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
wk1234123
Paddle
提交
e9325ea8
P
Paddle
项目概览
wk1234123
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
4
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
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
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
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
e9325ea8
编写于
10月 11, 2017
作者:
C
chengduo
提交者:
GitHub
10月 11, 2017
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #4676 from chengduoZH/fix_pool_op_doc_and_class_declarations
Fix pool op doc and class declarations
上级
2f170384
4849fba7
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
243 addition
and
160 deletion
+243
-160
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+5
-1
paddle/operators/math/CMakeLists.txt
paddle/operators/math/CMakeLists.txt
+4
-2
paddle/operators/pool_op.cc
paddle/operators/pool_op.cc
+167
-143
paddle/operators/pool_op.h
paddle/operators/pool_op.h
+28
-0
paddle/operators/pool_with_index_op.cc
paddle/operators/pool_with_index_op.cc
+39
-14
未找到文件。
paddle/operators/CMakeLists.txt
浏览文件 @
e9325ea8
...
...
@@ -112,7 +112,9 @@ set(DEPS_OPS
cond_op
cross_entropy_op
softmax_with_cross_entropy_op
sum_op
)
sum_op
pool_op
pool_with_index_op
)
op_library
(
recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
...
...
@@ -121,6 +123,8 @@ op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
op_library
(
cross_entropy_op DEPS cross_entropy
)
op_library
(
softmax_with_cross_entropy_op DEPS cross_entropy softmax
)
op_library
(
sum_op DEPS net_op
)
op_library
(
pool_op DEPS pooling
)
op_library
(
pool_with_index_op DEPS pooling
)
list
(
REMOVE_ITEM GENERAL_OPS
${
DEPS_OPS
}
)
foreach
(
src
${
GENERAL_OPS
}
)
...
...
paddle/operators/math/CMakeLists.txt
浏览文件 @
e9325ea8
if
(
WITH_GPU
)
nv_library
(
math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu
pooling.cc pooling.cu
DEPS cblas device_context operator
)
nv_library
(
math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu DEPS cblas device_context operator
)
nv_test
(
math_function_test SRCS math_function_test.cc DEPS math_function tensor
)
nv_library
(
softmax SRCS softmax.cc softmax.cu DEPS operator
)
nv_library
(
cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator
)
nv_library
(
pooling SRCS pooling.cc pooling.cu DEPS device_context
)
nv_library
(
vol2col SRCS vol2col.cc vol2col.cu DEPS device_context
)
else
()
cc_library
(
math_function SRCS math_function.cc im2col.cc
pooling.cc
DEPS cblas device_context operator
)
cc_library
(
math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator
)
cc_test
(
math_function_test SRCS math_function_test.cc DEPS math_function tensor
)
cc_library
(
softmax SRCS softmax.cc DEPS operator
)
cc_library
(
cross_entropy SRCS cross_entropy.cc DEPS operator
)
cc_library
(
pooling SRCS pooling.cc DEPS device_context
)
cc_library
(
vol2col SRCS vol2col.cc DEPS device_context
)
endif
()
...
...
paddle/operators/pool_op.cc
浏览文件 @
e9325ea8
...
...
@@ -22,157 +22,181 @@ int OutputSizePool(int input_size, int filter_size, int padding, int stride) {
return
output_size
;
}
class
PoolOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
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."
);
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
std
::
string
pooling_type
=
ctx
->
Attrs
().
Get
<
std
::
string
>
(
"poolingType"
);
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
(
pooling_type
==
"max"
||
pooling_type
==
"avg"
,
"pooling_type should be 'max' or 'avg'"
);
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
,
"Input size and Pooling size should be consistent."
);
PADDLE_ENFORCE
(
ksize
.
size
()
==
2
||
ksize
.
size
()
==
3
,
"Pooling 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
(
OutputSizePool
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
void
PoolOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
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."
);
auto
in_x_dims
=
ctx
->
GetInputDim
(
"X"
);
std
::
string
pooling_type
=
ctx
->
Attrs
().
Get
<
std
::
string
>
(
"poolingType"
);
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 tensor."
);
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
]);
}
};
class
PoolOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"X(Input) of Pooling should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Input@Grad of Pooling should not be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
PADDLE_ENFORCE
(
in_x_dims
.
size
()
-
ksize
.
size
()
==
2U
,
"Input size and pooling size should be consistent."
);
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
(
OutputSizePool
(
in_x_dims
[
i
+
2
],
ksize
[
i
],
paddings
[
i
],
strides
[
i
]));
}
};
class
Pool2dOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Pool2dOpMaker
(
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 feature."
);
AddOutput
(
"Out"
,
"The output tensor of pooling operator."
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
string
>
(
"poolingType"
,
"PoolingType of pooling operator."
"Str constant equal to 'max' or 'avg'."
)
.
InEnum
({
"max"
,
"avg"
});
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"Pooling size(depth, height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."
);
// TODO(Add checker)
AddAttr
<
bool
>
(
"globalPooling"
,
"Whether to use the globalPooling."
"Bool 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
});
// TODO(Add checker)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"Paddings(height, width) of pooling operator."
"Default {0,0}."
)
.
SetDefault
({
0
,
0
});
// TODO(Add checker)
AddComment
(
R"DOC(
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
output_shape
));
}
void
PoolOpGrad
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Input(X@GRAD) should not be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
Pool2dOpMaker
::
Pool2dOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(Tensor) 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 feature."
);
AddOutput
(
"Out"
,
"(Tensor) The output tensor of pooling operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is "
"the number of channels, H and W is the height and "
"width of feature."
);
AddAttr
<
std
::
string
>
(
"poolingType"
,
"PoolingType of pooling operator."
"Str constant equal to 'max' or 'avg'."
)
.
InEnum
({
"max"
,
"avg"
});
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"The pooling window size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."
);
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr
<
bool
>
(
"globalPooling"
,
"Whether to use the globalPooling."
"Bool 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"
,
"The strides(height, width) of pooling window."
"Default {1,1}."
)
.
SetDefault
({
1
,
1
});
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"The zero padding(height, width) size on both sides"
"Default {0,0}."
)
.
SetDefault
({
0
,
0
});
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddComment
(
R"DOC(
The pooling2d operation calculates the output based on
the input, poolingType and ksize, strides, paddings parameters.
Input(X) and output(Out) are in NCHW format. Where N is batch size, C is the
number of channels, H and W is the height and width of feature.
Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
X shape: (N, C, H_in, W_in)
Output:
Out shape: (N, C, H_out, W_out)
Mask shape: (N, C, H_out, W_out)
where
H_out = (H_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
W_out = (W_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
)DOC"
);
}
};
class
Pool3dOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Pool3dOpMaker
(
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 "
"feature."
);
AddOutput
(
"Out"
,
"The output tensor of pooling operator."
"The format of output tensor is also NCDHW."
);
AddAttr
<
std
::
string
>
(
"poolingType"
,
"PoolingType of pooling operator."
"str constant equal to 'max' or 'avg'."
)
.
InEnum
({
"max"
,
"avg"
});
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"Pooling size(depth, height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."
);
// TODO(Add checker)
AddAttr
<
bool
>
(
"globalPooling"
,
"Whether to use the globalPooling."
"Bool 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
});
// TODO(Add checker)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"Paddings(depth, height, width) of pooling operator."
"Default {0,0,0}."
)
.
SetDefault
({
0
,
0
,
0
});
// TODO(Add checker)
AddComment
(
R"DOC(
}
Pool3dOpMaker
::
Pool3dOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"(Tensor) 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 "
"feature."
);
AddOutput
(
"Out"
,
"(Tensor) The output tensor of pooling operator."
"The format of output tensor is also NCDHW."
"Where N is batch size, C is "
"the number of channels, D, H and W is the depth, height and "
"width of feature."
);
AddAttr
<
std
::
string
>
(
"poolingType"
,
"PoolingType of pooling operator."
"Str constant equal to 'max' or 'avg'."
)
.
InEnum
({
"max"
,
"avg"
});
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"The pooling window size(depth, height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."
);
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr
<
bool
>
(
"globalPooling"
,
"Whether to use the globalPooling."
"Bool 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
});
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"Paddings(depth, height, width) of pooling operator."
"Default {0,0,0}."
)
.
SetDefault
({
0
,
0
,
0
});
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddComment
(
R"DOC(
The pooling3d operation calculates the output based on
the input, poolingType and ksize, strides, paddings parameters.
Input(X) and output(Out) are in NCDHW format. Where N is batch
size, C is the number of channels, D, H and W is the depth, height and
width of feature. Parameters(ksize, strides, paddings) are three elements.
These three elements represent depth, height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
X shape: (N, C, D_in, H_in, W_in)
Output:
Out shape: (N, C, D_out, H_out, W_out)
Mask shape: (N, C, D_out, H_out, W_out)
where
D_out = (D_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
H_out = (H_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
W_out = (W_in - ksize[2] + 2 * paddings[2]) / strides[2] + 1;
)DOC"
);
}
};
}
}
// namespace operators
}
// namespace paddle
...
...
paddle/operators/pool_op.h
浏览文件 @
e9325ea8
...
...
@@ -24,6 +24,34 @@ namespace operators {
using
Tensor
=
framework
::
Tensor
;
class
PoolOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
class
PoolOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
class
Pool2dOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Pool2dOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
);
};
class
Pool3dOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Pool3dOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
);
};
template
<
typename
Place
,
typename
T
>
class
PoolKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
...
...
paddle/operators/pool_with_index_op.cc
浏览文件 @
e9325ea8
...
...
@@ -43,7 +43,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
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"
);
"Pooling intput should be 4-D or 5-D
tensor.
"
);
if
(
ctx
->
Attrs
().
Get
<
bool
>
(
"globalPooling"
))
{
ksize
.
resize
(
static_cast
<
size_t
>
(
in_x_dims
.
size
())
-
2
);
...
...
@@ -52,7 +52,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
}
PADDLE_ENFORCE
(
in_x_dims
.
size
()
-
ksize
.
size
()
==
2U
,
"In
t
put size and pooling size should be consistent."
);
"Input size and pooling size should be consistent."
);
PADDLE_ENFORCE_EQ
(
ksize
.
size
(),
strides
.
size
(),
"Strides size and pooling size should be the same."
);
PADDLE_ENFORCE_EQ
(
ksize
.
size
(),
paddings
.
size
(),
...
...
@@ -74,6 +74,7 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Mask"
),
"Input(Mask) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) must not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"X"
)),
"Input(X@GRAD) should not be null."
);
...
...
@@ -88,17 +89,17 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input tensor of pooling operator. "
"
(Tensor)
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."
"
(Tensor)
The output tensor of pooling operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is "
"the number of channels, H and W is the height and "
"width of image."
);
AddOutput
(
"Mask"
,
"The Mask tensor of pooling operator."
"
(Tensor)
The Mask tensor of pooling operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is the number of channels, H and W "
"is the height and width of image."
...
...
@@ -106,7 +107,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"The pooling size(height, width) of pooling operator."
"The pooling
window
size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."
);
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
...
...
@@ -118,13 +119,14 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"If globalPooling = true, ksize is ignored and need not be specified."
)
.
SetDefault
(
false
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"
Strides(height, width) of pooling operator
."
"
The strides(height, width) of pooling window
."
"Default {1,1}."
)
.
SetDefault
({
1
,
1
});
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"Paddings(height, width) of pooling operator."
"Default {0,0}."
)
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"The zero padding(height, width) size on both sides"
"Default {0,0}."
)
.
SetDefault
({
0
,
0
});
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
...
...
@@ -135,6 +137,17 @@ output(Out, Mask) are in NCHW format. Where N is batch size, C is the
number of channels, H and W is the height and width of feature.
Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out, Mask) size may be different.
Example:
Input:
X shape: (N, C, H_in, W_in)
Output:
Out shape: (N, C, H_out, W_out)
Mask shape: (N, C, H_out, W_out)
where
H_out = (H_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
W_out = (W_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
)DOC"
);
}
};
...
...
@@ -146,18 +159,18 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input tensor of pooling operator. "
"
(Tensor)
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."
"
(Tensor)
The output tensor of pooling operator."
"The format of output tensor is also 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
(
"Mask"
,
"The Mask tensor of pooling operator."
"
(Tensor)
The Mask tensor of pooling operator."
"The format of output tensor is also NCDHW."
"Where N is batch size, C is the number of channels, D, H and W "
"is the depth, height and width of image."
...
...
@@ -165,7 +178,7 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr
<
std
::
vector
<
int
>>
(
"ksize"
,
"The pooling size(depth, height, width) of pooling operator."
"The pooling
window
size(depth, height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."
);
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
...
...
@@ -196,6 +209,18 @@ Input(X) and output(Out, Mask) are in NCDHW format. Where N is batch
size, C is the number of channels, D, H and W is the depth, height and
width of feature. Parameters(ksize, strides, paddings) are three elements.
These three elements represent depth, height and width, respectively.
The input(X) size and output(Out, Mask) size may be different.
Example:
Input:
X shape: (N, C, D_in, H_in, W_in)
Output:
Out shape: (N, C, D_out, H_out, W_out)
Mask shape: (N, C, D_out, H_out, W_out)
where
D_out = (D_in - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
H_out = (H_in - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
W_out = (W_in - ksize[2] + 2 * paddings[2]) / strides[2] + 1;
)DOC"
);
}
};
...
...
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