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体验新版 GitCode,发现更多精彩内容 >>
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8778957c
编写于
9月 13, 2017
作者:
G
gongweibao
提交者:
GitHub
9月 13, 2017
浏览文件
操作
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下载
电子邮件补丁
差异文件
Add element-wise multiplication operator. (#3787)
Add element-wise multiplication operator
上级
0f42e564
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
477 addition
and
0 deletion
+477
-0
paddle/operators/elementwise_mul_op.cc
paddle/operators/elementwise_mul_op.cc
+109
-0
paddle/operators/elementwise_mul_op.cu
paddle/operators/elementwise_mul_op.cu
+25
-0
paddle/operators/elementwise_mul_op.h
paddle/operators/elementwise_mul_op.h
+185
-0
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+1
-0
python/paddle/v2/framework/tests/test_elementwise_mul_op.py
python/paddle/v2/framework/tests/test_elementwise_mul_op.py
+157
-0
未找到文件。
paddle/operators/elementwise_mul_op.cc
0 → 100644
浏览文件 @
8778957c
/* 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/elementwise_mul_op.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
class
ElementWiseMulOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Input(Y) should not be null"
);
auto
x_dim
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
y_dim
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
PADDLE_ENFORCE_GE
(
x_dim
.
size
(),
y_dim
.
size
(),
"Rank of first input must >= rank of second input."
)
ctx
.
Output
<
Tensor
>
(
"Out"
)
->
Resize
(
x_dim
);
}
};
class
ElementWiseMulOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
ElementWiseMulOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The first input of elementwise mul op"
);
AddInput
(
"Y"
,
"The second input of elementwise mul op"
);
AddAttr
<
int
>
(
"axis"
,
R"DOC(
When shape(Y) does not equal shape(X),Y will be broadcasted
to match the shape of X and axis should be dimension index Y in X
)DOC"
)
.
SetDefault
(
-
1
)
.
EqualGreaterThan
(
-
1
);
AddOutput
(
"Out"
,
"The output of elementwise mul op"
);
AddComment
(
R"DOC(
Limited elementwise multiple operator.The equation is: Out = X ⊙ Y.
1. The shape of Y should be same with X or
2. Y's shape is a subset of X.
Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
example:
shape(X) = (2, 3, 4, 5), shape(Y) = (,)
shape(X) = (2, 3, 4, 5), shape(Y) = (5,)
shape(X) = (2, 3, 4, 5), shape(Y) = (4, 5)
shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
shape(X) = (2, 3, 4, 5), shape(Y) = (2), with axis=0
)DOC"
);
}
};
class
ElementWiseMulOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Input(Y) should not be null"
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
y_dims
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
y_dims
.
size
(),
"Rank of first input must >= rank of second input."
)
if
(
x_grad
)
{
x_grad
->
Resize
(
x_dims
);
}
if
(
y_grad
)
{
y_grad
->
Resize
(
y_dims
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
elementwise_mul
,
ops
::
ElementWiseMulOp
,
ops
::
ElementWiseMulOpMaker
,
elementwise_mul_grad
,
ops
::
ElementWiseMulOpGrad
);
REGISTER_OP_CPU_KERNEL
(
elementwise_mul
,
ops
::
ElementWiseMulKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
elementwise_mul_grad
,
ops
::
ElementWiseMulGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/elementwise_mul_op.cu
0 → 100644
浏览文件 @
8778957c
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/elementwise_mul_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
elementwise_mul
,
ops
::
ElementWiseMulKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
elementwise_mul_grad
,
ops
::
ElementWiseMulGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/elementwise_mul_op.h
0 → 100644
浏览文件 @
8778957c
/* 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 <iostream>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
/*
* Out = X ⊙ Y
* 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
* pre=2, n=3*4, post=5
* 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
* pre=2*3, n=4*5, post=1
*/
inline
void
get_mid_dims
(
const
framework
::
DDim
&
x_dims
,
const
framework
::
DDim
&
y_dims
,
const
int
axis
,
int
&
pre
,
int
&
n
,
int
&
post
)
{
pre
=
1
;
n
=
1
;
post
=
1
;
for
(
int
i
=
0
;
i
<
axis
;
++
i
)
{
pre
*=
x_dims
[
i
];
}
for
(
int
i
=
0
;
i
<
y_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
x_dims
[
i
+
axis
],
y_dims
[
i
],
"Broadcast dimension mismatch."
);
n
*=
y_dims
[
i
];
}
for
(
int
i
=
axis
+
y_dims
.
size
();
i
<
x_dims
.
size
();
++
i
)
{
post
*=
x_dims
[
i
];
}
}
template
<
typename
Place
,
typename
T
>
class
ElementWiseMulKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
using
Tensor
=
framework
::
Tensor
;
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
z
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
z
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
x_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x
);
auto
y_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
y
);
auto
z_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
z
);
auto
x_dims
=
x
->
dims
();
auto
y_dims
=
y
->
dims
();
PADDLE_ENFORCE_GE
(
x_dims
.
size
(),
y_dims
.
size
(),
"Rank of first input must >= rank of second input."
)
if
(
x_dims
==
y_dims
||
product
(
y_dims
)
==
1
)
{
z_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
x_e
*
y_e
;
return
;
}
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
axis
=
(
axis
==
-
1
?
x_dims
.
size
()
-
y_dims
.
size
()
:
axis
);
PADDLE_ENFORCE
(
axis
>=
0
&&
axis
<
x_dims
.
size
(),
"Axis should be in range [0, x_dims)"
);
int
pre
,
n
,
post
;
get_mid_dims
(
x_dims
,
y_dims
,
axis
,
pre
,
n
,
post
);
if
(
post
==
1
)
{
auto
y_bcast
=
y_e
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
1
,
n
))
.
broadcast
(
Eigen
::
DSizes
<
int
,
2
>
(
pre
,
1
))
.
reshape
(
Eigen
::
DSizes
<
int
,
1
>
(
x_e
.
size
()));
z_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
x_e
*
y_bcast
;
return
;
}
else
{
auto
y_bcast
=
y_e
.
reshape
(
Eigen
::
DSizes
<
int
,
3
>
(
1
,
n
,
1
))
.
broadcast
(
Eigen
::
DSizes
<
int
,
3
>
(
pre
,
1
,
post
))
.
reshape
(
Eigen
::
DSizes
<
int
,
1
>
(
x_e
.
size
()));
z_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
x_e
*
y_bcast
;
return
;
}
}
};
template
<
typename
Place
,
typename
T
>
class
ElementWiseMulGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
using
Tensor
=
framework
::
Tensor
;
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
*
dout
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
x_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
x
);
auto
y_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
y
);
auto
dout_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dout
);
auto
x_dims
=
x
->
dims
();
auto
y_dims
=
y
->
dims
();
auto
*
dx
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
dy
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
if
(
dx
)
{
dx
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
if
(
dy
)
{
dy
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
}
if
(
x_dims
==
y_dims
||
product
(
y_dims
)
==
1
)
{
if
(
dx
)
{
auto
dx_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dx
);
dx_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
dout_e
*
y_e
;
}
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
x_e
*
dout_e
;
}
return
;
}
int
axis
=
ctx
.
Attr
<
int
>
(
"axis"
);
axis
=
(
axis
==
-
1
?
x_dims
.
size
()
-
y_dims
.
size
()
:
axis
);
int
pre
,
n
,
post
;
get_mid_dims
(
x_dims
,
y_dims
,
axis
,
pre
,
n
,
post
);
// TODO(gongweibao): wrap reshape to a function.
if
(
post
==
1
)
{
auto
y_e_bcast
=
y_e
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
1
,
n
))
.
broadcast
(
Eigen
::
DSizes
<
int
,
2
>
(
pre
,
1
))
.
reshape
(
Eigen
::
DSizes
<
int
,
1
>
(
x_e
.
size
()));
if
(
dx
)
{
auto
dx_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dx
);
dx_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
dout_e
*
y_e_bcast
;
}
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
(
x_e
*
dout_e
)
.
reshape
(
Eigen
::
DSizes
<
int
,
2
>
(
pre
,
n
))
.
sum
(
Eigen
::
array
<
int
,
1
>
{{
0
}});
}
return
;
}
else
{
auto
y_e_bcast
=
y_e
.
reshape
(
Eigen
::
DSizes
<
int
,
3
>
(
1
,
n
,
1
))
.
broadcast
(
Eigen
::
DSizes
<
int
,
3
>
(
pre
,
1
,
post
))
.
reshape
(
Eigen
::
DSizes
<
int
,
1
>
(
x_e
.
size
()));
if
(
dx
)
{
auto
dx_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dx
);
dx_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
dout_e
*
y_e_bcast
;
}
if
(
dy
)
{
auto
dy_e
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
dy
);
dy_e
.
device
(
ctx
.
GetEigenDevice
<
Place
>
())
=
(
x_e
*
dout_e
)
.
reshape
(
Eigen
::
DSizes
<
int
,
3
>
(
pre
,
n
,
post
))
.
sum
(
Eigen
::
array
<
int
,
2
>
{{
0
,
2
}});
}
return
;
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/pybind/pybind.cc
浏览文件 @
8778957c
...
...
@@ -35,6 +35,7 @@ USE_OP(add);
USE_OP
(
onehot_cross_entropy
);
USE_OP
(
sgd
);
USE_OP
(
mul
);
USE_OP
(
elementwise_mul
);
USE_OP
(
mean
);
USE_OP
(
sigmoid
);
USE_OP
(
softmax
);
...
...
python/paddle/v2/framework/tests/test_elementwise_mul_op.py
0 → 100644
浏览文件 @
8778957c
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestElementwiseMulOp_Matrix
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_mul"
""" Warning
CPU gradient check error!
'X': np.random.random((32,84)).astype("float32"),
'Y': np.random.random((32,84)).astype("float32")
"""
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Out'
:
np
.
multiply
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.1
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMulOp_Vector
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_mul"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
)).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
((
32
,
)).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Out'
:
np
.
multiply
(
self
.
inputs
[
'X'
],
self
.
inputs
[
'Y'
])}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.1
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMulOp_broadcast_0
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_mul"
self
.
inputs
=
{
'X'
:
np
.
random
.
rand
(
2
,
3
,
4
).
astype
(
np
.
float32
),
'Y'
:
np
.
random
.
rand
(
2
).
astype
(
np
.
float32
)
}
self
.
attrs
=
{
'axis'
:
0
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
inputs
[
'Y'
].
reshape
(
2
,
1
,
1
)
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.1
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMulOp_broadcast_1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_mul"
self
.
inputs
=
{
'X'
:
np
.
random
.
rand
(
2
,
3
,
4
).
astype
(
np
.
float32
),
'Y'
:
np
.
random
.
rand
(
3
).
astype
(
np
.
float32
)
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
1
)
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.1
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMulOp_broadcast_2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_mul"
self
.
inputs
=
{
'X'
:
np
.
random
.
rand
(
2
,
3
,
4
).
astype
(
np
.
float32
),
'Y'
:
np
.
random
.
rand
(
4
).
astype
(
np
.
float32
)
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
inputs
[
'Y'
].
reshape
(
1
,
1
,
4
)
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'X'
,
'Y'
],
'Out'
,
max_relative_error
=
0.1
)
def
test_check_grad_ingore_x
(
self
):
self
.
check_grad
(
[
'Y'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
"X"
))
def
test_check_grad_ingore_y
(
self
):
self
.
check_grad
(
[
'X'
],
'Out'
,
max_relative_error
=
0.1
,
no_grad_set
=
set
(
'Y'
))
class
TestElementwiseMulOp_broadcast_3
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"elementwise_mul"
self
.
inputs
=
{
'X'
:
np
.
random
.
rand
(
2
,
3
,
4
,
5
).
astype
(
np
.
float32
),
'Y'
:
np
.
random
.
rand
(
3
,
4
).
astype
(
np
.
float32
)
}
self
.
attrs
=
{
'axis'
:
1
}
self
.
outputs
=
{
'Out'
:
self
.
inputs
[
'X'
]
*
self
.
inputs
[
'Y'
].
reshape
(
1
,
3
,
4
,
1
)
}
if
__name__
==
'__main__'
:
unittest
.
main
()
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