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3e1050a2
编写于
8月 27, 2018
作者:
C
chengduo
提交者:
GitHub
8月 27, 2018
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电子邮件补丁
差异文件
Add pad_constant_like_op (#12943)
* Add pad_constant_batch_size_like * refine pad_op * optimize memory
上级
d361624c
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
529 addition
and
93 deletion
+529
-93
paddle/fluid/operators/math/padding.h
paddle/fluid/operators/math/padding.h
+124
-0
paddle/fluid/operators/pad_constant_like_op.cc
paddle/fluid/operators/pad_constant_like_op.cc
+196
-0
paddle/fluid/operators/pad_constant_like_op.cu
paddle/fluid/operators/pad_constant_like_op.cu
+27
-0
paddle/fluid/operators/pad_constant_like_op.h
paddle/fluid/operators/pad_constant_like_op.h
+93
-0
paddle/fluid/operators/pad_op.h
paddle/fluid/operators/pad_op.h
+20
-93
python/paddle/fluid/tests/unittests/test_pad_constant_like.py
...on/paddle/fluid/tests/unittests/test_pad_constant_like.py
+69
-0
未找到文件。
paddle/fluid/operators/math/padding.h
0 → 100644
浏览文件 @
3e1050a2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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 <utility>
#include <vector>
#include "paddle/fluid/framework/tensor.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
T
,
size_t
D
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenTensor
=
framework
::
EigenTensor
<
T
,
D
,
MajorType
,
IndexType
>
;
template
<
typename
DeviceContext
,
typename
T
,
size_t
D
>
void
PadFunction
(
const
framework
::
ExecutionContext
&
context
,
const
std
::
vector
<
int
>&
pads
,
const
framework
::
Tensor
&
src
,
T
pad_value
,
framework
::
Tensor
*
out
)
{
Eigen
::
array
<
std
::
pair
<
int
,
int
>
,
D
>
paddings
;
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
++
i
)
{
paddings
[
i
].
first
=
pads
[
i
*
2
];
paddings
[
i
].
second
=
pads
[
i
*
2
+
1
];
}
auto
src_tensor
=
EigenTensor
<
T
,
D
>::
From
(
src
);
auto
out_tensor
=
EigenTensor
<
T
,
D
>::
From
(
*
out
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
out_tensor
.
device
(
place
)
=
src_tensor
.
pad
(
paddings
,
pad_value
);
}
template
<
typename
DeviceContext
,
typename
T
,
size_t
D
>
void
PadGradFunction
(
const
framework
::
ExecutionContext
&
context
,
const
std
::
vector
<
int
>&
pads
,
const
framework
::
Tensor
&
src
,
framework
::
Tensor
*
d_out
)
{
Eigen
::
array
<
std
::
pair
<
int
,
int
>
,
D
>
paddings
;
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
++
i
)
{
paddings
[
i
].
first
=
-
pads
[
i
*
2
];
paddings
[
i
].
second
=
-
pads
[
i
*
2
+
1
];
}
auto
d_out_tensor
=
EigenTensor
<
T
,
D
>::
From
(
*
d_out
);
auto
src_tensor
=
EigenTensor
<
T
,
D
>::
From
(
src
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
d_out_tensor
.
device
(
place
)
=
src_tensor
.
pad
(
paddings
,
0
);
}
template
<
typename
DeviceContext
,
typename
T
>
void
PaddingFunctor
(
int
rank
,
const
framework
::
ExecutionContext
&
context
,
const
std
::
vector
<
int
>&
pads
,
T
pad_value
,
const
framework
::
Tensor
&
src
,
framework
::
Tensor
*
out
)
{
switch
(
rank
)
{
case
1
:
PadFunction
<
DeviceContext
,
T
,
1
>
(
context
,
pads
,
src
,
pad_value
,
out
);
break
;
case
2
:
PadFunction
<
DeviceContext
,
T
,
2
>
(
context
,
pads
,
src
,
pad_value
,
out
);
break
;
case
3
:
PadFunction
<
DeviceContext
,
T
,
3
>
(
context
,
pads
,
src
,
pad_value
,
out
);
break
;
case
4
:
PadFunction
<
DeviceContext
,
T
,
4
>
(
context
,
pads
,
src
,
pad_value
,
out
);
break
;
case
5
:
PadFunction
<
DeviceContext
,
T
,
5
>
(
context
,
pads
,
src
,
pad_value
,
out
);
break
;
case
6
:
PadFunction
<
DeviceContext
,
T
,
6
>
(
context
,
pads
,
src
,
pad_value
,
out
);
break
;
default:
PADDLE_THROW
(
"PadOp only support tensors with no more than 6 dimensions."
);
}
}
template
<
typename
DeviceContext
,
typename
T
>
void
PaddingGradFunctor
(
int
rank
,
const
framework
::
ExecutionContext
&
context
,
const
std
::
vector
<
int
>&
pads
,
const
framework
::
Tensor
&
src
,
framework
::
Tensor
*
out
)
{
switch
(
rank
)
{
case
1
:
PadGradFunction
<
DeviceContext
,
T
,
1
>
(
context
,
pads
,
src
,
out
);
break
;
case
2
:
PadGradFunction
<
DeviceContext
,
T
,
2
>
(
context
,
pads
,
src
,
out
);
break
;
case
3
:
PadGradFunction
<
DeviceContext
,
T
,
3
>
(
context
,
pads
,
src
,
out
);
break
;
case
4
:
PadGradFunction
<
DeviceContext
,
T
,
4
>
(
context
,
pads
,
src
,
out
);
break
;
case
5
:
PadGradFunction
<
DeviceContext
,
T
,
5
>
(
context
,
pads
,
src
,
out
);
break
;
case
6
:
PadGradFunction
<
DeviceContext
,
T
,
6
>
(
context
,
pads
,
src
,
out
);
break
;
default:
PADDLE_THROW
(
"PadOp only support tensors with no more than 6 dimensions."
);
}
}
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/pad_constant_like_op.cc
0 → 100644
浏览文件 @
3e1050a2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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/fluid/operators/pad_constant_like_op.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
PadConstantLikeOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of PadConstantLikeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"Input(Y) of PadConstantLikeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of PadConstantLikeOp should not be null."
);
auto
x_dim
=
ctx
->
GetInputDim
(
"X"
);
auto
y_dim
=
ctx
->
GetInputDim
(
"Y"
);
PADDLE_ENFORCE_EQ
(
x_dim
.
size
(),
y_dim
.
size
(),
"The dimention of X and Y should be the same."
);
for
(
int
i
=
0
;
i
<
x_dim
.
size
();
++
i
)
{
PADDLE_ENFORCE_GE
(
x_dim
[
i
],
y_dim
[
i
]);
}
ctx
->
SetOutputDim
(
"Out"
,
x_dim
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
};
class
PadConstantLikeOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"The input of pad_constant_like op. "
"The input should be a k-D tensor(k > 0 and k < 7)"
);
AddInput
(
"Y"
,
"The input of pad_constant_like op. "
"The input should be a k-D tensor(k > 0 and k < 7)"
);
AddOutput
(
"Out"
,
"The output of pad_constant_like op. "
"A tensor with the same shape as X."
);
AddAttr
<
float
>
(
"pad_value"
,
"(float, default 0.0) "
"The value to fill the padded areas."
)
.
SetDefault
(
0.0
f
);
AddComment
(
R"DOC(
PadConstantLikeOp Operator.
Pad input(Y) with a pad_value, the number of values padded to the edges of each
axis is specified by the difference of the shape of X and Y.
((0, shape_x_0 - shape_y_0), … (0, shape_x_n - shape_y_n)) unique pad widths for
each axis.
The input should be a k-D tensor(k > 0 and k < 7). As an example:
case1:
Given:
X = [[1, 2],
[3, 4],
[1, 2],
[3, 4]]],
X.shape = (4, 2)
Y = [[5, 6],
[7, 8]],
Y.shape = (2, 2)
And
pad_value = 0,
Return:
Out = [[5, 6],
[7, 8],
[0, 0],
[0, 0]]
Out.shape = (4, 2)
case2:
Given:
X = [[[[ 0, 1, 2],
[ 3, 4, 5]],
[[ 6, 7, 8],
[ 9, 10, 11]],
[[12, 13, 14],
[15, 16, 17]]],
[[[18, 19, 20],
[21, 22, 23]],
[[24, 25, 26],
[27, 28, 29]],
[[30, 31, 32],
[33, 34, 35]]]]
X.shape = (2, 3, 2, 3)
Y = [[[[35, 36, 37]],
[[38, 39, 40]],
[[41, 42, 43]]]]
Y.shape = (1, 3, 1, 3)
And
pad_value = -1,
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
)DOC"
);
}
};
class
PadConstantLikeOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"Input(Y) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
y_dim
=
ctx
->
GetInputDim
(
"Y"
);
auto
dout_dim
=
ctx
->
GetInputDim
(
framework
::
GradVarName
(
"Out"
));
PADDLE_ENFORCE_EQ
(
dout_dim
.
size
(),
y_dim
.
size
(),
"The dimention of X and Y should be the same."
);
auto
y_grad_name
=
framework
::
GradVarName
(
"Y"
);
if
(
ctx
->
HasOutput
(
y_grad_name
))
{
ctx
->
SetOutputDim
(
y_grad_name
,
y_dim
);
ctx
->
ShareLoD
(
"Y"
,
/*->*/
y_grad_name
);
for
(
int
i
=
0
;
i
<
y_dim
.
size
();
++
i
)
{
PADDLE_ENFORCE_GE
(
dout_dim
[
i
],
y_dim
[
i
]);
}
}
}
};
class
PadConstantLikeOpGradMaker
:
public
framework
::
SingleGradOpDescMaker
{
public:
using
framework
::
SingleGradOpDescMaker
::
SingleGradOpDescMaker
;
protected:
std
::
unique_ptr
<
framework
::
OpDesc
>
Apply
()
const
override
{
auto
*
bind
=
new
framework
::
OpDesc
();
bind
->
SetType
(
"pad_constant_like_grad"
);
bind
->
SetInput
(
"Y"
,
Input
(
"Y"
));
bind
->
SetInput
(
framework
::
GradVarName
(
"Out"
),
OutputGrad
(
"Out"
));
bind
->
SetOutput
(
framework
::
GradVarName
(
"Y"
),
InputGrad
(
"Y"
));
bind
->
SetAttrMap
(
Attrs
());
return
std
::
unique_ptr
<
framework
::
OpDesc
>
(
bind
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
pad_constant_like
,
ops
::
PadConstantLikeOp
,
ops
::
PadConstantLikeOpMaker
,
ops
::
PadConstantLikeOpGradMaker
);
REGISTER_OPERATOR
(
pad_constant_like_grad
,
ops
::
PadConstantLikeOpGrad
);
REGISTER_OP_CPU_KERNEL
(
pad_constant_like
,
ops
::
PadConstantLikeKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
PadConstantLikeKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
pad_constant_like_grad
,
ops
::
PadConstantLikeGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
PadConstantLikeGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
);
paddle/fluid/operators/pad_constant_like_op.cu
0 → 100644
浏览文件 @
3e1050a2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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/fluid/operators/pad_constant_like_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
pad_constant_like
,
ops
::
PadConstantLikeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
PadConstantLikeKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
REGISTER_OP_CUDA_KERNEL
(
pad_constant_like_grad
,
ops
::
PadConstantLikeGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
PadConstantLikeGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
);
paddle/fluid/operators/pad_constant_like_op.h
0 → 100644
浏览文件 @
3e1050a2
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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 <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/padding.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
class
PadConstantLikeKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
in_x
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
auto
in_y
=
context
.
Input
<
framework
::
Tensor
>
(
"Y"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
if
(
in_x
->
dims
()
==
in_y
->
dims
())
{
// TensorCopy(in_y, context.GetPlace(), context, out);
out
->
ShareDataWith
(
*
in_y
);
return
;
}
T
pad_value
=
context
.
Attr
<
T
>
(
"pad_value"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
rank
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
dims
().
size
();
std
::
vector
<
int
>
pads
(
rank
*
2
,
0
);
for
(
int
j
=
0
;
j
<
rank
;
++
j
)
{
pads
[
j
*
2
]
=
0
;
pads
[
j
*
2
+
1
]
=
static_cast
<
int
>
(
in_x
->
dims
()[
j
]
-
in_y
->
dims
()[
j
]);
}
math
::
PaddingFunctor
<
DeviceContext
,
T
>
(
rank
,
context
,
pads
,
pad_value
,
*
in_y
,
out
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
PadConstantLikeGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
in_y
=
context
.
Input
<
framework
::
Tensor
>
(
"Y"
);
auto
in_dout
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_y
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Y"
));
if
(
d_y
==
nullptr
)
{
return
;
}
if
(
in_dout
->
dims
()
==
in_y
->
dims
())
{
// TensorCopy(in_dout, context.GetPlace(), context, d_y);
d_y
->
ShareDataWith
(
*
in_dout
);
return
;
}
d_y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
rank
=
in_dout
->
dims
().
size
();
std
::
vector
<
int
>
pads
(
static_cast
<
size_t
>
(
rank
)
*
2
,
0
);
for
(
int
j
=
0
;
j
<
rank
;
++
j
)
{
pads
[
j
*
2
]
=
0
;
pads
[
j
*
2
+
1
]
=
static_cast
<
int
>
(
in_dout
->
dims
()[
j
]
-
in_y
->
dims
()[
j
]);
}
math
::
PaddingGradFunctor
<
DeviceContext
,
T
>
(
rank
,
context
,
pads
,
*
in_dout
,
d_y
);
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/pad_op.h
浏览文件 @
3e1050a2
...
...
@@ -18,117 +18,44 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/padding.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
size_t
D
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenTensor
=
framework
::
EigenTensor
<
T
,
D
,
MajorType
,
IndexType
>
;
template
<
typename
DeviceContext
,
typename
T
,
size_t
D
>
void
PadFunction
(
const
framework
::
ExecutionContext
&
context
)
{
auto
pads
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
Eigen
::
array
<
std
::
pair
<
int
,
int
>
,
D
>
paddings
;
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
++
i
)
{
paddings
[
i
].
first
=
pads
[
i
*
2
];
paddings
[
i
].
second
=
pads
[
i
*
2
+
1
];
}
T
pad_value
=
context
.
Attr
<
T
>
(
"pad_value"
);
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
x_tensor
=
EigenTensor
<
T
,
D
>::
From
(
*
x
);
auto
out_tensor
=
EigenTensor
<
T
,
D
>::
From
(
*
out
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
out_tensor
.
device
(
place
)
=
x_tensor
.
pad
(
paddings
,
pad_value
);
}
template
<
typename
DeviceContext
,
typename
T
>
class
PadKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
int
rank
=
context
.
Input
<
Tensor
>
(
"X"
)
->
dims
().
size
();
switch
(
rank
)
{
case
1
:
PadFunction
<
DeviceContext
,
T
,
1
>
(
context
);
break
;
case
2
:
PadFunction
<
DeviceContext
,
T
,
2
>
(
context
);
break
;
case
3
:
PadFunction
<
DeviceContext
,
T
,
3
>
(
context
);
break
;
case
4
:
PadFunction
<
DeviceContext
,
T
,
4
>
(
context
);
break
;
case
5
:
PadFunction
<
DeviceContext
,
T
,
5
>
(
context
);
break
;
case
6
:
PadFunction
<
DeviceContext
,
T
,
6
>
(
context
);
break
;
default:
PADDLE_THROW
(
"PadOp only support tensors with no more than 6 dimensions."
);
}
auto
pads
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
T
pad_value
=
context
.
Attr
<
T
>
(
"pad_value"
);
auto
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
rank
=
x
->
dims
().
size
();
math
::
PaddingFunctor
<
DeviceContext
,
T
>
(
rank
,
context
,
pads
,
pad_value
,
*
x
,
out
);
}
};
template
<
typename
DeviceContext
,
typename
T
,
size_t
D
>
void
PadGradFunction
(
const
framework
::
ExecutionContext
&
context
)
{
auto
pads
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
Eigen
::
array
<
std
::
pair
<
int
,
int
>
,
D
>
paddings
;
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
++
i
)
{
paddings
[
i
].
first
=
-
pads
[
i
*
2
];
paddings
[
i
].
second
=
-
pads
[
i
*
2
+
1
];
}
auto
*
d_out
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_x
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
d_x
!=
nullptr
)
{
d_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
d_x_tensor
=
EigenTensor
<
T
,
D
>::
From
(
*
d_x
);
auto
d_out_tensor
=
EigenTensor
<
T
,
D
>::
From
(
*
d_out
);
auto
&
place
=
*
context
.
template
device_context
<
DeviceContext
>().
eigen_device
();
d_x_tensor
.
device
(
place
)
=
d_out_tensor
.
pad
(
paddings
,
0
);
}
}
template
<
typename
DeviceContext
,
typename
T
>
class
PadGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
size_t
rank
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
().
size
();
switch
(
rank
)
{
case
1
:
PadGradFunction
<
DeviceContext
,
T
,
1
>
(
context
);
break
;
case
2
:
PadGradFunction
<
DeviceContext
,
T
,
2
>
(
context
);
break
;
case
3
:
PadGradFunction
<
DeviceContext
,
T
,
3
>
(
context
);
break
;
case
4
:
PadGradFunction
<
DeviceContext
,
T
,
4
>
(
context
);
break
;
case
5
:
PadGradFunction
<
DeviceContext
,
T
,
5
>
(
context
);
break
;
case
6
:
PadGradFunction
<
DeviceContext
,
T
,
6
>
(
context
);
break
;
default:
PADDLE_THROW
(
"PadOp only support tensors with no more than 6 dimensions."
);
auto
pads
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
auto
*
d_out
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_x
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
if
(
d_x
==
nullptr
)
{
return
;
}
d_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
rank
=
d_out
->
dims
().
size
();
math
::
PaddingGradFunctor
<
DeviceContext
,
T
>
(
rank
,
context
,
pads
,
*
d_out
,
d_x
);
}
};
...
...
python/paddle/fluid/tests/unittests/test_pad_constant_like.py
0 → 100644
浏览文件 @
3e1050a2
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestPadOp
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
self
.
op_type
=
"pad_constant_like"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
self
.
x_shape
).
astype
(
"float32"
),
'Y'
:
np
.
random
.
random
(
self
.
y_shape
).
astype
(
"float32"
)
}
self
.
attrs
=
{}
self
.
attrs
[
'pad_value'
]
=
self
.
pad_value
self
.
outputs
=
{
'Out'
:
np
.
pad
(
self
.
inputs
[
'Y'
],
self
.
paddings
,
mode
=
'constant'
,
constant_values
=
self
.
pad_value
)
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad_normal
(
self
):
self
.
check_grad
([
'Y'
],
'Out'
,
max_relative_error
=
0.006
)
def
initTestCase
(
self
):
self
.
x_shape
=
(
16
,
16
)
self
.
y_shape
=
(
3
,
16
)
self
.
pad_value
=
0.1
self
.
paddings
=
[(
0
,
13
),
(
0
,
0
)]
class
TestCase1
(
TestPadOp
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
4
,
3
,
4
,
4
)
self
.
y_shape
=
(
2
,
3
,
4
,
4
)
self
.
paddings
=
[(
0
,
2
),
(
0
,
0
),
(
0
,
0
),
(
0
,
0
)]
self
.
pad_value
=
0.5
class
TestCase2
(
TestPadOp
):
def
initTestCase
(
self
):
self
.
x_shape
=
(
4
,
3
,
4
,
4
)
self
.
y_shape
=
(
2
,
3
,
2
,
4
)
self
.
paddings
=
[(
0
,
2
),
(
0
,
0
),
(
0
,
2
),
(
0
,
0
)]
self
.
pad_value
=
0.5
if
__name__
==
'__main__'
:
unittest
.
main
()
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