=
Output
(
framework
::
GradVarName
(
"X"
));
AppendOp
(
framework
::
OpRegistry
::
CreateOp
(
"concat"
,
{{
"X"
,
out_grad
}},
{{
"Out"
,
{
x_grad
}}},
attrs
));
CompleteAddOp
(
false
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
USE_CPU_ONLY_OP
(
concat
);
REGISTER_OP
(
split
,
ops
::
SplitOp
,
ops
::
SplitOpMaker
,
split_grad
,
ops
::
SplitOpGrad
);
REGISTER_OP_CPU_KERNEL
(
split
,
ops
::
SplitKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/split_op.h
0 → 100644
浏览文件 @
56b1b701
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include <vector>
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
Place
,
typename
T
>
class
SplitKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
in
=
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
outs
=
ctx
.
MultiOutput
<
framework
::
Tensor
>
(
"Out"
);
int64_t
axis
=
static_cast
<
int64_t
>
(
ctx
.
Attr
<
int
>
(
"axis"
));
size_t
before
=
1
,
after
=
1
;
const
size_t
n
=
outs
.
size
();
size_t
input_axis_dim
=
in
->
dims
()[
axis
];
for
(
int64_t
i
=
0
;
i
<
in
->
dims
().
size
();
++
i
)
{
if
(
i
==
axis
)
{
continue
;
}
if
(
i
<
axis
)
{
before
*=
in
->
dims
()[
i
];
}
else
{
after
*=
in
->
dims
()[
i
];
}
}
size_t
input_offset
=
0
;
for
(
size_t
i
=
0
;
i
<
n
;
i
++
)
{
auto
&
out
=
outs
[
i
];
size_t
axis_dim
=
out
->
dims
()[
axis
];
for
(
size_t
j
=
0
;
j
<
before
;
j
++
)
{
size_t
len
=
axis_dim
*
after
*
sizeof
(
T
);
T
*
dest
=
out
->
mutable_data
<
T
>
(
platform
::
CPUPlace
())
+
axis_dim
*
after
*
j
;
const
T
*
src
=
in
->
data
<
T
>
()
+
input_offset
+
input_axis_dim
*
after
*
j
;
memcpy
(
dest
,
src
,
len
);
}
input_offset
+=
axis_dim
*
after
;
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/op_test.py
浏览文件 @
56b1b701
...
@@ -194,10 +194,13 @@ class OpTest(unittest.TestCase):
...
@@ -194,10 +194,13 @@ class OpTest(unittest.TestCase):
for
out_name
,
out_dup
in
Operator
.
get_op_outputs
(
self
.
op
.
type
()):
for
out_name
,
out_dup
in
Operator
.
get_op_outputs
(
self
.
op
.
type
()):
if
out_dup
:
if
out_dup
:
sub_out
=
self
.
outputs
[
out_name
]
sub_out
=
self
.
outputs
[
out_name
]
for
sub_out_name
,
sub_out_array
in
sub_out
:
if
not
isinstance
(
sub_out
,
list
):
raise
AssertionError
(
"sub_out type %s is not list"
,
type
(
sub_out
))
for
sub_out_name
,
expect
in
sub_out
:
actual
=
np
.
array
(
actual
=
np
.
array
(
self
.
scope
.
find_var
(
sub_out_name
).
get_tensor
())
self
.
scope
.
find_var
(
sub_out_name
).
get_tensor
())
expect
=
sub_out_array
self
.
assertTrue
(
self
.
assertTrue
(
np
.
allclose
(
np
.
allclose
(
actual
,
expect
,
atol
=
1e-05
),
actual
,
expect
,
atol
=
1e-05
),
...
...
python/paddle/v2/framework/tests/test_split_op.py
0 → 100644
浏览文件 @
56b1b701
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestSplitOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"split"
axis
=
0
num
=
2
x
=
np
.
random
.
random
((
4
,
2
)).
astype
(
'float32'
)
out
=
np
.
split
(
x
,
num
,
axis
)
self
.
inputs
=
{
'X'
:
x
}
self
.
attrs
=
{
'axis'
:
axis
,
'num'
:
num
}
self
.
outputs
=
{
'Out'
:
[(
'out%d'
%
i
,
out
[
i
])
\
for
i
in
xrange
(
len
(
out
))]}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
([
'X'
],
[
'out0'
,
'out1'
])
if
__name__
==
'__main__'
:
unittest
.
main
()
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