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体验新版 GitCode,发现更多精彩内容 >>
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feaf1e2d
编写于
11月 14, 2017
作者:
C
chengduo
提交者:
GitHub
11月 14, 2017
浏览文件
操作
浏览文件
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差异文件
Merge pull request #5608 from chengduoZH/fix_pooling_function_parameter_order
fix pooling functor parameter order
上级
d7319c22
21604977
变更
5
展开全部
显示空白变更内容
内联
并排
Showing
5 changed file
with
278 addition
and
272 deletion
+278
-272
paddle/operators/math/pooling.cc
paddle/operators/math/pooling.cc
+61
-57
paddle/operators/math/pooling.cu
paddle/operators/math/pooling.cu
+167
-167
paddle/operators/math/pooling.h
paddle/operators/math/pooling.h
+26
-24
paddle/operators/pool_op.h
paddle/operators/pool_op.h
+16
-16
paddle/operators/pool_with_index_op.h
paddle/operators/pool_with_index_op.h
+8
-8
未找到文件。
paddle/operators/math/pooling.cc
浏览文件 @
feaf1e2d
...
@@ -27,15 +27,15 @@ template <typename PoolProcess, typename T>
...
@@ -27,15 +27,15 @@ template <typename PoolProcess, typename T>
class
Pool2dFunctor
<
platform
::
CPUPlace
,
PoolProcess
,
T
>
{
class
Pool2dFunctor
<
platform
::
CPUPlace
,
PoolProcess
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
input
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
stride
s
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
padding
s
,
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_process
)
{
PoolProcess
pool_process
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
const
int
input_width
=
input
.
dims
()[
3
];
const
int
output_channels
=
output
.
dims
()[
1
];
const
int
output_channels
=
output
->
dims
()[
1
];
const
int
output_height
=
output
.
dims
()[
2
];
const
int
output_height
=
output
->
dims
()[
2
];
const
int
output_width
=
output
.
dims
()[
3
];
const
int
output_width
=
output
->
dims
()[
3
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_width
=
ksize
[
1
];
const
int
ksize_width
=
ksize
[
1
];
const
int
stride_height
=
strides
[
0
];
const
int
stride_height
=
strides
[
0
];
...
@@ -47,7 +47,7 @@ class Pool2dFunctor<platform::CPUPlace, PoolProcess, T> {
...
@@ -47,7 +47,7 @@ class Pool2dFunctor<platform::CPUPlace, PoolProcess, T> {
const
int
output_stride
=
output_height
*
output_width
;
const
int
output_stride
=
output_height
*
output_width
;
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -87,11 +87,12 @@ template <typename PoolProcess, class T>
...
@@ -87,11 +87,12 @@ template <typename PoolProcess, class T>
class
Pool2dGradFunctor
<
platform
::
CPUPlace
,
PoolProcess
,
T
>
{
class
Pool2dGradFunctor
<
platform
::
CPUPlace
,
PoolProcess
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_grad_process
)
{
PoolProcess
pool_grad_process
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
const
int
input_width
=
input
.
dims
()[
3
];
...
@@ -110,7 +111,7 @@ class Pool2dGradFunctor<platform::CPUPlace, PoolProcess, T> {
...
@@ -110,7 +111,7 @@ class Pool2dGradFunctor<platform::CPUPlace, PoolProcess, T> {
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
output_data
=
output
.
data
<
T
>
();
const
T
*
output_data
=
output
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -154,10 +155,11 @@ template <class T>
...
@@ -154,10 +155,11 @@ template <class T>
class
MaxPool2dGradFunctor
<
platform
::
CPUPlace
,
T
>
{
class
MaxPool2dGradFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
const
int
input_width
=
input
.
dims
()[
3
];
...
@@ -176,7 +178,7 @@ class MaxPool2dGradFunctor<platform::CPUPlace, T> {
...
@@ -176,7 +178,7 @@ class MaxPool2dGradFunctor<platform::CPUPlace, T> {
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
output_data
=
output
.
data
<
T
>
();
const
T
*
output_data
=
output
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -240,17 +242,17 @@ template <typename PoolProcess, class T>
...
@@ -240,17 +242,17 @@ template <typename PoolProcess, class T>
class
Pool3dFunctor
<
platform
::
CPUPlace
,
PoolProcess
,
T
>
{
class
Pool3dFunctor
<
platform
::
CPUPlace
,
PoolProcess
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
input
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
stride
s
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
padding
s
,
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_process
)
{
PoolProcess
pool_process
,
framework
::
Tensor
*
output
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
const
int
input_height
=
input
.
dims
()[
3
];
const
int
input_width
=
input
.
dims
()[
4
];
const
int
input_width
=
input
.
dims
()[
4
];
const
int
output_channels
=
output
.
dims
()[
1
];
const
int
output_channels
=
output
->
dims
()[
1
];
const
int
output_depth
=
output
.
dims
()[
2
];
const
int
output_depth
=
output
->
dims
()[
2
];
const
int
output_height
=
output
.
dims
()[
3
];
const
int
output_height
=
output
->
dims
()[
3
];
const
int
output_width
=
output
.
dims
()[
4
];
const
int
output_width
=
output
->
dims
()[
4
];
const
int
ksize_depth
=
ksize
[
0
];
const
int
ksize_depth
=
ksize
[
0
];
const
int
ksize_height
=
ksize
[
1
];
const
int
ksize_height
=
ksize
[
1
];
const
int
ksize_width
=
ksize
[
2
];
const
int
ksize_width
=
ksize
[
2
];
...
@@ -265,7 +267,7 @@ class Pool3dFunctor<platform::CPUPlace, PoolProcess, T> {
...
@@ -265,7 +267,7 @@ class Pool3dFunctor<platform::CPUPlace, PoolProcess, T> {
const
int
output_stride
=
output_depth
*
output_height
*
output_width
;
const
int
output_stride
=
output_depth
*
output_height
*
output_width
;
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -315,11 +317,12 @@ template <typename PoolProcess, class T>
...
@@ -315,11 +317,12 @@ template <typename PoolProcess, class T>
class
Pool3dGradFunctor
<
platform
::
CPUPlace
,
PoolProcess
,
T
>
{
class
Pool3dGradFunctor
<
platform
::
CPUPlace
,
PoolProcess
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_grad_process
)
{
PoolProcess
pool_grad_process
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
const
int
input_height
=
input
.
dims
()[
3
];
...
@@ -343,7 +346,7 @@ class Pool3dGradFunctor<platform::CPUPlace, PoolProcess, T> {
...
@@ -343,7 +346,7 @@ class Pool3dGradFunctor<platform::CPUPlace, PoolProcess, T> {
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
output_data
=
output
.
data
<
T
>
();
const
T
*
output_data
=
output
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -398,10 +401,11 @@ template <class T>
...
@@ -398,10 +401,11 @@ template <class T>
class
MaxPool3dGradFunctor
<
platform
::
CPUPlace
,
T
>
{
class
MaxPool3dGradFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
input_grad
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
const
int
input_height
=
input
.
dims
()[
3
];
...
@@ -425,7 +429,7 @@ class MaxPool3dGradFunctor<platform::CPUPlace, T> {
...
@@ -425,7 +429,7 @@ class MaxPool3dGradFunctor<platform::CPUPlace, T> {
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
output_data
=
output
.
data
<
T
>
();
const
T
*
output_data
=
output
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -498,15 +502,15 @@ template <typename T>
...
@@ -498,15 +502,15 @@ template <typename T>
class
MaxPool2dWithIndexFunctor
<
platform
::
CPUPlace
,
T
>
{
class
MaxPool2dWithIndexFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
input
,
std
::
vector
<
int
>&
ksize
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
const
int
input_width
=
input
.
dims
()[
3
];
const
int
output_channels
=
output
.
dims
()[
1
];
const
int
output_channels
=
output
->
dims
()[
1
];
const
int
output_height
=
output
.
dims
()[
2
];
const
int
output_height
=
output
->
dims
()[
2
];
const
int
output_width
=
output
.
dims
()[
3
];
const
int
output_width
=
output
->
dims
()[
3
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_width
=
ksize
[
1
];
const
int
ksize_width
=
ksize
[
1
];
const
int
stride_height
=
strides
[
0
];
const
int
stride_height
=
strides
[
0
];
...
@@ -517,8 +521,8 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
...
@@ -517,8 +521,8 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
const
int
output_stride
=
output_height
*
output_width
;
const
int
output_stride
=
output_height
*
output_width
;
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
mask_data
=
mask
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
mask_data
=
mask
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -563,13 +567,13 @@ template <typename T>
...
@@ -563,13 +567,13 @@ template <typename T>
class
MaxPool2dWithIndexGradFunctor
<
platform
::
CPUPlace
,
T
>
{
class
MaxPool2dWithIndexGradFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
const
int
batch_size
=
input_grad
.
dims
()[
0
];
framework
::
Tensor
*
input_grad
)
{
const
int
input_height
=
input_grad
.
dims
()[
2
];
const
int
batch_size
=
input_grad
->
dims
()[
0
];
const
int
input_width
=
input_grad
.
dims
()[
3
];
const
int
input_height
=
input_grad
->
dims
()[
2
];
const
int
input_width
=
input_grad
->
dims
()[
3
];
const
int
output_channels
=
output_grad
.
dims
()[
1
];
const
int
output_channels
=
output_grad
.
dims
()[
1
];
const
int
output_height
=
output_grad
.
dims
()[
2
];
const
int
output_height
=
output_grad
.
dims
()[
2
];
const
int
output_width
=
output_grad
.
dims
()[
3
];
const
int
output_width
=
output_grad
.
dims
()[
3
];
...
@@ -578,7 +582,7 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
...
@@ -578,7 +582,7 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
const
T
*
mask_data
=
mask
.
data
<
T
>
();
const
T
*
mask_data
=
mask
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -612,17 +616,17 @@ template <typename T>
...
@@ -612,17 +616,17 @@ template <typename T>
class
MaxPool3dWithIndexFunctor
<
platform
::
CPUPlace
,
T
>
{
class
MaxPool3dWithIndexFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
input
,
std
::
vector
<
int
>&
ksize
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
const
int
input_height
=
input
.
dims
()[
3
];
const
int
input_width
=
input
.
dims
()[
4
];
const
int
input_width
=
input
.
dims
()[
4
];
const
int
output_channels
=
output
.
dims
()[
1
];
const
int
output_channels
=
output
->
dims
()[
1
];
const
int
output_depth
=
output
.
dims
()[
2
];
const
int
output_depth
=
output
->
dims
()[
2
];
const
int
output_height
=
output
.
dims
()[
3
];
const
int
output_height
=
output
->
dims
()[
3
];
const
int
output_width
=
output
.
dims
()[
4
];
const
int
output_width
=
output
->
dims
()[
4
];
const
int
ksize_depth
=
ksize
[
0
];
const
int
ksize_depth
=
ksize
[
0
];
const
int
ksize_height
=
ksize
[
1
];
const
int
ksize_height
=
ksize
[
1
];
const
int
ksize_width
=
ksize
[
2
];
const
int
ksize_width
=
ksize
[
2
];
...
@@ -636,8 +640,8 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
...
@@ -636,8 +640,8 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
const
int
output_stride
=
output_depth
*
output_height
*
output_width
;
const
int
output_stride
=
output_depth
*
output_height
*
output_width
;
const
T
*
input_data
=
input
.
data
<
T
>
();
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
output_data
=
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
mask_data
=
mask
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
mask_data
=
mask
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
@@ -691,14 +695,14 @@ template <typename T>
...
@@ -691,14 +695,14 @@ template <typename T>
class
MaxPool3dWithIndexGradFunctor
<
platform
::
CPUPlace
,
T
>
{
class
MaxPool3dWithIndexGradFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
const
int
batch_size
=
input_grad
.
dims
()[
0
];
framework
::
Tensor
*
input_grad
)
{
const
int
input_depth
=
input_grad
.
dims
()[
2
];
const
int
batch_size
=
input_grad
->
dims
()[
0
];
const
int
input_height
=
input_grad
.
dims
()[
3
];
const
int
input_depth
=
input_grad
->
dims
()[
2
];
const
int
input_width
=
input_grad
.
dims
()[
4
];
const
int
input_height
=
input_grad
->
dims
()[
3
];
const
int
input_width
=
input_grad
->
dims
()[
4
];
const
int
output_channels
=
output_grad
.
dims
()[
1
];
const
int
output_channels
=
output_grad
.
dims
()[
1
];
const
int
output_depth
=
output_grad
.
dims
()[
2
];
const
int
output_depth
=
output_grad
.
dims
()[
2
];
const
int
output_height
=
output_grad
.
dims
()[
3
];
const
int
output_height
=
output_grad
.
dims
()[
3
];
...
@@ -708,7 +712,7 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
...
@@ -708,7 +712,7 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
const
T
*
mask_data
=
mask
.
data
<
T
>
();
const
T
*
mask_data
=
mask
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
int
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
...
...
paddle/operators/math/pooling.cu
浏览文件 @
feaf1e2d
此差异已折叠。
点击以展开。
paddle/operators/math/pooling.h
浏览文件 @
feaf1e2d
...
@@ -88,60 +88,62 @@ template <typename Place, typename PoolProcess, typename T>
...
@@ -88,60 +88,62 @@ template <typename Place, typename PoolProcess, typename T>
class
Pool2dFunctor
{
class
Pool2dFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
input
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
stride
s
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
padding
s
,
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
);
PoolProcess
pool_compute
,
framework
::
Tensor
*
output
);
};
};
template
<
typename
Place
,
typename
PoolProcess
,
typename
T
>
template
<
typename
Place
,
typename
PoolProcess
,
typename
T
>
class
Pool2dGradFunctor
{
class
Pool2dGradFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
);
PoolProcess
pool_compute
,
framework
::
Tensor
*
input_grad
);
};
};
template
<
typename
Place
,
class
T
>
template
<
typename
Place
,
class
T
>
class
MaxPool2dGradFunctor
{
class
MaxPool2dGradFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
input_grad
);
};
};
template
<
typename
Place
,
typename
PoolProcess
,
typename
T
>
template
<
typename
Place
,
typename
PoolProcess
,
typename
T
>
class
Pool3dFunctor
{
class
Pool3dFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
input
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
stride
s
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
padding
s
,
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
);
PoolProcess
pool_compute
,
framework
::
Tensor
*
output
);
};
};
template
<
typename
Place
,
typename
PoolProcess
,
typename
T
>
template
<
typename
Place
,
typename
PoolProcess
,
typename
T
>
class
Pool3dGradFunctor
{
class
Pool3dGradFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
PoolProcess
pool_compute
);
PoolProcess
pool_compute
,
framework
::
Tensor
*
input_grad
);
};
};
template
<
typename
Place
,
class
T
>
template
<
typename
Place
,
class
T
>
class
MaxPool3dGradFunctor
{
class
MaxPool3dGradFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
input
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
output_grad
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
input_grad
);
};
};
/*
/*
...
@@ -155,38 +157,38 @@ template <typename Place, typename T>
...
@@ -155,38 +157,38 @@ template <typename Place, typename T>
class
MaxPool2dWithIndexFunctor
{
class
MaxPool2dWithIndexFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
input
,
std
::
vector
<
int
>&
ksize
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
};
};
template
<
typename
Place
,
typename
T
>
template
<
typename
Place
,
typename
T
>
class
MaxPool2dWithIndexGradFunctor
{
class
MaxPool2dWithIndexGradFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
input_grad
);
};
};
template
<
typename
Place
,
typename
T
>
template
<
typename
Place
,
typename
T
>
class
MaxPool3dWithIndexFunctor
{
class
MaxPool3dWithIndexFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
const
framework
::
Tensor
&
input
,
std
::
vector
<
int
>&
ksize
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
framework
::
Tensor
*
output
,
framework
::
Tensor
*
mask
);
};
};
template
<
typename
Place
,
typename
T
>
template
<
typename
Place
,
typename
T
>
class
MaxPool3dWithIndexGradFunctor
{
class
MaxPool3dWithIndexGradFunctor
{
public:
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
framework
::
Tensor
*
input_grad
);
};
};
}
// namespace math
}
// namespace math
...
...
paddle/operators/pool_op.h
浏览文件 @
feaf1e2d
...
@@ -75,16 +75,16 @@ class PoolKernel : public framework::OpKernel<T> {
...
@@ -75,16 +75,16 @@ class PoolKernel : public framework::OpKernel<T> {
Place
,
paddle
::
operators
::
math
::
MaxPool
<
T
>
,
T
>
Place
,
paddle
::
operators
::
math
::
MaxPool
<
T
>
,
T
>
pool2d_forward
;
pool2d_forward
;
paddle
::
operators
::
math
::
MaxPool
<
T
>
pool_process
;
paddle
::
operators
::
math
::
MaxPool
<
T
>
pool_process
;
pool2d_forward
(
context
.
device_context
(),
*
in_x
,
*
out
,
ksize
,
strides
,
pool2d_forward
(
context
.
device_context
(),
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
);
paddings
,
pool_process
,
out
);
}
else
if
(
pooling_type
==
"avg"
)
{
}
else
if
(
pooling_type
==
"avg"
)
{
paddle
::
operators
::
math
::
Pool2dFunctor
<
paddle
::
operators
::
math
::
Pool2dFunctor
<
Place
,
paddle
::
operators
::
math
::
AvgPool
<
T
>
,
T
>
Place
,
paddle
::
operators
::
math
::
AvgPool
<
T
>
,
T
>
pool2d_forward
;
pool2d_forward
;
paddle
::
operators
::
math
::
AvgPool
<
T
>
pool_process
;
paddle
::
operators
::
math
::
AvgPool
<
T
>
pool_process
;
pool2d_forward
(
context
.
device_context
(),
*
in_x
,
*
out
,
ksize
,
strides
,
pool2d_forward
(
context
.
device_context
(),
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
);
paddings
,
pool_process
,
out
);
}
}
}
break
;
}
break
;
case
3
:
{
case
3
:
{
...
@@ -93,15 +93,15 @@ class PoolKernel : public framework::OpKernel<T> {
...
@@ -93,15 +93,15 @@ class PoolKernel : public framework::OpKernel<T> {
Place
,
paddle
::
operators
::
math
::
MaxPool
<
T
>
,
T
>
Place
,
paddle
::
operators
::
math
::
MaxPool
<
T
>
,
T
>
pool3d_forward
;
pool3d_forward
;
paddle
::
operators
::
math
::
MaxPool
<
T
>
pool_process
;
paddle
::
operators
::
math
::
MaxPool
<
T
>
pool_process
;
pool3d_forward
(
context
.
device_context
(),
*
in_x
,
*
out
,
ksize
,
strides
,
pool3d_forward
(
context
.
device_context
(),
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
);
paddings
,
pool_process
,
out
);
}
else
if
(
pooling_type
==
"avg"
)
{
}
else
if
(
pooling_type
==
"avg"
)
{
paddle
::
operators
::
math
::
Pool3dFunctor
<
paddle
::
operators
::
math
::
Pool3dFunctor
<
Place
,
paddle
::
operators
::
math
::
AvgPool
<
T
>
,
T
>
Place
,
paddle
::
operators
::
math
::
AvgPool
<
T
>
,
T
>
pool3d_forward
;
pool3d_forward
;
paddle
::
operators
::
math
::
AvgPool
<
T
>
pool_process
;
paddle
::
operators
::
math
::
AvgPool
<
T
>
pool_process
;
pool3d_forward
(
context
.
device_context
(),
*
in_x
,
*
out
,
ksize
,
strides
,
pool3d_forward
(
context
.
device_context
(),
*
in_x
,
ksize
,
strides
,
paddings
,
pool_process
);
paddings
,
pool_process
,
out
);
}
}
}
break
;
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
...
@@ -142,30 +142,30 @@ class PoolGradKernel : public framework::OpKernel<T> {
...
@@ -142,30 +142,30 @@ class PoolGradKernel : public framework::OpKernel<T> {
if
(
pooling_type
==
"max"
)
{
if
(
pooling_type
==
"max"
)
{
paddle
::
operators
::
math
::
MaxPool2dGradFunctor
<
Place
,
T
>
paddle
::
operators
::
math
::
MaxPool2dGradFunctor
<
Place
,
T
>
pool2d_backward
;
pool2d_backward
;
pool2d_backward
(
context
.
device_context
(),
*
in_x
,
*
in_x_grad
,
*
out
,
pool2d_backward
(
context
.
device_context
(),
*
in_x
,
*
out
,
*
out_grad
,
*
out_grad
,
ksize
,
strides
,
paddings
);
ksize
,
strides
,
paddings
,
in_x_grad
);
}
else
if
(
pooling_type
==
"avg"
)
{
}
else
if
(
pooling_type
==
"avg"
)
{
paddle
::
operators
::
math
::
Pool2dGradFunctor
<
paddle
::
operators
::
math
::
Pool2dGradFunctor
<
Place
,
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
,
T
>
Place
,
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
,
T
>
pool2d_backward
;
pool2d_backward
;
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
pool_process
;
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
pool_process
;
pool2d_backward
(
context
.
device_context
(),
*
in_x
,
*
in_x_grad
,
*
out
,
pool2d_backward
(
context
.
device_context
(),
*
in_x
,
*
out
,
*
out_grad
,
*
out_grad
,
ksize
,
strides
,
paddings
,
pool_process
);
ksize
,
strides
,
paddings
,
pool_process
,
in_x_grad
);
}
}
}
break
;
}
break
;
case
3
:
{
case
3
:
{
if
(
pooling_type
==
"max"
)
{
if
(
pooling_type
==
"max"
)
{
paddle
::
operators
::
math
::
MaxPool3dGradFunctor
<
Place
,
T
>
paddle
::
operators
::
math
::
MaxPool3dGradFunctor
<
Place
,
T
>
pool3d_backward
;
pool3d_backward
;
pool3d_backward
(
context
.
device_context
(),
*
in_x
,
*
in_x_grad
,
*
out
,
pool3d_backward
(
context
.
device_context
(),
*
in_x
,
*
out
,
*
out_grad
,
*
out_grad
,
ksize
,
strides
,
paddings
);
ksize
,
strides
,
paddings
,
in_x_grad
);
}
else
if
(
pooling_type
==
"avg"
)
{
}
else
if
(
pooling_type
==
"avg"
)
{
paddle
::
operators
::
math
::
Pool3dGradFunctor
<
paddle
::
operators
::
math
::
Pool3dGradFunctor
<
Place
,
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
,
T
>
Place
,
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
,
T
>
pool3d_backward
;
pool3d_backward
;
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
pool_process
;
paddle
::
operators
::
math
::
AvgPoolGrad
<
T
>
pool_process
;
pool3d_backward
(
context
.
device_context
(),
*
in_x
,
*
in_x_grad
,
*
out
,
pool3d_backward
(
context
.
device_context
(),
*
in_x
,
*
out
,
*
out_grad
,
*
out_grad
,
ksize
,
strides
,
paddings
,
pool_process
);
ksize
,
strides
,
paddings
,
pool_process
,
in_x_grad
);
}
}
}
break
;
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
...
...
paddle/operators/pool_with_index_op.h
浏览文件 @
feaf1e2d
...
@@ -46,14 +46,14 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
...
@@ -46,14 +46,14 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
case
2
:
{
case
2
:
{
paddle
::
operators
::
math
::
MaxPool2dWithIndexFunctor
<
Place
,
T
>
paddle
::
operators
::
math
::
MaxPool2dWithIndexFunctor
<
Place
,
T
>
pool2d_forward
;
pool2d_forward
;
pool2d_forward
(
context
.
device_context
(),
*
in_x
,
*
out
,
*
mask
,
ksize
,
pool2d_forward
(
context
.
device_context
(),
*
in_x
,
ksize
,
strides
,
strides
,
paddings
);
paddings
,
out
,
mask
);
}
break
;
}
break
;
case
3
:
{
case
3
:
{
paddle
::
operators
::
math
::
MaxPool3dWithIndexFunctor
<
Place
,
T
>
paddle
::
operators
::
math
::
MaxPool3dWithIndexFunctor
<
Place
,
T
>
pool3d_forward
;
pool3d_forward
;
pool3d_forward
(
context
.
device_context
(),
*
in_x
,
*
out
,
*
mask
,
ksize
,
pool3d_forward
(
context
.
device_context
(),
*
in_x
,
ksize
,
strides
,
strides
,
paddings
);
paddings
,
out
,
mask
);
}
break
;
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
}
}
...
@@ -89,14 +89,14 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
...
@@ -89,14 +89,14 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
case
2
:
{
case
2
:
{
paddle
::
operators
::
math
::
MaxPool2dWithIndexGradFunctor
<
Place
,
T
>
paddle
::
operators
::
math
::
MaxPool2dWithIndexGradFunctor
<
Place
,
T
>
pool2d_backward
;
pool2d_backward
;
pool2d_backward
(
context
.
device_context
(),
*
in_x_grad
,
*
out_grad
,
pool2d_backward
(
context
.
device_context
(),
*
out_grad
,
*
mask
,
ksize
,
*
mask
,
ksize
,
strides
,
paddings
);
strides
,
paddings
,
in_x_grad
);
}
break
;
}
break
;
case
3
:
{
case
3
:
{
paddle
::
operators
::
math
::
MaxPool3dWithIndexGradFunctor
<
Place
,
T
>
paddle
::
operators
::
math
::
MaxPool3dWithIndexGradFunctor
<
Place
,
T
>
pool3d_backward
;
pool3d_backward
;
pool3d_backward
(
context
.
device_context
(),
*
in_x_grad
,
*
out_grad
,
pool3d_backward
(
context
.
device_context
(),
*
out_grad
,
*
mask
,
ksize
,
*
mask
,
ksize
,
strides
,
paddings
);
strides
,
paddings
,
in_x_grad
);
}
break
;
}
break
;
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
default:
{
PADDLE_THROW
(
"Pool op only supports 2D and 3D input."
);
}
}
}
...
...
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