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3206094b
编写于
11月 29, 2017
作者:
S
sweetsky0901
浏览文件
操作
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电子邮件补丁
差异文件
format code
上级
d2ee3c98
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
46 addition
and
45 deletion
+46
-45
paddle/operators/math/unpooling.cc
paddle/operators/math/unpooling.cc
+2
-2
paddle/operators/math/unpooling.cu
paddle/operators/math/unpooling.cu
+18
-18
paddle/operators/math/unpooling.h
paddle/operators/math/unpooling.h
+1
-2
paddle/operators/unpool_op.cc
paddle/operators/unpool_op.cc
+22
-20
paddle/operators/unpool_op.cu.cc
paddle/operators/unpool_op.cu.cc
+3
-3
未找到文件。
paddle/operators/math/unpooling.cc
浏览文件 @
3206094b
...
...
@@ -20,8 +20,8 @@ template <typename T>
class
Unpool2dMaxFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
paddle/operators/math/unpooling.cu
浏览文件 @
3206094b
...
...
@@ -20,11 +20,12 @@ namespace operators {
namespace
math
{
template
<
typename
T
>
__global__
void
KernelUnpool2dMax
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
const
int
channels
,
T
*
output_data
,
const
int
output_height
,
const
int
output_width
)
{
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
const
int
channels
,
T
*
output_data
,
const
int
output_height
,
const
int
output_width
)
{
int
in_n_stride
=
input_height
*
input_width
*
channels
;
int
in_c_stride
=
input_height
*
input_width
;
int
out_n_stride
=
output_height
*
output_width
*
channels
;
...
...
@@ -42,12 +43,11 @@ __global__ void KernelUnpool2dMax(const int nthreads, const T* input_data,
}
}
template
<
typename
T
>
__global__
void
KernelUnpool2dMaxGrad
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
const
int
channels
,
const
T
*
output_data
,
const
T
*
output_grad
,
const
int
output_height
,
const
int
output_width
,
T
*
input_grad
)
{
__global__
void
KernelUnpool2dMaxGrad
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
const
int
channels
,
const
T
*
output_data
,
const
T
*
output_grad
,
const
int
output_height
,
const
int
output_width
,
T
*
input_grad
)
{
int
in_n_stride
=
input_height
*
input_width
*
channels
;
int
in_c_stride
=
input_height
*
input_width
;
int
out_n_stride
=
output_height
*
output_width
*
channels
;
...
...
@@ -71,8 +71,8 @@ template <typename T>
class
Unpool2dMaxFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -88,8 +88,8 @@ class Unpool2dMaxFunctor<platform::GPUPlace, T> {
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
input
.
numel
(),
input_data
,
indices_data
,
input_height
,
input_width
,
output_channels
,
output_data
,
output_height
,
output_width
);
input_height
,
input_width
,
output_channels
,
output_data
,
output_height
,
output_width
);
}
};
/*
...
...
@@ -121,9 +121,9 @@ class Unpool2dMaxGradFunctor<platform::GPUPlace, T> {
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
input
.
numel
(),
input_data
,
indices_data
,
input_height
,
input_width
,
output_channels
,
output_data
,
output_grad_data
,
output_height
,
output_width
,
input_grad_data
);
input_height
,
input_width
,
output_channels
,
output_data
,
output_grad_data
,
output_height
,
output_width
,
input_grad_data
);
}
};
template
class
Unpool2dMaxGradFunctor
<
platform
::
GPUPlace
,
float
>;
...
...
paddle/operators/math/unpooling.h
浏览文件 @
3206094b
...
...
@@ -23,8 +23,7 @@ class Unpool2dMaxFunctor {
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
);
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
);
};
template
<
typename
Place
,
class
T
>
class
Unpool2dMaxGradFunctor
{
...
...
paddle/operators/unpool_op.cc
浏览文件 @
3206094b
...
...
@@ -75,36 +75,38 @@ int OutputSize(int input_size, int ksize, int padding, int stride) {
class
UnpoolOp
:
public
framework
::
OperatorWithKernel
{
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
}
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of UnpoolOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Indices"
),
"Input(Indices) of UnpoolOp"
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of UnpoolOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Indices"
),
"Input(Indices) of UnpoolOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of UnpoolOp should not be null."
);
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
auto
in_y_dims
=
ctx
->
GetInputDim
(
"Indices"
);
std
::
string
unpooling_type
=
ctx
->
Attrs
()
.
Get
<
std
::
string
>
(
"unpooling_type"
);
std
::
string
unpooling_type
=
ctx
->
Attrs
()
.
Get
<
std
::
string
>
(
"unpooling_type"
);
std
::
vector
<
int
>
ksize
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
PADDLE_ENFORCE
(
in_x_dims
.
size
()
==
4
,
"Unpooling intput must be of 4-dimensional."
);
"Unpooling intput must be of 4-dimensional."
);
PADDLE_ENFORCE_EQ
(
in_x_dims
,
in_y_dims
);
std
::
vector
<
int64_t
>
output_shape
({
in_x_dims
[
0
],
in_x_dims
[
1
]});
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
output_shape
.
push_back
(
OutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
OutputSize
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
}
...
...
@@ -113,30 +115,30 @@ class UnpoolOp : public framework::OperatorWithKernel {
class
UnpoolOpGrad
:
public
framework
::
OperatorWithKernel
{
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
}
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Input(X@GRAD) should not be null."
);
"Input(X@GRAD) should not be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
}
// namespace operators
}
// namespace paddle
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
unpool
,
ops
::
UnpoolOp
,
ops
::
Unpool2dOpMaker
,
unpool_grad
,
ops
::
UnpoolOpGrad
);
REGISTER_OP_CPU_KERNEL
(
unpool
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
unpool
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
REGISTER_OP_CPU_KERNEL
(
unpool_grad
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
);
...
...
paddle/operators/unpool_op.cu.cc
浏览文件 @
3206094b
...
...
@@ -15,9 +15,9 @@ limitations under the License. */
#include "paddle/operators/unpool_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
unpool
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
unpool
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
UnpoolKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
REGISTER_OP_GPU_KERNEL
(
unpool_grad
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
double
>
);
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