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a24691a2
编写于
10月 31, 2018
作者:
D
dengkaipeng
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add nearest neighbor interpolation operator cpu kernel
上级
d2e622f3
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
455 addition
and
0 deletion
+455
-0
paddle/fluid/operators/nearest_neighbor_interp_op.cc
paddle/fluid/operators/nearest_neighbor_interp_op.cc
+115
-0
paddle/fluid/operators/nearest_neighbor_interp_op.cu
paddle/fluid/operators/nearest_neighbor_interp_op.cu
+210
-0
paddle/fluid/operators/nearest_neighbor_interp_op.h
paddle/fluid/operators/nearest_neighbor_interp_op.h
+130
-0
未找到文件。
paddle/fluid/operators/nearest_neighbor_interp_op.cc
0 → 100644
浏览文件 @
a24691a2
/* 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/fluid/operators/nearest_neighbor_interp_op.h"
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
NearestNeighborInterpOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of BilinearInterOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of BilinearInterOp should not be null."
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
// NCHW format
int
out_h
=
ctx
->
Attrs
().
Get
<
int
>
(
"out_h"
);
int
out_w
=
ctx
->
Attrs
().
Get
<
int
>
(
"out_w"
);
PADDLE_ENFORCE_EQ
(
dim_x
.
size
(),
4
,
"X's dimension must be 4"
);
if
(
ctx
->
HasInput
(
"OutSize"
))
{
auto
out_size_dim
=
ctx
->
GetInputDim
(
"OutSize"
);
PADDLE_ENFORCE_EQ
(
out_size_dim
.
size
(),
1
,
"OutSize's dimension size must be 1"
);
PADDLE_ENFORCE_EQ
(
out_size_dim
[
0
],
2
,
"OutSize's dim[0] must be 2"
);
}
std
::
vector
<
int64_t
>
dim_out
({
dim_x
[
0
],
dim_x
[
1
],
out_h
,
out_w
});
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
dim_out
));
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
GetPlace
());
}
};
class
NearestNeighborInterpOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"The input tensor of nearest neighbor interpolation, "
"This is a 4-D tensor with shape of (N x C x h x w)"
);
AddInput
(
"OutSize"
,
"This is a 1-D tensor with two number. "
"The first number is height and the second number is width."
)
.
AsDispensable
();
AddOutput
(
"Out"
,
"The dimension of output is (N x C x out_h x out_w)"
);
AddAttr
<
int
>
(
"out_h"
,
"output height of bilinear interpolation op."
);
AddAttr
<
int
>
(
"out_w"
,
"output width of bilinear interpolation op."
);
AddComment
(
R"DOC(
Nearest neighbor interpolation is to perform nearest neighbor interpolation
in bot the 3rd dimention(in height direction) and the 4th dimention(in width
direction) on input tensor.
For details, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
)DOC"
);
}
};
class
NearestNeighborInterpOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) should not be null"
);
auto
dim_x
=
ctx
->
GetInputDim
(
"X"
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
dim_x
);
}
}
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
ctx
.
GetPlace
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
nearest_neighbor_interp
,
ops
::
NearestNeighborInterpOp
,
ops
::
NearestNeighborInterpOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
nearest_neighbor_interp_grad
,
ops
::
NearestNeighborInterpOpGrad
);
REGISTER_OP_CPU_KERNEL
(
nearest_neighbor_interp
,
ops
::
NearestNeighborInterpKernel
<
float
>
,
ops
::
NearestNeighborInterpKernel
<
uint8_t
>
);
REGISTER_OP_CPU_KERNEL
(
nearest_neighbor_interp_grad
,
ops
::
NearestNeighborInterpGradKernel
<
float
>
);
paddle/fluid/operators/nearest_neighbor_interp_op.cu
0 → 100644
浏览文件 @
a24691a2
/* 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/fluid/operators/nearest_neighbor_interp_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
,
size_t
D
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenTensor
=
framework
::
EigenTensor
<
T
,
D
,
MajorType
,
IndexType
>
;
using
framework
::
Tensor
;
template
<
typename
T
>
__global__
void
KeBilinearInterpFw
(
const
T
*
in
,
const
size_t
in_img_h
,
const
size_t
in_img_w
,
const
size_t
input_h
,
const
size_t
input_w
,
T
*
out
,
const
size_t
out_img_h
,
const
size_t
out_img_w
,
const
size_t
output_h
,
const
size_t
output_w
,
const
size_t
num_channels
,
const
T
ratio_h
,
const
T
ratioW
)
{
int
nthreads
=
output_h
*
output_w
;
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
tid
<
nthreads
)
{
int
out_id_h
=
tid
/
output_w
;
int
out_id_w
=
tid
%
output_w
;
int
in_img_size
=
input_w
/
num_channels
;
int
out_img_size
=
output_w
/
num_channels
;
int
channel_id
=
out_id_w
/
out_img_size
;
int
out_img_idy
=
(
out_id_w
%
out_img_size
)
/
out_img_w
;
int
in_img_idy
=
ratio_h
*
out_img_idy
;
int
h_id
=
(
in_img_idy
<
in_img_h
-
1
)
?
1
:
0
;
T
h1lambda
=
ratio_h
*
out_img_idy
-
in_img_idy
;
T
h2lambda
=
1.
f
-
h1lambda
;
int
out_img_idx
=
tid
%
out_img_w
;
int
in_img_idx
=
ratioW
*
out_img_idx
;
int
w_id
=
(
in_img_idx
<
in_img_w
-
1
)
?
1
:
0
;
T
w1lambda
=
ratioW
*
out_img_idx
-
in_img_idx
;
T
w2lambda
=
1.
f
-
w1lambda
;
const
T
*
in_pos
=
&
in
[
out_id_h
*
input_w
+
channel_id
*
in_img_size
+
in_img_idy
*
in_img_w
+
in_img_idx
];
// bilinear interpolation
out
[
out_id_h
*
output_w
+
out_id_w
]
=
h2lambda
*
(
w2lambda
*
in_pos
[
0
]
+
w1lambda
*
in_pos
[
w_id
])
+
h1lambda
*
(
w2lambda
*
in_pos
[
h_id
*
in_img_w
]
+
w1lambda
*
in_pos
[
h_id
*
in_img_w
+
w_id
]);
}
}
template
<
typename
T
>
__global__
void
KeBilinearInterpBw
(
T
*
in
,
const
size_t
in_img_h
,
const
size_t
in_img_w
,
const
size_t
input_h
,
const
size_t
input_w
,
const
T
*
out
,
const
size_t
out_img_h
,
const
size_t
out_img_w
,
const
size_t
output_h
,
const
size_t
output_w
,
const
size_t
num_channels
,
const
T
ratio_h
,
const
T
ratioW
)
{
int
nthreads
=
output_h
*
output_w
;
int
tid
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
tid
<
nthreads
)
{
int
out_id_h
=
tid
/
output_w
;
int
out_id_w
=
tid
%
output_w
;
int
in_img_size
=
input_w
/
num_channels
;
int
out_img_size
=
output_w
/
num_channels
;
int
channel_id
=
out_id_w
/
out_img_size
;
int
out_img_idy
=
(
out_id_w
%
out_img_size
)
/
out_img_w
;
int
in_img_idy
=
ratio_h
*
out_img_idy
;
int
h_id
=
(
in_img_idy
<
in_img_h
-
1
)
?
1
:
0
;
T
h1lambda
=
ratio_h
*
out_img_idy
-
in_img_idy
;
T
h2lambda
=
1.
f
-
h1lambda
;
int
out_img_idx
=
tid
%
out_img_w
;
int
in_img_idx
=
ratioW
*
out_img_idx
;
int
w_id
=
(
in_img_idx
<
in_img_w
-
1
)
?
1
:
0
;
T
w1lambda
=
ratioW
*
out_img_idx
-
in_img_idx
;
T
w2lambda
=
1.
f
-
w1lambda
;
T
*
in_pos
=
&
in
[
out_id_h
*
input_w
+
channel_id
*
in_img_size
+
in_img_idy
*
in_img_w
+
in_img_idx
];
const
T
*
out_pos
=
&
out
[
out_id_h
*
output_w
+
out_id_w
];
atomicAdd
(
&
in_pos
[
0
],
h2lambda
*
w2lambda
*
out_pos
[
0
]);
atomicAdd
(
&
in_pos
[
w_id
],
h2lambda
*
w1lambda
*
out_pos
[
0
]);
atomicAdd
(
&
in_pos
[
h_id
*
in_img_w
],
h1lambda
*
w2lambda
*
out_pos
[
0
]);
atomicAdd
(
&
in_pos
[
h_id
*
in_img_w
+
w_id
],
h1lambda
*
w1lambda
*
out_pos
[
0
]);
}
}
template
<
typename
T
>
class
NearestNeighborInterpOpCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()),
"This kernel only runs on GPU device."
);
auto
*
input_t
=
ctx
.
Input
<
Tensor
>
(
"X"
);
// float tensor
auto
*
output_t
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
// float tensor
auto
*
input
=
input_t
->
data
<
T
>
();
int
out_h
=
ctx
.
Attr
<
int
>
(
"out_h"
);
int
out_w
=
ctx
.
Attr
<
int
>
(
"out_w"
);
auto
out_dims
=
output_t
->
dims
();
auto
out_size_t
=
ctx
.
Input
<
Tensor
>
(
"OutSize"
);
if
(
out_size_t
!=
nullptr
)
{
Tensor
sizes
;
framework
::
TensorCopy
(
*
out_size_t
,
platform
::
CPUPlace
(),
&
sizes
);
auto
size_data
=
sizes
.
data
<
int
>
();
out_h
=
size_data
[
0
];
out_w
=
size_data
[
1
];
}
auto
*
output
=
output_t
->
mutable_data
<
T
>
(
{
out_dims
[
0
],
out_dims
[
1
],
out_h
,
out_w
},
ctx
.
GetPlace
());
int
batch_size
=
input_t
->
dims
()[
0
];
int
channels
=
input_t
->
dims
()[
1
];
int
in_h
=
input_t
->
dims
()[
2
];
int
in_w
=
input_t
->
dims
()[
3
];
int
in_hw
=
in_h
*
in_w
;
int
out_hw
=
out_h
*
out_w
;
int
in_chw
=
channels
*
in_hw
;
int
out_chw
=
channels
*
out_hw
;
T
ratio_h
=
(
out_h
>
1
)
?
static_cast
<
T
>
(
in_h
-
1
)
/
(
out_h
-
1
)
:
0.
f
;
T
ratio_w
=
(
out_w
>
1
)
?
static_cast
<
T
>
(
in_w
-
1
)
/
(
out_w
-
1
)
:
0.
f
;
if
(
in_h
==
out_h
&&
in_w
==
out_w
)
{
memcpy
(
output
,
input
,
input_t
->
numel
()
*
sizeof
(
T
));
}
else
{
int
threadNum
=
batch_size
*
out_chw
;
int
blocks
=
(
threadNum
+
1024
-
1
)
/
1024
;
KeBilinearInterpFw
<
T
><<<
blocks
,
1024
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
input
,
in_h
,
in_w
,
batch_size
,
in_chw
,
output
,
out_h
,
out_w
,
batch_size
,
out_chw
,
channels
,
ratio_h
,
ratio_w
);
}
}
};
template
<
typename
T
>
class
NearestNeighborInterpGradOpCUDAKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
d_input_t
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
d_output_t
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_output
=
d_output_t
->
data
<
T
>
();
auto
*
d_input
=
d_input_t
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
auto
&
device_ctx
=
ctx
.
template
device_context
<
platform
::
CUDADeviceContext
>();
math
::
SetConstant
<
platform
::
CUDADeviceContext
,
T
>
zero
;
zero
(
device_ctx
,
d_input_t
,
static_cast
<
T
>
(
0.0
));
int
out_h
=
ctx
.
Attr
<
int
>
(
"out_h"
);
int
out_w
=
ctx
.
Attr
<
int
>
(
"out_w"
);
auto
out_size_t
=
ctx
.
Input
<
Tensor
>
(
"OutSize"
);
if
(
out_size_t
!=
nullptr
)
{
Tensor
sizes
;
framework
::
TensorCopy
(
*
out_size_t
,
platform
::
CPUPlace
(),
&
sizes
);
auto
size_data
=
sizes
.
data
<
int
>
();
out_h
=
size_data
[
0
];
out_w
=
size_data
[
1
];
}
int
batch_size
=
d_input_t
->
dims
()[
0
];
int
channels
=
d_input_t
->
dims
()[
1
];
int
in_h
=
d_input_t
->
dims
()[
2
];
int
in_w
=
d_input_t
->
dims
()[
3
];
int
in_hw
=
in_h
*
in_w
;
int
out_hw
=
out_h
*
out_w
;
int
in_chw
=
channels
*
in_hw
;
int
out_chw
=
channels
*
out_hw
;
T
ratio_h
=
(
out_h
>
1
)
?
static_cast
<
T
>
(
in_h
-
1
)
/
(
out_h
-
1
)
:
0.
f
;
T
ratio_w
=
(
out_w
>
1
)
?
static_cast
<
T
>
(
in_w
-
1
)
/
(
out_w
-
1
)
:
0.
f
;
if
(
in_h
==
out_h
&&
in_w
==
out_w
)
{
memcpy
(
d_input
,
d_output
,
d_input_t
->
numel
()
*
sizeof
(
T
));
}
else
{
int
threadNum
=
batch_size
*
out_chw
;
int
blocks
=
(
threadNum
+
1024
-
1
)
/
1024
;
KeBilinearInterpBw
<
T
><<<
blocks
,
1024
,
0
,
ctx
.
cuda_device_context
().
stream
()
>>>
(
d_input
,
in_h
,
in_w
,
batch_size
,
in_chw
,
d_output
,
out_h
,
out_w
,
batch_size
,
out_chw
,
channels
,
ratio_h
,
ratio_w
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
nearest_neighbor_interp
,
ops
::
NearestNeighborInterpOpCUDAKernel
<
float
>
);
REGISTER_OP_CUDA_KERNEL
(
nearest_neighborinterp_grad
,
ops
::
NearestNeighborInterpGradOpCUDAKernel
<
float
>
);
paddle/fluid/operators/nearest_neighbor_interp_op.h
0 → 100644
浏览文件 @
a24691a2
/* 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/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
,
size_t
D
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenTensor
=
framework
::
EigenTensor
<
T
,
D
,
MajorType
,
IndexType
>
;
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
NearestNeighborInterpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input
=
ctx
.
Input
<
Tensor
>
(
"X"
);
auto
*
output
=
ctx
.
Output
<
Tensor
>
(
"Out"
);
int
out_h
=
ctx
.
Attr
<
int
>
(
"out_h"
);
int
out_w
=
ctx
.
Attr
<
int
>
(
"out_w"
);
auto
out_size
=
ctx
.
Input
<
Tensor
>
(
"OutSize"
);
if
(
out_size
!=
nullptr
)
{
auto
out_size_data
=
out_size
->
data
<
int
>
();
out_h
=
out_size_data
[
0
];
out_w
=
out_size_data
[
1
];
}
const
int
in_n
=
input
->
dims
()[
0
];
const
int
in_c
=
input
->
dims
()[
1
];
const
int
in_h
=
input
->
dims
()[
2
];
const
int
in_w
=
input
->
dims
()[
3
];
output
->
mutable_data
<
T
>
({
in_n
,
in_c
,
out_h
,
out_w
},
ctx
.
GetPlace
());
auto
&
device_ctx
=
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>();
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
T
>
zero
;
zero
(
device_ctx
,
output
,
static_cast
<
T
>
(
0.0
));
if
(
in_h
==
out_h
&&
in_w
==
out_w
)
{
framework
::
TensorCopy
(
*
input
,
ctx
.
GetPlace
(),
output
);
return
;
}
float
ratio_h
=
(
out_h
>
1
)
?
static_cast
<
float
>
(
in_h
-
1
)
/
(
out_h
-
1
)
:
0.
f
;
float
ratio_w
=
(
out_w
>
1
)
?
static_cast
<
float
>
(
in_w
-
1
)
/
(
out_w
-
1
)
:
0.
f
;
auto
input_t
=
EigenTensor
<
T
,
4
>::
From
(
*
input
);
auto
output_t
=
EigenTensor
<
T
,
4
>::
From
(
*
output
);
for
(
int
k
=
0
;
k
<
out_h
;
k
++
)
{
// loop for images
for
(
int
l
=
0
;
l
<
out_w
;
l
++
)
{
int
in_k
=
static_cast
<
int
>
(
round
(
ratio_h
*
k
));
int
in_l
=
static_cast
<
int
>
(
round
(
ratio_w
*
l
));
for
(
int
i
=
0
;
i
<
in_n
;
i
++
)
{
// loop for batches
for
(
int
j
=
0
;
j
<
in_c
;
j
++
)
{
// loop for channels
output_t
(
i
,
j
,
k
,
l
)
=
input_t
(
i
,
j
,
in_k
,
in_l
);
}
}
}
}
}
};
template
<
typename
T
>
class
NearestNeighborInterpGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
auto
*
input_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
output_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
int
out_h
=
ctx
.
Attr
<
int
>
(
"out_h"
);
int
out_w
=
ctx
.
Attr
<
int
>
(
"out_w"
);
auto
out_size
=
ctx
.
Input
<
Tensor
>
(
"OutSize"
);
if
(
out_size
!=
nullptr
)
{
auto
out_size_data
=
out_size
->
data
<
int
>
();
out_h
=
out_size_data
[
0
];
out_w
=
out_size_data
[
1
];
}
const
int
in_n
=
input_grad
->
dims
()[
0
];
const
int
in_c
=
input_grad
->
dims
()[
1
];
const
int
in_h
=
input_grad
->
dims
()[
2
];
const
int
in_w
=
input_grad
->
dims
()[
3
];
auto
&
device_ctx
=
ctx
.
template
device_context
<
platform
::
CPUDeviceContext
>();
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
T
>
zero
;
zero
(
device_ctx
,
input_grad
,
static_cast
<
T
>
(
0.0
));
if
(
in_h
==
out_h
&&
in_w
==
out_w
)
{
framework
::
TensorCopy
(
*
output_grad
,
ctx
.
GetPlace
(),
input_grad
);
return
;
}
float
ratio_h
=
(
out_h
>
1
)
?
static_cast
<
float
>
(
in_h
-
1
)
/
(
out_h
-
1
)
:
0.
f
;
float
ratio_w
=
(
out_w
>
1
)
?
static_cast
<
float
>
(
in_w
-
1
)
/
(
out_w
-
1
)
:
0.
f
;
auto
input_grad_t
=
EigenTensor
<
T
,
4
>::
From
(
*
input_grad
);
auto
output_grad_t
=
EigenTensor
<
T
,
4
>::
From
(
*
output_grad
);
for
(
int
k
=
0
;
k
<
out_h
;
k
++
)
{
// loop for images
for
(
int
l
=
0
;
l
<
out_w
;
l
++
)
{
int
in_k
=
static_cast
<
int
>
(
round
(
ratio_h
*
k
));
int
in_l
=
static_cast
<
int
>
(
round
(
ratio_w
*
l
));
for
(
int
i
=
0
;
i
<
in_n
;
i
++
)
{
// loop for batches
for
(
int
j
=
0
;
j
<
in_c
;
j
++
)
{
// loop for channels
input_grad_t
(
i
,
j
,
in_k
,
in_l
)
+=
output_grad_t
(
i
,
j
,
k
,
l
);
}
}
}
}
}
};
}
// namespace operators
}
// namespace paddle
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