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bd561384
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
d9673cad
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
133 addition
and
140 deletion
+133
-140
paddle/operators/math/unpooling.cc
paddle/operators/math/unpooling.cc
+5
-12
paddle/operators/math/unpooling.cu
paddle/operators/math/unpooling.cu
+40
-47
paddle/operators/math/unpooling.h
paddle/operators/math/unpooling.h
+4
-5
paddle/operators/unpool_op.cc
paddle/operators/unpool_op.cc
+71
-63
paddle/operators/unpool_op.h
paddle/operators/unpool_op.h
+4
-4
python/paddle/v2/fluid/tests/test_unpool_op.py
python/paddle/v2/fluid/tests/test_unpool_op.py
+9
-9
未找到文件。
paddle/operators/math/unpooling.cc
浏览文件 @
bd561384
...
...
@@ -17,15 +17,13 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
namespace
math
{
// All tensors are in NCHW format
template
<
typename
T
>
class
Unpool2dMaxFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
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
];
...
...
@@ -40,7 +38,7 @@ class Unpool2dMaxFunctor<platform::CPUPlace, T> {
for
(
int
b
=
0
;
b
<
batch_size
;
++
b
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
i
=
0
;
i
<
input_feasize
;
++
i
)
{
int
index
=
indices_data
[
i
];
int
index
=
indices_data
[
i
];
PADDLE_ENFORCE
(
index
<
output_feasize
,
"err index in unpooling!"
);
output_data
[
index
]
=
input_data
[
i
];
}
...
...
@@ -51,9 +49,6 @@ class Unpool2dMaxFunctor<platform::CPUPlace, T> {
}
}
};
template
<
class
T
>
class
Unpool2dMaxGradFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
...
...
@@ -62,7 +57,7 @@ public:
const
framework
::
Tensor
&
indices
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
framework
::
Tensor
*
input_grad
)
{
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -89,12 +84,10 @@ public:
}
}
};
template
class
Unpool2dMaxGradFunctor
<
platform
::
CPUPlace
,
float
>;
template
class
Unpool2dMaxGradFunctor
<
platform
::
CPUPlace
,
double
>;
template
class
Unpool2dMaxFunctor
<
platform
::
CPUPlace
,
float
>;
template
class
Unpool2dMaxFunctor
<
platform
::
CPUPlace
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/unpooling.cu
浏览文件 @
bd561384
...
...
@@ -18,36 +18,33 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
T
>
__global__
void
KernelUnpool2dMax
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
__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
)
{
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
;
int
out_c_stride
=
output_height
*
output_width
;
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
offset
=
blockDim
.
x
*
gridDim
.
x
;
for
(
int
i
=
index
;
i
<
nthreads
;
i
+=
offset
)
{
int
bidx
=
i
/
in_n_stride
;
int
boffset
=
i
%
in_n_stride
;
int
cidx
=
boffset
/
in_c_stride
;
int
out_offset
=
bidx
*
out_n_stride
+
cidx
*
out_c_stride
;
int
out_index
=
indices_data
[
i
];
PADDLE_ASSERT
(
out_index
<
out_c_stride
);
output_data
[
out_offset
+
out_index
]
=
input_data
[
i
];
}
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
;
int
out_c_stride
=
output_height
*
output_width
;
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
offset
=
blockDim
.
x
*
gridDim
.
x
;
for
(
int
i
=
index
;
i
<
nthreads
;
i
+=
offset
)
{
int
bidx
=
i
/
in_n_stride
;
int
boffset
=
i
%
in_n_stride
;
int
cidx
=
boffset
/
in_c_stride
;
int
out_offset
=
bidx
*
out_n_stride
+
cidx
*
out_c_stride
;
int
out_index
=
indices_data
[
i
];
PADDLE_ASSERT
(
out_index
<
out_c_stride
);
output_data
[
out_offset
+
out_index
]
=
input_data
[
i
];
}
}
template
<
typename
T
>
__global__
void
KernelUnpool2dMaxGrad
(
const
int
nthreads
,
const
T
*
input_data
,
__global__
void
KernelUnpool2dMaxGrad
(
const
int
nthreads
,
const
T
*
input_data
,
const
int
*
indices_data
,
const
int
input_height
,
const
int
input_width
,
...
...
@@ -57,32 +54,32 @@ __global__ void KernelUnpool2dMaxGrad(const int nthreads,
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
;
int
out_c_stride
=
output_height
*
output_width
;
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
offset
=
blockDim
.
x
*
gridDim
.
x
;
for
(
int
i
=
index
;
i
<
nthreads
;
i
+=
offset
)
{
int
bidx
=
i
/
in_n_stride
;
int
boffset
=
i
%
in_n_stride
;
int
cidx
=
boffset
/
in_c_stride
;
int
out_offset
=
bidx
*
out_n_stride
+
cidx
*
out_c_stride
;
int
out_index
=
indices_data
[
i
];
PADDLE_ASSERT
(
out_index
<
out_c_stride
);
input_grad
[
i
]
=
output_grad
[
out_offset
+
out_index
];
}
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
;
int
out_c_stride
=
output_height
*
output_width
;
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
int
offset
=
blockDim
.
x
*
gridDim
.
x
;
for
(
int
i
=
index
;
i
<
nthreads
;
i
+=
offset
)
{
int
bidx
=
i
/
in_n_stride
;
int
boffset
=
i
%
in_n_stride
;
int
cidx
=
boffset
/
in_c_stride
;
int
out_offset
=
bidx
*
out_n_stride
+
cidx
*
out_c_stride
;
int
out_index
=
indices_data
[
i
];
PADDLE_ASSERT
(
out_index
<
out_c_stride
);
input_grad
[
i
]
=
output_grad
[
out_offset
+
out_index
];
}
}
/*
* All tensors are in NCHW format.
*/
template
<
typename
T
>
class
Unpool2dMaxFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
framework
::
Tensor
*
output
)
{
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -93,7 +90,7 @@ class Unpool2dMaxFunctor<platform::GPUPlace, T> {
const
int
*
indices_data
=
indices
.
data
<
int
>
();
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
threads
=
1024
;
int
grid
=
(
input
.
numel
()
+
threads
-
1
)
/
threads
;
int
grid
=
(
input
.
numel
()
+
threads
-
1
)
/
threads
;
KernelUnpool2dMax
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
...
...
@@ -107,13 +104,13 @@ class Unpool2dMaxFunctor<platform::GPUPlace, T> {
*/
template
<
typename
T
>
class
Unpool2dMaxGradFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
framework
::
Tensor
*
input_grad
)
{
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
...
...
@@ -126,24 +123,20 @@ class Unpool2dMaxGradFunctor<platform::GPUPlace, T> {
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
int
threads
=
1024
;
int
grid
=
(
input
.
numel
()
+
threads
-
1
)
/
threads
;
int
grid
=
(
input
.
numel
()
+
threads
-
1
)
/
threads
;
KernelUnpool2dMaxGrad
<
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
);
output_height
,
output_width
,
input_grad_data
);
}
};
template
class
Unpool2dMaxGradFunctor
<
platform
::
GPUPlace
,
float
>;
template
class
Unpool2dMaxGradFunctor
<
platform
::
GPUPlace
,
double
>;
template
class
Unpool2dMaxFunctor
<
platform
::
GPUPlace
,
float
>;
template
class
Unpool2dMaxFunctor
<
platform
::
GPUPlace
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/unpooling.h
浏览文件 @
bd561384
...
...
@@ -22,22 +22,21 @@ namespace math {
template
<
typename
Place
,
typename
T
>
class
Unpool2dMaxFunctor
{
public:
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
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
indices
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
framework
::
Tensor
*
input_grad
);
framework
::
Tensor
*
input_grad
);
};
}
// namespace math
}
// namespace operators
...
...
paddle/operators/unpool_op.cc
浏览文件 @
bd561384
...
...
@@ -21,107 +21,115 @@ class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
Unpool2dOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
AddInput
(
"X"
,
"(Tensor) The input tensor of unpool operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature."
);
AddInput
(
"Indices"
,
AddInput
(
"Indices"
,
"(Tensor) The input tensor of the indices given out by MaxPool2d. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature."
);
AddOutput
(
"Out"
,
AddOutput
(
"Out"
,
"(Tensor) The output tensor of unpool operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is "
"the number of channels, H and W is the height and "
"width of feature."
);
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"(vector), the unpooling window size(height, width) "
"of unpooling operator."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector, default:{1, 1}), "
"strides (height, width) of unpooling operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"(vector defalut:{0,0}), "
"paddings (height, width) of unpooling operator."
)
.
SetDefault
({
0
,
0
});
AddAttr
<
std
::
string
>
(
"unpooling_type"
,
AddAttr
<
std
::
string
>
(
"unpooling_type"
,
"(string), unpooling type, can be
\"
max
\"
for max-unpooling "
)
.
InEnum
({
"max"
});
AddComment
(
R"DOC(
"Input shape: $(N, C_{in}, H_{in}, W_{in})$
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
"Input shape: $(N, C_{in}, H_{in}, W_{in})$
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out} = (H_{in}−1) * strides[0] − 2 * paddings[0] + ksize[0] \\
W_{out} = (W_{in}−1) * strides[1] − 2 * paddings[1] + ksize[1]
$$
Paper: http://www.matthewzeiler.com/wp-content/uploads/2017
/07/iccv2011.pdf
Paper: http://www.matthewzeiler.com/wp-content/uploads/2017
/07/iccv2011.pdf
)DOC"
);
}
};
int
OutputSize
(
int
input_size
,
int
ksize
,
int
padding
,
int
stride
)
{
int
output_size
=
(
input_size
-
1
)
*
stride
-
2
*
padding
+
ksize
;
int
output_size
=
(
input_size
-
1
)
*
stride
-
2
*
padding
+
ksize
;
return
output_size
;
}
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
()),
ctx
.
device_context
());
}
protected:
framework
::
OpKernelType
GetKernelType
(
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"
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"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Indices"
),
"Input(Indices) of UnpoolOp"
"should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
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
::
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."
);
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
]));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
}
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
::
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."
);
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
]));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
}
};
class
UnpoolOpGrad
:
public
framework
::
OperatorWithKernel
{
protected:
framework
::
OpKernelType
GetKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
Tensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
protected:
framework
::
OpKernelType
GetKernelType
(
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"
)),
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."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
}
// namespace operators
}
// namespace paddle
...
...
@@ -129,10 +137,10 @@ class UnpoolOpGrad : public framework::OperatorWithKernel {
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_grad
,
ops
::
UnpoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
UnpoolGradKernel
<
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.h
浏览文件 @
bd561384
...
...
@@ -27,7 +27,7 @@ class UnpoolKernel : public framework::OpKernel<T> {
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
framework
::
Tensor
*
in_x
=
context
.
Input
<
framework
::
Tensor
>
(
"X"
);
const
framework
::
Tensor
*
in_y
=
context
.
Input
<
framework
::
Tensor
>
(
"Indices"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
auto
*
out
=
context
.
Output
<
framework
::
Tensor
>
(
"Out"
);
std
::
string
unpooling_type
=
context
.
Attr
<
std
::
string
>
(
"unpooling_type"
);
std
::
vector
<
int
>
ksize
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
...
...
@@ -52,7 +52,7 @@ class UnpoolGradKernel : public framework::OpKernel<T> {
const
framework
::
Tensor
*
out_grad
=
context
.
Input
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
framework
::
Tensor
*
in_x_grad
=
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
context
.
Output
<
framework
::
Tensor
>
(
framework
::
GradVarName
(
"X"
));
std
::
string
unpooling_type
=
context
.
Attr
<
std
::
string
>
(
"unpooling_type"
);
std
::
vector
<
int
>
ksize
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
...
...
@@ -65,8 +65,8 @@ class UnpoolGradKernel : public framework::OpKernel<T> {
zero
(
device_ctx
,
in_x_grad
,
static_cast
<
T
>
(
0
));
}
math
::
Unpool2dMaxGradFunctor
<
Place
,
T
>
unpool2d_max_backward
;
unpool2d_max_backward
(
context
.
device_context
(),
*
in_x
,
*
in_y
,
*
out
,
*
out
_grad
,
in_x_grad
);
unpool2d_max_backward
(
context
.
device_context
(),
*
in_x
,
*
in_y
,
*
out
,
*
out_grad
,
in_x_grad
);
}
};
...
...
python/paddle/v2/fluid/tests/test_unpool_op.py
浏览文件 @
bd561384
...
...
@@ -52,14 +52,16 @@ class TestUnpoolOp(OpTest):
c_start
+
arg
%
self
.
ksize
[
1
]
output
=
self
.
unpool2d_forward_naive
(
input
,
indices
,
self
.
ksize
,
\
self
.
strides
,
self
.
paddings
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
input
.
astype
(
'float32'
),
'Indices'
:
indices
.
astype
(
'int32'
)}
self
.
inputs
=
{
'X'
:
input
.
astype
(
'float32'
),
'Indices'
:
indices
.
astype
(
'int32'
)
}
self
.
attrs
=
{
'strides'
:
self
.
strides
,
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'unpooling_type'
:
self
.
unpooling_type
,
}
'strides'
:
self
.
strides
,
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'unpooling_type'
:
self
.
unpooling_type
,
}
self
.
outputs
=
{
'Out'
:
output
.
astype
(
'float32'
)}
def
test_check_output
(
self
):
...
...
@@ -76,7 +78,5 @@ class TestUnpoolOp(OpTest):
self
.
strides
=
[
2
,
2
]
self
.
paddings
=
[
0
,
0
]
if
__name__
==
'__main__'
:
unittest
.
main
()
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