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体验新版 GitCode,发现更多精彩内容 >>
提交
a072ab9e
编写于
9月 07, 2017
作者:
Y
Yang yaming
提交者:
GitHub
9月 07, 2017
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差异文件
Merge pull request #3768 from pkuyym/fix-3736
Add squared_l2_distance_op
上级
fee6e7fb
57f9723d
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
359 addition
and
2 deletion
+359
-2
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+1
-1
paddle/operators/squared_l2_distance_op.cc
paddle/operators/squared_l2_distance_op.cc
+118
-0
paddle/operators/squared_l2_distance_op.cu
paddle/operators/squared_l2_distance_op.cu
+25
-0
paddle/operators/squared_l2_distance_op.h
paddle/operators/squared_l2_distance_op.h
+123
-0
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+1
-0
python/paddle/v2/framework/tests/CMakeLists.txt
python/paddle/v2/framework/tests/CMakeLists.txt
+1
-0
python/paddle/v2/framework/tests/op_test_util.py
python/paddle/v2/framework/tests/op_test_util.py
+1
-1
python/paddle/v2/framework/tests/test_squared_l2_distance_op.py
.../paddle/v2/framework/tests/test_squared_l2_distance_op.py
+89
-0
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
a072ab9e
...
...
@@ -59,7 +59,7 @@ set(DEPS_OPS
op_library
(
identity_op DEPS scale_op
)
op_library
(
minus_op DEPS scale_op
)
op_library
(
mul_op DEPS math_function
)
op_library
(
recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
op_library
(
recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor operator net_op
)
op_library
(
scale_op DEPS net_op
)
...
...
paddle/operators/squared_l2_distance_op.cc
0 → 100644
浏览文件 @
a072ab9e
/* 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/squared_l2_distance_op.h"
namespace
paddle
{
namespace
operators
{
class
SquaredL2DistanceOp
:
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 of SquaredL2DistanceOp "
"must be initialized."
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Target of SquaredL2DistanceOp "
"must be initialized."
);
auto
*
x
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
x_dims
=
x
->
dims
();
auto
*
y
=
ctx
.
Input
<
Tensor
>
(
"Y"
);
auto
y_dims
=
y
->
dims
();
PADDLE_ENFORCE_EQ
(
framework
::
arity
(
x_dims
),
framework
::
arity
(
y_dims
),
"Tensor rank of both SquaredL2DistanceOp's "
"inputs must be same."
);
int
rank
=
framework
::
arity
(
x_dims
);
PADDLE_ENFORCE_GE
(
rank
,
2
,
"Tensor rank should be at least equal to 2."
);
PADDLE_ENFORCE_EQ
(
framework
::
product
(
x_dims
)
/
x_dims
[
0
],
framework
::
product
(
y_dims
)
/
y_dims
[
0
],
"Product of dimensions expcet the first dimension of "
"input and target must be equal."
);
PADDLE_ENFORCE
(
y_dims
[
0
]
==
1
||
y_dims
[
0
]
==
x_dims
[
0
],
"First dimension of target must be equal to input "
"or to 1."
);
ctx
.
Output
<
Tensor
>
(
"sub_result"
)
->
Resize
({
static_cast
<
int
>
(
x_dims
[
0
]),
static_cast
<
int
>
(
framework
::
product
(
x_dims
)
/
x_dims
[
0
])});
ctx
.
Output
<
Tensor
>
(
"Out"
)
->
Resize
({
x_dims
[
0
],
1
});
}
};
class
SquaredL2DistanceOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SquaredL2DistanceOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of SquaredL2DistanceOp."
);
AddInput
(
"Y"
,
"Target of SquaredL2DistanceOp."
);
AddOutput
(
"sub_result"
,
"Buffering substraction result which "
"will be reused in backward."
)
.
AsIntermediate
();
AddOutput
(
"Out"
,
"Squared l2 distance between input and target."
);
AddComment
(
R"DOC(
SquaredL2DistanceOp will cacluate the squared L2 distance for
input and target. Number of distance value equals to the
first dimension of input. First dimension of target could be equal to
input or to 1. If the first dimension of target is 1, SquaredL2DistanceOp
will broadcast target's first dimension to input's first dimension.
You can decide whether calculate the gradient of input and target.
)DOC"
);
}
};
class
SquaredL2DistanceGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Out"
)),
"Gradient of Out should not be null"
);
auto
out_dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
auto
x_dims
=
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
();
auto
y_dims
=
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
();
PADDLE_ENFORCE_EQ
(
out_dims
[
0
],
x_dims
[
0
],
"First dimension of output gradient and "
"input value must be equal."
);
PADDLE_ENFORCE_EQ
(
out_dims
[
1
],
1
,
"Second dimension of output gradient "
"must be 1."
);
auto
*
x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
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
(
squared_l2_distance
,
ops
::
SquaredL2DistanceOp
,
ops
::
SquaredL2DistanceOpMaker
,
squared_l2_distance_grad
,
ops
::
SquaredL2DistanceGradOp
);
REGISTER_OP_CPU_KERNEL
(
squared_l2_distance
,
ops
::
SquaredL2DistanceKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
squared_l2_distance_grad
,
ops
::
SquaredL2DistanceGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/squared_l2_distance_op.cu
0 → 100644
浏览文件 @
a072ab9e
/* 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/squared_l2_distance_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
squared_l2_distance
,
ops
::
SquaredL2DistanceKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
squared_l2_distance_grad
,
ops
::
SquaredL2DistanceGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/squared_l2_distance_op.h
0 → 100644
浏览文件 @
a072ab9e
/* 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 "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
Place
,
typename
T
>
class
SquaredL2DistanceKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in0
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
in1
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
*
out0
=
context
.
Output
<
Tensor
>
(
"sub_result"
);
auto
*
out1
=
context
.
Output
<
Tensor
>
(
"Out"
);
auto
in0_dims
=
in0
->
dims
();
auto
in1_dims
=
in1
->
dims
();
int
cols
=
framework
::
product
(
in0_dims
)
/
in0_dims
[
0
];
// reduce dimensions except the first
auto
x
=
EigenMatrix
<
T
>::
From
(
*
in0
,
framework
::
make_ddim
({
in0_dims
[
0
],
cols
}));
auto
y
=
EigenMatrix
<
T
>::
From
(
*
in1
,
framework
::
make_ddim
({
in1_dims
[
0
],
cols
}));
out0
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out1
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
sub_result
=
EigenMatrix
<
T
>::
From
(
*
out0
);
auto
z
=
EigenVector
<
T
>::
Flatten
(
*
out1
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
x_dims
=
x
.
dimensions
();
auto
y_dims
=
y
.
dimensions
();
// buffer the substraction result
if
(
y_dims
[
0
]
==
1
&&
x_dims
[
0
]
>
y_dims
[
0
])
{
sub_result
.
device
(
place
)
=
x
-
y
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
static_cast
<
int
>
(
x_dims
[
0
]),
1
}}));
}
else
{
sub_result
.
device
(
place
)
=
x
-
y
;
}
auto
sub_res_pow2
=
sub_result
*
sub_result
;
z
.
device
(
place
)
=
sub_res_pow2
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
1
}}));
}
};
template
<
typename
Place
,
typename
T
>
class
SquaredL2DistanceGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in0
=
context
.
Input
<
Tensor
>
(
"sub_result"
);
auto
*
in1
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
x_g
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
y_g
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
auto
sub_result
=
EigenMatrix
<
T
>::
From
(
*
in0
);
auto
out_grad
=
EigenMatrix
<
T
>::
From
(
*
in1
);
auto
x_dims
=
x_g
->
dims
();
auto
y_dims
=
y_g
->
dims
();
int
cols
=
framework
::
product
(
x_dims
)
/
x_dims
[
0
];
// calculate gradient
auto
grad_mat
=
2
*
(
out_grad
.
broadcast
(
Eigen
::
array
<
int
,
2
>
({{
1
,
cols
}})))
*
sub_result
;
// propagate back to input
auto
eigen_place
=
context
.
GetEigenDevice
<
Place
>
();
if
(
x_g
)
{
x_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
// eigen matrix
auto
x_grad
=
EigenMatrix
<
T
>::
From
(
*
x_g
,
framework
::
make_ddim
({
x_dims
[
0
],
cols
}));
// dimensions are same with subResult
x_grad
.
device
(
eigen_place
)
=
grad_mat
;
}
if
(
y_g
)
{
y_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
PADDLE_ENFORCE_GE
(
sub_result
.
dimensions
()[
0
],
y_dims
[
0
],
"First dimension of gradient must be greater or "
"equal than first dimension of target."
);
if
(
sub_result
.
dimensions
()[
0
]
==
y_dims
[
0
])
{
auto
y_grad
=
EigenMatrix
<
T
>::
From
(
*
y_g
,
framework
::
make_ddim
({
y_dims
[
0
],
cols
}));
y_grad
.
device
(
eigen_place
)
=
-
1
*
grad_mat
;
}
else
{
auto
col_sum_res
=
-
1
*
(
grad_mat
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}})));
auto
y_grad
=
EigenVector
<
T
>::
Flatten
(
*
y_g
);
y_grad
.
device
(
eigen_place
)
=
col_sum_res
;
}
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/pybind/pybind.cc
浏览文件 @
a072ab9e
...
...
@@ -49,6 +49,7 @@ USE_OP(minus);
USE_OP
(
cos_sim
);
USE_CPU_ONLY_OP
(
gather
);
USE_CPU_ONLY_OP
(
scatter
);
USE_OP
(
squared_l2_distance
);
namespace
paddle
{
namespace
framework
{
...
...
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @
a072ab9e
...
...
@@ -33,3 +33,4 @@ py_test(test_gradient_checker SRCS test_gradient_checker.py)
py_test
(
test_lookup_table SRCS test_lookup_table.py
)
py_test
(
test_scale_and_identity_op SRCS test_scale_and_identity_op.py
)
py_test
(
mnist SRCS mnist.py
)
py_test
(
test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py
)
python/paddle/v2/framework/tests/op_test_util.py
浏览文件 @
a072ab9e
...
...
@@ -66,7 +66,7 @@ class OpTestMeta(type):
self
.
assertTrue
(
numpy
.
allclose
(
actual
,
expect
,
atol
=
1e-05
),
"output name: "
+
out_name
+
"has diff"
)
"output name: "
+
out_name
+
"
has diff"
)
obj
.
test_all
=
test_all
return
obj
python/paddle/v2/framework/tests/test_squared_l2_distance_op.py
0 → 100644
浏览文件 @
a072ab9e
import
unittest
from
op_test_util
import
OpTestMeta
from
gradient_checker
import
GradientChecker
,
create_op
import
numpy
as
np
class
TestSquaredL2DistanceOp_f0
(
unittest
.
TestCase
):
__metaclass__
=
OpTestMeta
def
setUp
(
self
):
self
.
type
=
'squared_l2_distance'
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1.
,
(
32
,
64
)).
astype
(
'float32'
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
1.
,
(
32
,
64
)).
astype
(
'float32'
)
}
sub_res
=
self
.
inputs
[
'X'
]
-
self
.
inputs
[
'Y'
]
output
=
sub_res
*
sub_res
self
.
outputs
=
{
'sub_result'
:
sub_res
,
'Out'
:
np
.
expand_dims
(
output
.
sum
(
1
),
1
)
}
class
TestSquaredL2DistanceOp_f1
(
unittest
.
TestCase
):
__metaclass__
=
OpTestMeta
def
setUp
(
self
):
self
.
type
=
'squared_l2_distance'
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1.
,
(
32
,
64
)).
astype
(
'float32'
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
1.
,
(
1
,
64
)).
astype
(
'float32'
)
}
sub_res
=
self
.
inputs
[
'X'
]
-
self
.
inputs
[
'Y'
]
output
=
sub_res
*
sub_res
self
.
outputs
=
{
'sub_result'
:
sub_res
,
'Out'
:
np
.
expand_dims
(
output
.
sum
(
1
),
1
)
}
class
TestSquaredL2DistanceOp_f2
(
unittest
.
TestCase
):
__metaclass__
=
OpTestMeta
def
setUp
(
self
):
self
.
type
=
'squared_l2_distance'
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1.
,
(
32
,
64
,
128
)).
astype
(
'float32'
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
1.
,
(
1
,
64
,
128
)).
astype
(
'float32'
)
}
sub_res
=
self
.
inputs
[
'X'
]
-
self
.
inputs
[
'Y'
]
sub_res
=
sub_res
.
reshape
((
32
,
64
*
128
))
output
=
sub_res
*
sub_res
self
.
outputs
=
{
'sub_result'
:
sub_res
,
'Out'
:
np
.
expand_dims
(
output
.
sum
(
1
),
1
)
}
class
TestSquaredL2DistanceGradOp
(
GradientChecker
):
def
test_squared_l2_distance_b0
(
self
):
op
=
create_op
(
"squared_l2_distance"
)
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
.
6
,
(
2
,
3
)).
astype
(
'float32'
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
.
6
,
(
2
,
3
)).
astype
(
'float32'
)
}
self
.
compare_grad
(
op
,
inputs
)
self
.
check_grad
(
op
,
inputs
,
set
([
"X"
,
"Y"
]),
"Out"
)
def
test_squared_l2_distance_b1
(
self
):
op
=
create_op
(
"squared_l2_distance"
)
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
.
6
,
(
2
,
3
)).
astype
(
'float32'
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
.
6
,
(
1
,
3
)).
astype
(
'float32'
)
}
self
.
compare_grad
(
op
,
inputs
)
self
.
check_grad
(
op
,
inputs
,
set
([
"X"
,
"Y"
]),
"Out"
)
def
test_squared_l2_distance_b2
(
self
):
op
=
create_op
(
"squared_l2_distance"
)
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
.
6
,
(
2
,
3
,
4
)).
astype
(
'float32'
),
'Y'
:
np
.
random
.
uniform
(
0.1
,
.
6
,
(
1
,
3
,
4
)).
astype
(
'float32'
)
}
self
.
compare_grad
(
op
,
inputs
)
self
.
check_grad
(
op
,
inputs
,
set
([
"X"
,
"Y"
]),
"Out"
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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