Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
6326c40d
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
大约 1 年 前同步成功
通知
694
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
6326c40d
编写于
9月 28, 2017
作者:
C
chengduoZH
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add max pool with index
上级
38bca7d3
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
1171 addition
and
2 deletion
+1171
-2
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+6
-0
paddle/operators/math/CMakeLists.txt
paddle/operators/math/CMakeLists.txt
+2
-2
paddle/operators/math/pooling.cc
paddle/operators/math/pooling.cc
+255
-0
paddle/operators/math/pooling.cu
paddle/operators/math/pooling.cu
+387
-0
paddle/operators/math/pooling.h
paddle/operators/math/pooling.h
+68
-0
paddle/operators/pool_with_index_op.cc
paddle/operators/pool_with_index_op.cc
+198
-0
paddle/operators/pool_with_index_op.cu
paddle/operators/pool_with_index_op.cu
+31
-0
paddle/operators/pool_with_index_op.h
paddle/operators/pool_with_index_op.h
+99
-0
python/paddle/v2/framework/tests/test_pool_max_op.py
python/paddle/v2/framework/tests/test_pool_max_op.py
+125
-0
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
6326c40d
...
@@ -62,6 +62,12 @@ function(op_library TARGET)
...
@@ -62,6 +62,12 @@ function(op_library TARGET)
file
(
APPEND
${
pybind_file
}
"USE_OP(sigmoid);
\n
"
)
file
(
APPEND
${
pybind_file
}
"USE_OP(sigmoid);
\n
"
)
endif
()
endif
()
if
(
"
${
TARGET
}
"
STREQUAL
"pool_with_index_op"
)
set
(
pybind_flag 1
)
# It's enough to just adding one operator to pybind
file
(
APPEND
${
pybind_file
}
"USE_OP(maxPool2dWithIndex);
\n
"
)
endif
()
# pybind USE_NO_KERNEL_OP
# pybind USE_NO_KERNEL_OP
file
(
READ
${
TARGET
}
.cc TARGET_CONTENT
)
file
(
READ
${
TARGET
}
.cc TARGET_CONTENT
)
string
(
REGEX MATCH
"OperatorWithKernel"
regex_result
"
${
TARGET_CONTENT
}
"
)
string
(
REGEX MATCH
"OperatorWithKernel"
regex_result
"
${
TARGET_CONTENT
}
"
)
...
...
paddle/operators/math/CMakeLists.txt
浏览文件 @
6326c40d
if
(
WITH_GPU
)
if
(
WITH_GPU
)
nv_library
(
math_function SRCS math_function.cc math_function.cu im2col.cc
nv_library
(
math_function SRCS math_function.cc math_function.cu im2col.cc
im2col.cu DEPS cblas device_context operator
)
im2col.cu
pooling.cc pooling.cu
DEPS cblas device_context operator
)
nv_library
(
softmax_function SRCS softmax.cc softmax.cu
nv_library
(
softmax_function SRCS softmax.cc softmax.cu
DEPS operator
)
DEPS operator
)
nv_library
(
cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu
nv_library
(
cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu
DEPS operator
)
DEPS operator
)
else
()
else
()
cc_library
(
math_function SRCS math_function.cc im2col.cc
cc_library
(
math_function SRCS math_function.cc im2col.cc
pooling.cc
DEPS cblas device_context operator
)
DEPS cblas device_context operator
)
cc_library
(
softmax_function SRCS softmax.cc DEPS operator
)
cc_library
(
softmax_function SRCS softmax.cc DEPS operator
)
cc_library
(
cross_entropy_function SRCS cross_entropy.cc DEPS operator
)
cc_library
(
cross_entropy_function SRCS cross_entropy.cc DEPS operator
)
...
...
paddle/operators/math/pooling.cc
0 → 100644
浏览文件 @
6326c40d
/* 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/operators/math/pooling.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
T
>
class
MaxPool2dWithIndexFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
const
int
output_channels
=
output
.
dims
()[
1
];
const
int
output_height
=
output
.
dims
()[
2
];
const
int
output_width
=
output
.
dims
()[
3
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_width
=
ksize
[
1
];
const
int
stride_height
=
strides
[
0
];
const
int
stride_width
=
strides
[
1
];
const
int
padding_height
=
paddings
[
0
];
const
int
padding_width
=
paddings
[
1
];
const
int
input_stride
=
input_height
*
input_width
;
const
int
output_stride
=
output_height
*
output_width
;
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
mask_data
=
mask
.
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
T
ele
=
static_cast
<
T
>
(
-
FLT_MAX
);
int
index
=
-
1
;
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
int
w
=
wstart
;
w
<
wend
;
++
w
)
{
if
(
ele
<
input_data
[
h
*
input_width
+
w
])
{
ele
=
input_data
[
h
*
input_width
+
w
];
index
=
h
*
input_width
+
w
;
}
}
}
output_data
[
ph
*
output_width
+
pw
]
=
ele
;
mask_data
[
ph
*
output_width
+
pw
]
=
index
;
}
}
// offset
input_data
+=
input_stride
;
output_data
+=
output_stride
;
mask_data
+=
output_stride
;
}
}
}
};
template
<
typename
T
>
class
MaxPool2dWithIndexGradFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
const
int
batch_size
=
input_grad
.
dims
()[
0
];
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_height
=
output_grad
.
dims
()[
2
];
const
int
output_width
=
output_grad
.
dims
()[
3
];
const
int
input_stride
=
input_height
*
input_width
;
const
int
output_stride
=
output_height
*
output_width
;
const
T
*
mask_data
=
mask
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
size_t
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
size_t
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
size_t
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
for
(
size_t
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
const
size_t
output_idx
=
ph
*
output_width
+
pw
;
const
size_t
input_idx
=
static_cast
<
size_t
>
(
mask_data
[
output_idx
]);
input_grad_data
[
input_idx
]
+=
output_grad_data
[
output_idx
];
}
}
}
// offset
input_grad_data
+=
input_stride
;
output_grad_data
+=
output_stride
;
mask_data
+=
output_stride
;
}
}
};
template
class
MaxPool2dWithIndexFunctor
<
platform
::
CPUPlace
,
float
>;
template
class
MaxPool2dWithIndexGradFunctor
<
platform
::
CPUPlace
,
float
>;
template
class
MaxPool2dWithIndexFunctor
<
platform
::
CPUPlace
,
double
>;
template
class
MaxPool2dWithIndexGradFunctor
<
platform
::
CPUPlace
,
double
>;
template
<
typename
T
>
class
MaxPool3dWithIndexFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
const
int
input_width
=
input
.
dims
()[
4
];
const
int
output_channels
=
output
.
dims
()[
1
];
const
int
output_depth
=
output
.
dims
()[
2
];
const
int
output_height
=
output
.
dims
()[
3
];
const
int
output_width
=
output
.
dims
()[
4
];
const
int
ksize_depth
=
ksize
[
0
];
const
int
ksize_height
=
ksize
[
1
];
const
int
ksize_width
=
ksize
[
2
];
const
int
stride_depth
=
strides
[
0
];
const
int
stride_height
=
strides
[
1
];
const
int
stride_width
=
strides
[
2
];
const
int
padding_depth
=
paddings
[
0
];
const
int
padding_height
=
paddings
[
1
];
const
int
padding_width
=
paddings
[
2
];
const
int
input_stride
=
input_depth
*
input_height
*
input_width
;
const
int
output_stride
=
output_depth
*
output_height
*
output_width
;
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
mask_data
=
mask
.
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
for
(
int
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
int
pd
=
0
;
pd
<
output_depth
;
++
pd
)
{
int
dstart
=
pd
*
stride_depth
-
padding_depth
;
int
dend
=
std
::
min
(
dstart
+
ksize_depth
,
input_depth
);
dstart
=
std
::
max
(
dstart
,
0
);
for
(
int
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
std
::
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
std
::
max
(
hstart
,
0
);
for
(
int
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
std
::
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
std
::
max
(
wstart
,
0
);
int
output_idx
=
(
pd
*
output_height
+
ph
)
*
output_width
+
pw
;
T
ele
=
static_cast
<
T
>
(
-
FLT_MAX
);
int
index
=
-
1
;
for
(
int
d
=
dstart
;
d
<
dend
;
++
d
)
{
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
int
w
=
wstart
;
w
<
wend
;
++
w
)
{
if
(
ele
<
input_data
[(
d
*
input_height
+
h
)
*
input_width
+
w
])
{
index
=
(
d
*
input_height
+
h
)
*
input_width
+
w
;
ele
=
input_data
[(
d
*
input_height
+
h
)
*
input_width
+
w
];
}
}
}
}
output_data
[
output_idx
]
=
ele
;
mask_data
[
output_idx
]
=
index
;
}
}
}
// offset
input_data
+=
input_stride
;
output_data
+=
output_stride
;
mask_data
+=
output_stride
;
}
}
}
};
template
<
typename
T
>
class
MaxPool3dWithIndexGradFunctor
<
platform
::
CPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
const
int
batch_size
=
input_grad
.
dims
()[
0
];
const
int
input_depth
=
input_grad
.
dims
()[
2
];
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_depth
=
output_grad
.
dims
()[
2
];
const
int
output_height
=
output_grad
.
dims
()[
3
];
const
int
output_width
=
output_grad
.
dims
()[
4
];
const
int
input_stride
=
input_depth
*
input_height
*
input_width
;
const
int
output_stride
=
output_depth
*
output_height
*
output_width
;
const
T
*
mask_data
=
mask
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
for
(
size_t
n
=
0
;
n
<
batch_size
;
++
n
)
{
for
(
size_t
c
=
0
;
c
<
output_channels
;
++
c
)
{
for
(
size_t
pd
=
0
;
pd
<
output_depth
;
++
pd
)
{
for
(
size_t
ph
=
0
;
ph
<
output_height
;
++
ph
)
{
for
(
size_t
pw
=
0
;
pw
<
output_width
;
++
pw
)
{
const
size_t
output_idx
=
(
pd
*
output_height
+
ph
)
*
output_width
+
pw
;
const
size_t
input_idx
=
static_cast
<
size_t
>
(
mask_data
[
output_idx
]);
input_grad_data
[
input_idx
]
+=
output_grad_data
[
output_idx
];
}
}
}
// offset
input_grad_data
+=
input_stride
;
output_grad_data
+=
output_stride
;
mask_data
+=
output_stride
;
}
}
}
};
template
class
MaxPool3dWithIndexFunctor
<
platform
::
CPUPlace
,
float
>;
template
class
MaxPool3dWithIndexGradFunctor
<
platform
::
CPUPlace
,
float
>;
template
class
MaxPool3dWithIndexFunctor
<
platform
::
CPUPlace
,
double
>;
template
class
MaxPool3dWithIndexGradFunctor
<
platform
::
CPUPlace
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/pooling.cu
0 → 100644
浏览文件 @
6326c40d
/* 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/operators/math/pooling.h"
#include "paddle/platform/cuda_helper.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
template
<
typename
T
>
__global__
void
KernelMaxPool2dWithIdxForward
(
const
int
nthreads
,
const
T
*
input_data
,
T
*
output_data
,
T
*
mask_data
,
const
int
channels
,
const
int
input_height
,
const
int
input_width
,
const
int
output_height
,
const
int
output_width
,
const
int
ksize_height
,
const
int
ksize_width
,
const
int
stride_height
,
const
int
stride_width
,
const
int
padding_height
,
const
int
padding_width
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
nthreads
)
{
int
pw
=
index
%
output_width
;
int
ph
=
(
index
/
output_width
)
%
output_height
;
int
c
=
(
index
/
output_width
/
output_height
)
%
channels
;
int
batch_idx
=
index
/
output_width
/
output_height
/
channels
;
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
hend
=
min
(
hstart
+
ksize_height
,
input_height
);
hstart
=
max
(
hstart
,
0
);
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
wend
=
min
(
wstart
+
ksize_width
,
input_width
);
wstart
=
max
(
wstart
,
0
);
input_data
+=
(
batch_idx
*
channels
+
c
)
*
input_height
*
input_width
;
T
ele
=
-
FLT_MAX
;
int
index
=
-
1
;
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
int
w
=
wstart
;
w
<
wend
;
++
w
)
{
if
(
ele
<
input_data
[
h
*
input_width
+
w
])
{
index
=
h
*
input_width
+
w
;
ele
=
input_data
[
h
*
input_width
+
w
];
}
}
}
output_data
[
index
]
=
ele
;
mask_data
[
index
]
=
index
;
}
}
template
<
typename
T
>
__global__
void
KernelMaxPool2DWithIdxBackward
(
const
int
nthreads
,
T
*
input_grad
,
const
T
*
output_grad
,
const
T
*
mask_data
,
const
int
channels
,
const
int
input_height
,
const
int
input_width
,
const
int
output_height
,
const
int
output_width
,
const
int
ksize_height
,
const
int
ksize_width
,
const
int
stride_height
,
const
int
stride_width
,
const
int
padding_height
,
const
int
padding_width
)
{
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
if
(
index
<
nthreads
)
{
int
offsetW
=
index
%
input_width
+
padding_width
;
int
offsetH
=
(
index
/
input_width
)
%
input_height
+
padding_height
;
int
offsetC
=
(
index
/
input_width
/
input_height
)
%
channels
;
int
batch_idx
=
index
/
input_width
/
input_height
/
channels
;
int
phstart
=
(
offsetH
<
ksize_height
)
?
0
:
(
offsetH
-
ksize_height
)
/
stride_height
+
1
;
int
pwstart
=
(
offsetW
<
ksize_width
)
?
0
:
(
offsetW
-
ksize_width
)
/
stride_width
+
1
;
int
phend
=
min
(
offsetH
/
stride_height
+
1
,
output_height
);
int
pwend
=
min
(
offsetW
/
stride_width
+
1
,
output_width
);
T
gradient
=
0
;
int
output_idx
=
(
batch_idx
*
channels
+
offsetC
)
*
output_height
*
output_width
;
mask_data
+=
output_idx
;
output_grad
+=
output_idx
;
for
(
int
ph
=
phstart
;
ph
<
phend
;
++
ph
)
{
for
(
int
pw
=
pwstart
;
pw
<
pwend
;
++
pw
)
{
if
((
offsetH
*
input_width
+
offsetW
)
==
mask_data
[
ph
*
output_width
+
pw
])
gradient
+=
output_grad
[
ph
*
output_width
+
pw
];
}
}
input_grad
[
index
]
=
gradient
;
}
}
template
<
typename
T
>
class
MaxPool2dWithIndexFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_channels
=
input
.
dims
()[
1
];
const
int
input_height
=
input
.
dims
()[
2
];
const
int
input_width
=
input
.
dims
()[
3
];
const
int
output_channels
=
output
.
dims
()[
1
];
const
int
output_height
=
output
.
dims
()[
2
];
const
int
output_width
=
output
.
dims
()[
3
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_width
=
ksize
[
1
];
const
int
stride_height
=
strides
[
0
];
const
int
stride_width
=
strides
[
1
];
const
int
padding_height
=
paddings
[
0
];
const
int
padding_width
=
paddings
[
1
];
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
mask_data
=
mask
.
mutable_data
<
T
>
(
context
.
GetPlace
());
int
nthreads
=
batch_size
*
output_channels
*
output_height
*
output_width
;
int
blocks
=
(
nthreads
+
1024
-
1
)
/
1024
;
dim3
threads
(
1024
,
1
);
dim3
grid
(
blocks
,
1
);
KernelMaxPool2dWithIdxForward
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
nthreads
,
input_data
,
output_data
,
mask_data
,
input_channels
,
input_height
,
input_width
,
output_height
,
output_width
,
ksize_height
,
ksize_width
,
stride_height
,
stride_width
,
padding_height
,
padding_width
);
}
};
template
<
typename
T
>
class
MaxPool2dWithIndexGradFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
const
int
batch_size
=
input_grad
.
dims
()[
0
];
const
int
input_channels
=
input_grad
.
dims
()[
1
];
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_height
=
output_grad
.
dims
()[
2
];
const
int
output_width
=
output_grad
.
dims
()[
3
];
const
int
ksize_height
=
ksize
[
0
];
const
int
ksize_width
=
ksize
[
1
];
const
int
stride_height
=
strides
[
0
];
const
int
stride_width
=
strides
[
1
];
const
int
padding_height
=
paddings
[
0
];
const
int
padding_width
=
paddings
[
1
];
const
T
*
mask_data
=
mask
.
data
<
T
>
();
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
int
nthreads
=
batch_size
*
input_channels
*
input_height
*
input_width
;
int
blocks
=
(
nthreads
+
1024
-
1
)
/
1024
;
dim3
threads
(
1024
,
1
);
dim3
grid
(
blocks
,
1
);
KernelMaxPool2DWithIdxBackward
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
nthreads
,
input_grad_data
,
output_grad_data
,
mask_data
,
input_channels
,
input_height
,
input_width
,
output_height
,
output_width
,
ksize_height
,
ksize_width
,
stride_height
,
stride_width
,
padding_height
,
padding_width
);
}
};
template
class
MaxPool2dWithIndexFunctor
<
platform
::
GPUPlace
,
float
>;
template
class
MaxPool2dWithIndexGradFunctor
<
platform
::
GPUPlace
,
float
>;
template
class
MaxPool2dWithIndexFunctor
<
platform
::
GPUPlace
,
double
>;
template
class
MaxPool2dWithIndexGradFunctor
<
platform
::
GPUPlace
,
double
>;
template
<
typename
T
>
__global__
void
KernelMaxPool3DWithIdxForward
(
const
int
nthreads
,
const
T
*
input_data
,
T
*
output_data
,
T
*
mask_data
,
const
int
channels
,
const
int
input_depth
,
const
int
input_height
,
const
int
input_width
,
const
int
output_depth
,
const
int
output_height
,
const
int
output_width
,
const
int
ksize_depth
,
const
int
ksize_height
,
const
int
ksize_width
,
const
int
stride_depth
,
const
int
stride_height
,
const
int
stride_width
,
const
int
padding_depth
,
const
int
padding_height
,
const
int
padding_width
)
{
for
(
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
index
<
(
nthreads
);
index
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
pw
=
index
%
output_width
;
int
ph
=
(
index
/
output_width
)
%
output_height
;
int
pd
=
(
index
/
output_width
/
output_height
)
%
output_depth
;
int
c
=
(
index
/
output_width
/
output_height
/
output_depth
)
%
channels
;
int
batch_idx
=
index
/
output_width
/
output_height
/
output_depth
/
channels
;
int
dstart
=
pd
*
stride_depth
-
padding_depth
;
int
hstart
=
ph
*
stride_height
-
padding_height
;
int
wstart
=
pw
*
stride_width
-
padding_width
;
int
dend
=
min
(
dstart
+
ksize_depth
,
input_depth
);
int
hend
=
min
(
hstart
+
ksize_height
,
input_height
);
int
wend
=
min
(
wstart
+
ksize_width
,
input_width
);
dstart
=
max
(
dstart
,
0
);
hstart
=
max
(
hstart
,
0
);
wstart
=
max
(
wstart
,
0
);
T
ele
=
-
FLT_MAX
;
int
index
=
-
1
;
input_data
+=
(
batch_idx
*
channels
+
c
)
*
input_depth
*
input_height
*
input_width
;
for
(
int
d
=
dstart
;
d
<
dend
;
++
d
)
{
for
(
int
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
int
w
=
wstart
;
w
<
wend
;
++
w
)
{
if
(
ele
<
input_data
[(
d
*
input_height
+
h
)
*
input_width
+
w
])
{
index
=
(
d
*
input_height
+
h
)
*
input_width
+
w
;
ele
=
input_data
[(
d
*
input_height
+
h
)
*
input_width
+
w
];
}
}
}
}
output_data
[
index
]
=
ele
;
mask_data
[
index
]
=
index
;
}
}
template
<
typename
T
>
__global__
void
KernelMaxPool3DWithIdxBackward
(
const
int
nthreads
,
T
*
input_grad
,
const
T
*
output_grad
,
const
T
*
mask
,
const
int
channels
,
const
int
input_depth
,
const
int
input_height
,
const
int
input_width
,
const
int
output_depth
,
const
int
output_height
,
const
int
output_width
,
const
int
ksize_depth
,
const
int
ksize_height
,
const
int
ksize_width
,
const
int
stride_depth
,
const
int
stride_height
,
const
int
stride_width
,
const
int
padding_depth
,
const
int
padding_height
,
const
int
padding_width
)
{
for
(
int
index
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
index
<
(
nthreads
);
index
+=
blockDim
.
x
*
gridDim
.
x
)
{
int
offsetW
=
index
%
input_width
+
padding_width
;
int
offsetH
=
(
index
/
input_width
)
%
input_height
+
padding_height
;
int
offsetD
=
(
index
/
input_width
/
input_height
)
%
input_depth
+
padding_depth
;
int
offsetC
=
(
index
/
input_width
/
input_height
/
input_depth
)
%
channels
;
int
batch_idx
=
index
/
input_width
/
input_height
/
input_depth
/
channels
;
int
pdstart
=
(
offsetD
<
ksize_depth
)
?
0
:
(
offsetD
-
ksize_depth
)
/
stride_depth
+
1
;
int
phstart
=
(
offsetH
<
ksize_height
)
?
0
:
(
offsetH
-
ksize_height
)
/
stride_height
+
1
;
int
pwstart
=
(
offsetW
<
ksize_width
)
?
0
:
(
offsetW
-
ksize_width
)
/
stride_width
+
1
;
int
pdend
=
min
((
offsetD
)
/
stride_depth
+
1
,
output_depth
);
int
phend
=
min
((
offsetH
)
/
stride_height
+
1
,
output_height
);
int
pwend
=
min
((
offsetW
)
/
stride_width
+
1
,
output_width
);
T
gradient
=
0
;
int
output_idx
=
(
batch_idx
*
channels
+
offsetC
)
*
output_depth
*
output_height
*
output_width
;
mask
+=
output_idx
;
output_grad
+=
output_idx
;
for
(
int
pd
=
pdstart
;
pd
<
pdend
;
++
pd
)
{
for
(
int
ph
=
phstart
;
ph
<
phend
;
++
ph
)
{
for
(
int
pw
=
pwstart
;
pw
<
pwend
;
++
pw
)
{
if
(((
offsetD
*
input_height
+
offsetH
)
*
input_width
+
offsetW
)
==
mask
[(
pd
*
output_height
+
ph
)
*
output_width
+
pw
])
gradient
+=
output_grad
[(
pd
*
output_height
+
ph
)
*
output_width
+
pw
];
}
}
}
input_grad
[
index
]
=
gradient
;
}
}
template
<
typename
T
>
class
MaxPool3dWithIndexFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
const
int
batch_size
=
input
.
dims
()[
0
];
const
int
input_channels
=
input
.
dims
()[
1
];
const
int
input_depth
=
input
.
dims
()[
2
];
const
int
input_height
=
input
.
dims
()[
3
];
const
int
input_width
=
input
.
dims
()[
4
];
const
int
output_channels
=
output
.
dims
()[
1
];
const
int
output_depth
=
output
.
dims
()[
2
];
const
int
output_height
=
output
.
dims
()[
3
];
const
int
output_width
=
output
.
dims
()[
4
];
const
int
ksize_depth
=
ksize
[
0
];
const
int
ksize_height
=
ksize
[
1
];
const
int
ksize_width
=
ksize
[
2
];
const
int
stride_depth
=
strides
[
0
];
const
int
stride_height
=
strides
[
1
];
const
int
stride_width
=
strides
[
2
];
const
int
padding_depth
=
paddings
[
0
];
const
int
padding_height
=
paddings
[
1
];
const
int
padding_width
=
paddings
[
2
];
const
T
*
input_data
=
input
.
data
<
T
>
();
T
*
output_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
T
*
mask_data
=
output
.
mutable_data
<
T
>
(
context
.
GetPlace
());
int
nthreads
=
batch_size
*
output_channels
*
output_depth
*
output_height
*
output_width
;
int
blocks
=
(
nthreads
+
1024
-
1
)
/
1024
;
dim3
threads
(
1024
,
1
);
dim3
grid
(
blocks
,
1
);
KernelMaxPool3DWithIdxForward
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
nthreads
,
input_data
,
output_data
,
mask_data
,
input_channels
,
input_depth
,
input_height
,
input_width
,
output_depth
,
output_height
,
output_width
,
ksize_depth
,
ksize_height
,
ksize_width
,
stride_depth
,
stride_height
,
stride_width
,
padding_depth
,
padding_height
,
padding_width
);
}
};
template
<
typename
T
>
class
MaxPool3dWithIndexGradFunctor
<
platform
::
GPUPlace
,
T
>
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
)
{
const
int
batch_size
=
input_grad
.
dims
()[
0
];
const
int
input_channels
=
input_grad
.
dims
()[
1
];
const
int
input_depth
=
input_grad
.
dims
()[
2
];
const
int
input_height
=
input_grad
.
dims
()[
3
];
const
int
input_width
=
input_grad
.
dims
()[
4
];
const
int
output_channels
=
input_grad
.
dims
()[
1
];
const
int
output_depth
=
input_grad
.
dims
()[
2
];
const
int
output_height
=
input_grad
.
dims
()[
3
];
const
int
output_width
=
input_grad
.
dims
()[
4
];
const
int
ksize_depth
=
ksize
[
0
];
const
int
ksize_height
=
ksize
[
1
];
const
int
ksize_width
=
ksize
[
2
];
const
int
stride_depth
=
strides
[
0
];
const
int
stride_height
=
strides
[
1
];
const
int
stride_width
=
strides
[
2
];
const
int
padding_depth
=
paddings
[
0
];
const
int
padding_height
=
paddings
[
1
];
const
int
padding_width
=
paddings
[
2
];
const
T
*
output_grad_data
=
output_grad
.
data
<
T
>
();
const
T
*
mask_data
=
mask
.
data
<
T
>
();
T
*
input_grad_data
=
input_grad
.
mutable_data
<
T
>
(
context
.
GetPlace
());
int
nthreads
=
batch_size
*
input_channels
*
input_depth
*
input_height
*
input_width
;
int
blocks
=
(
nthreads
+
1024
-
1
)
/
1024
;
dim3
threads
(
1024
,
1
);
dim3
grid
(
blocks
,
1
);
KernelMaxPool3DWithIdxBackward
<
T
><<<
grid
,
threads
,
0
,
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
)
.
stream
()
>>>
(
nthreads
,
input_grad_data
,
output_grad_data
,
mask_data
,
input_channels
,
input_depth
,
input_height
,
input_width
,
output_depth
,
output_height
,
output_width
,
ksize_depth
,
ksize_height
,
ksize_width
,
stride_depth
,
stride_height
,
stride_width
,
padding_depth
,
padding_height
,
padding_width
);
}
};
template
class
MaxPool3dWithIndexFunctor
<
platform
::
GPUPlace
,
float
>;
template
class
MaxPool3dWithIndexGradFunctor
<
platform
::
GPUPlace
,
float
>;
template
class
MaxPool3dWithIndexFunctor
<
platform
::
GPUPlace
,
double
>;
template
class
MaxPool3dWithIndexGradFunctor
<
platform
::
GPUPlace
,
double
>;
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/math/pooling.h
0 → 100644
浏览文件 @
6326c40d
/* 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/framework/eigen.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/hostdevice.h"
namespace
paddle
{
namespace
operators
{
namespace
math
{
//////////////////////
#define FLT_MAX __FLT_MAX__
/////////////////////
template
<
typename
Place
,
typename
T
>
class
MaxPool2dWithIndexFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
};
template
<
typename
Place
,
typename
T
>
class
MaxPool2dWithIndexGradFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
};
template
<
typename
Place
,
typename
T
>
class
MaxPool3dWithIndexFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
const
framework
::
Tensor
&
input
,
framework
::
Tensor
&
output
,
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
};
template
<
typename
Place
,
typename
T
>
class
MaxPool3dWithIndexGradFunctor
{
public:
void
operator
()(
const
platform
::
DeviceContext
&
context
,
framework
::
Tensor
&
input_grad
,
const
framework
::
Tensor
&
output_grad
,
const
framework
::
Tensor
&
mask
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
);
};
}
// namespace math
}
// namespace operators
}
// namespace paddle
paddle/operators/pool_with_index_op.cc
0 → 100644
浏览文件 @
6326c40d
/* 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/operators/pool_with_index_op.h"
namespace
paddle
{
namespace
operators
{
int
OutputSizeMaxPool
(
int
input_size
,
int
filter_size
,
int
padding
,
int
stride
)
{
int
output_size
=
(
input_size
-
filter_size
+
2
*
padding
)
/
stride
+
1
;
return
output_size
;
}
class
MaxPoolWithIndexOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"X(Input) of Pooling should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Out(Output) of Pooling should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Mask"
),
"Out(Output) of Pooling should not be null."
);
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
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
||
in_x_dims
.
size
()
==
5
,
"Pooling intput should be 4-D or 5-D"
);
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"globalPooling"
))
{
ksize
.
resize
(
static_cast
<
size_t
>
(
in_x_dims
.
size
())
-
2
);
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
ksize
[
i
]
=
static_cast
<
int
>
(
in_x_dims
[
i
+
2
]);
}
PADDLE_ENFORCE
(
in_x_dims
.
size
()
-
ksize
.
size
()
==
2U
,
"Pooling intput size and pooling size should be consistent"
);
PADDLE_ENFORCE
(
ksize
.
size
()
==
2
||
ksize
.
size
()
==
3
,
"Pooling size size should be 2 elements. or 3 elements."
);
PADDLE_ENFORCE_EQ
(
ksize
.
size
(),
strides
.
size
(),
"strides size and pooling size should be the same."
);
PADDLE_ENFORCE_EQ
(
ksize
.
size
(),
paddings
.
size
(),
"paddings size and pooling size should be the same."
);
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
(
OutputSizeMaxPool
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
ctx
->
SetOutputDim
(
"Mask"
,
framework
::
make_ddim
(
output_shape
));
}
};
class
MaxPoolWithIndexOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContextBase
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"X"
)),
"X(Input) of MaxPoolWithIndexOpGrad should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"X@GRAD(Input@GRAD) of MaxPoolWithIndexOpGrad should not be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
class
MaxPool2dWithIndexOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
MaxPool2dWithIndexOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input tensor of pooling 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 image."
);
AddOutput
(
"Out"
,
"The output tensor of pooling operator."
"The format of output tensor is also NCHW."
);
AddOutput
(
"Mask"
,
"The Mask tensor of pooling operator."
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"pooling size(height, width) of pooling operator."
);
AddAttr
<
bool
>
(
"globalPooling"
,
"whether to use the globalPooling."
"int constant equal to false or true"
"default false"
"If globalPooling = true, ksize is ignored and need not be specified."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"strides(height, width) of pooling operator."
"default {1,1}"
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"paddings(height, width) of pooling operator."
"default {0,0}"
)
.
SetDefault
({
0
,
0
});
AddComment
(
R"DOC(
The maxPooling2d with index operation calculates the output and the mask based on
the input and ksize, strides, paddings parameters.
)DOC"
);
}
};
class
MaxPool3dWithIndexOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
MaxPool3dWithIndexOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input tensor of pooling operator. "
"The format of input tensor is NCDHW. Where N is batch size, C is "
"the number of channels, D, H and W is the depth, height and width of "
"image."
);
AddOutput
(
"Out"
,
"The output tensor of pooling operator."
"The format of output tensor is also NCDHW."
);
AddOutput
(
"Mask"
,
"The Mask tensor of pooling operator."
"The format of output tensor is also NCDHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"pooling size(depth, height, width) of pooling operator."
);
AddAttr
<
bool
>
(
"globalPooling"
,
"whether to use the globalPooling."
"int constant equal to false or true"
"default false"
"If globalPooling = true, ksize is ignored and need not be specified."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"strides(depth, height, width) of pooling operator."
"default {1,1,1}"
)
.
SetDefault
({
1
,
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"paddings(depth, height, width) of pooling operator."
"default {0,0,0}"
)
.
SetDefault
({
0
,
0
,
0
});
AddComment
(
R"DOC(
The maxpooling3d with index operation calculates the output and the mask based on
the input and ksize, strides, paddings parameters.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
maxPool2dWithIndex
,
ops
::
MaxPoolWithIndexOp
,
ops
::
MaxPool2dWithIndexOpMaker
,
maxPool2dWithIndex_grad
,
ops
::
MaxPoolWithIndexOpGrad
);
REGISTER_OP_CPU_KERNEL
(
maxPool2dWithIndex
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
maxPool2dWithIndex_grad
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
)
REGISTER_OP
(
maxPool3dWithIndex
,
ops
::
MaxPoolWithIndexOp
,
ops
::
MaxPool3dWithIndexOpMaker
,
maxPool3dWithIndex_grad
,
ops
::
MaxPoolWithIndexOpGrad
);
REGISTER_OP_CPU_KERNEL
(
maxPool3dWithIndex
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
maxPool3dWithIndex_grad
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
)
paddle/operators/pool_with_index_op.cu
0 → 100644
浏览文件 @
6326c40d
/* 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/operators/pool_with_index_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
maxPool2dWithIndex
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
maxPool2dWithIndex_grad
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
)
REGISTER_OP_GPU_KERNEL
(
maxPool3dWithIndex
,
ops
::
MaxPoolWithIndexKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
maxPool3dWithIndex_grad
,
ops
::
MaxPoolWithIndexGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
)
paddle/operators/pool_with_index_op.h
0 → 100644
浏览文件 @
6326c40d
/* 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/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/pooling.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
Place
,
typename
T
>
class
MaxPoolWithIndexKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
in_x
=
context
.
Input
<
Tensor
>
(
"X"
);
Tensor
*
out
=
context
.
Output
<
Tensor
>
(
"Out"
);
Tensor
*
mask
=
context
.
Output
<
Tensor
>
(
"Mask"
);
bool
global_pooling
=
context
.
Attr
<
bool
>
(
"globalPooling"
);
std
::
vector
<
int
>
ksize
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
if
(
global_pooling
)
{
for
(
size_t
i
=
0
;
i
<
ksize
.
size
();
++
i
)
{
ksize
[
i
]
=
static_cast
<
int
>
(
in_x
->
dims
()[
i
+
2
]);
}
}
switch
(
ksize
.
size
())
{
case
2
:
{
paddle
::
operators
::
math
::
MaxPool2dWithIndexFunctor
<
Place
,
T
>
pool2d_forward
;
pool2d_forward
(
context
.
device_context
(),
*
in_x
,
*
out
,
*
mask
,
ksize
,
strides
,
paddings
);
}
break
;
case
3
:
{
paddle
::
operators
::
math
::
MaxPool3dWithIndexFunctor
<
Place
,
T
>
pool3d_forward
;
pool3d_forward
(
context
.
device_context
(),
*
in_x
,
*
out
,
*
mask
,
ksize
,
strides
,
paddings
);
}
break
;
}
}
};
template
<
typename
Place
,
typename
T
>
class
MaxPoolWithIndexGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
mask
=
context
.
Input
<
Tensor
>
(
"Maks"
);
const
Tensor
*
out_grad
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
Tensor
*
in_x_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
std
::
vector
<
int
>
ksize
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
if
(
in_x_grad
)
{
in_x_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
temp
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
in_x_grad
);
temp
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
temp
.
constant
(
static_cast
<
T
>
(
0
));
switch
(
ksize
.
size
())
{
case
2
:
{
paddle
::
operators
::
math
::
MaxPool2dWithIndexGradFunctor
<
Place
,
T
>
pool2d_backward
;
pool2d_backward
(
context
.
device_context
(),
*
in_x_grad
,
*
out_grad
,
*
mask
,
ksize
,
strides
,
paddings
);
}
break
;
case
3
:
{
paddle
::
operators
::
math
::
MaxPool3dWithIndexGradFunctor
<
Place
,
T
>
pool3d_backward
;
pool3d_backward
(
context
.
device_context
(),
*
in_x_grad
,
*
out_grad
,
*
mask
,
ksize
,
strides
,
paddings
);
}
break
;
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_pool_max_op.py
0 → 100644
浏览文件 @
6326c40d
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
max_pool3D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
N
,
C
,
D
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
D
,
H
,
W
]
D_out
=
(
D
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
H_out
=
(
H
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
W_out
=
(
W
-
ksize
[
2
]
+
2
*
paddings
[
2
])
/
strides
[
2
]
+
1
out
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
mask
=
np
.
zeros
((
N
,
C
,
D_out
,
H_out
,
W_out
))
for
k
in
xrange
(
D_out
):
d_start
=
np
.
max
((
k
*
strides
[
0
]
-
paddings
[
0
],
0
))
d_end
=
np
.
min
((
k
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
D
))
for
i
in
xrange
(
H_out
):
h_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
h_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
for
j
in
xrange
(
W_out
):
w_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
w_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
d_start
:
d_end
,
h_start
:
h_end
,
w_start
:
w_end
]
out
[:,
:,
k
,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
,
4
))
# mask[:,:, k, i, j] = np.argmax(x_masked, axis=(2, 3, 4))
return
out
def
max_pool2D_forward_naive
(
x
,
ksize
,
strides
,
paddings
=
[
0
,
0
],
global_pool
=
0
):
N
,
C
,
H
,
W
=
x
.
shape
if
global_pool
==
1
:
ksize
=
[
H
,
W
]
H_out
=
(
H
-
ksize
[
0
]
+
2
*
paddings
[
0
])
/
strides
[
0
]
+
1
W_out
=
(
W
-
ksize
[
1
]
+
2
*
paddings
[
1
])
/
strides
[
1
]
+
1
out
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
mask
=
np
.
zeros
((
N
,
C
,
H_out
,
W_out
))
for
i
in
xrange
(
H_out
):
for
j
in
xrange
(
W_out
):
r_start
=
np
.
max
((
i
*
strides
[
0
]
-
paddings
[
0
],
0
))
r_end
=
np
.
min
((
i
*
strides
[
0
]
+
ksize
[
0
]
-
paddings
[
0
],
H
))
c_start
=
np
.
max
((
j
*
strides
[
1
]
-
paddings
[
1
],
0
))
c_end
=
np
.
min
((
j
*
strides
[
1
]
+
ksize
[
1
]
-
paddings
[
1
],
W
))
x_masked
=
x
[:,
:,
r_start
:
r_end
,
c_start
:
c_end
]
out
[:,
:,
i
,
j
]
=
np
.
max
(
x_masked
,
axis
=
(
2
,
3
))
# mask[:,:, i, j] = np.argmax(x_masked, axis=(2, 3))
return
out
class
TestMaxPoolWithIndex_Op
(
OpTest
):
def
setUp
(
self
):
self
.
initTestCase
()
self
.
op_type
=
"maxPool3dWithIndex"
input
=
np
.
random
.
random
(
self
.
shape
).
astype
(
"float32"
)
output
=
self
.
pool_forward_naive
(
input
,
self
.
ksize
,
self
.
strides
,
self
.
paddings
,
self
.
global_pool
)
# mask = np.zeros(output.shape)
self
.
attrs
=
{
'strides'
:
self
.
strides
,
'paddings'
:
self
.
paddings
,
'ksize'
:
self
.
ksize
,
'globalPooling'
:
self
.
global_pool
,
}
self
.
inputs
=
{
'X'
:
input
}
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
# def test_check_grad(self):
# self.check_grad(set(['X']), ['Out'], max_relative_error=0.07)
def
initTestCase
(
self
):
self
.
global_pool
=
0
self
.
pool_forward_naive
=
max_pool3D_forward_naive
self
.
shape
=
[
2
,
3
,
7
,
7
,
7
]
self
.
ksize
=
[
3
,
3
,
3
]
self
.
strides
=
[
1
,
1
,
1
]
self
.
paddings
=
[
1
,
1
,
1
]
""""
class TestCase1(TestMaxPoolWithIndex_Op):
def initTestCase(self):
self.global_pool = 1
self.op_type = "maxPool3dWithIndex"
self.pool_forward_naive = max_pool3D_forward_naive
self.shape = [2, 3, 5, 5, 5]
self.ksize = [3, 3, 3]
self.strides = [1, 1, 1]
self.paddings = [0, 0, 0]
class TestCase2(TestMaxPoolWithIndex_Op):
def initTestCase(self):
self.global_pool = 0
self.op_type = "maxPool2dWithIndex"
self.pool_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [1, 1]
class TestCase3(TestMaxPoolWithIndex_Op):
def initTestCase(self):
self.global_pool = 1
self.op_type = "maxPool2dWithIndex"
self.pool_forward_naive = max_pool2D_forward_naive
self.shape = [2, 3, 5, 5]
self.ksize = [3, 3]
self.strides = [1, 1]
self.paddings = [0, 0]
if __name__ == '__main__':
unittest.main()
"""
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录