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
机器未来
Paddle
提交
1b01f1ea
P
Paddle
项目概览
机器未来
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
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
1
Issue
1
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
提交
1b01f1ea
编写于
9月 19, 2017
作者:
L
Luo Tao
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
implement framework of seq_pool_op and its unitest
上级
d4d4580d
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
139 addition
and
53 deletion
+139
-53
paddle/operators/sequence_pool_op.cc
paddle/operators/sequence_pool_op.cc
+41
-20
paddle/operators/sequence_pool_op.cu
paddle/operators/sequence_pool_op.cu
+4
-5
paddle/operators/sequence_pool_op.h
paddle/operators/sequence_pool_op.h
+45
-14
python/paddle/v2/framework/tests/test_seq_pool.py
python/paddle/v2/framework/tests/test_seq_pool.py
+49
-14
未找到文件。
paddle/operators/sequence_
avg_
pool_op.cc
→
paddle/operators/sequence_pool_op.cc
浏览文件 @
1b01f1ea
...
@@ -12,22 +12,22 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
...
@@ -12,22 +12,22 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
See the License for the specific language governing permissions and
limitations under the License. */
limitations under the License. */
#include "paddle/operators/sequence_
avg_
pool_op.h"
#include "paddle/operators/sequence_pool_op.h"
namespace
paddle
{
namespace
paddle
{
namespace
operators
{
namespace
operators
{
class
Sequence
Avg
PoolOp
:
public
framework
::
OperatorWithKernel
{
class
SequencePoolOp
:
public
framework
::
OperatorWithKernel
{
public:
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE_NOT_NULL
(
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
"X"
),
ctx
.
InputVar
(
"X"
),
"Input(X) of SequenceAvg
PoolOp should not be null."
);
"Input(X) of Sequence
PoolOp should not be null."
);
PADDLE_ENFORCE_NOT_NULL
(
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
OutputVar
(
"Out"
),
ctx
.
OutputVar
(
"Out"
),
"Output(Out) of Sequence
Avg
PoolOp should not be null."
);
"Output(Out) of SequencePoolOp should not be null."
);
auto
*
x
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
*
x
=
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
dims
=
x
->
dims
();
auto
dims
=
x
->
dims
();
...
@@ -42,21 +42,44 @@ class SequenceAvgPoolOp : public framework::OperatorWithKernel {
...
@@ -42,21 +42,44 @@ class SequenceAvgPoolOp : public framework::OperatorWithKernel {
}
}
};
};
class
Sequence
Avg
PoolOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
class
SequencePoolOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
public:
Sequence
Avg
PoolOpMaker
(
framework
::
OpProto
*
proto
,
SequencePoolOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"Input of SequenceAvgPoolOp."
);
AddInput
(
"X"
,
"A LoDTensor, the variable-length input of SequencePoolOp"
);
AddOutput
(
"Out"
,
"The output of SequenceAvgPoolOp."
);
AddOutput
(
"Out"
,
"A LoDTensor, the variable-length output of SequencePoolOp."
);
AddAttr
<
int
>
(
"strategy"
,
"(int, default AVERAGE) the pooling strategy of SequencePoolOp."
)
.
SetDefault
(
AVERAGE
)
.
InEnum
({
AVERAGE
,
SUM
,
SQRT
,
MAX
,
LAST
,
FIRST
});
AddComment
(
R"DOC(
AddComment
(
R"DOC(
SequenceAvgPoolOp averages features of all time-steps of each instance.
SequencePoolOp pools features of all time-steps of each instance.
More detailed comments will be added later.
For a mini-batch of 3 variable lengths sentences, containing 2, 3, and 2 words:
X = [[1, 3], [2, 4, 6], [5, 1]],
and X->lod()[0] = [0, 2, 5, 7]
then, for different strategy, we get:
- AVERAGE: Out = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
- SUM: Out = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
- SQRT: Out = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2), 6.93=(2+4+6)/sqrt(3),
4.24=(5+1)/sqrt(2)
- MAX: Out = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
- LAST: Out = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
- FIRST: Out = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
and X->lod() is nullptr.
)DOC"
);
)DOC"
);
}
}
};
};
class
Sequence
Avg
PoolGradOp
:
public
framework
::
OperatorWithKernel
{
class
SequencePoolGradOp
:
public
framework
::
OperatorWithKernel
{
public:
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
...
@@ -84,12 +107,10 @@ class SequenceAvgPoolGradOp : public framework::OperatorWithKernel {
...
@@ -84,12 +107,10 @@ class SequenceAvgPoolGradOp : public framework::OperatorWithKernel {
}
// namespace paddle
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
sequence_avg_pool
,
ops
::
SequenceAvgPoolOp
,
REGISTER_OP
(
sequence_pool
,
ops
::
SequencePoolOp
,
ops
::
SequencePoolOpMaker
,
ops
::
SequenceAvgPoolOpMaker
,
sequence_avg_pool_grad
,
sequence_pool_grad
,
ops
::
SequencePoolGradOp
);
ops
::
SequenceAvgPoolGradOp
);
REGISTER_OP_CPU_KERNEL
(
REGISTER_OP_CPU_KERNEL
(
sequence_avg_pool
,
sequence_pool
,
ops
::
SequencePoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
SequenceAvgPoolKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
REGISTER_OP_CPU_KERNEL
(
sequence_
avg_
pool_grad
,
sequence_pool_grad
,
ops
::
Sequence
Avg
PoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
SequencePoolGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/sequence_
avg_
pool_op.cu
→
paddle/operators/sequence_pool_op.cu
浏览文件 @
1b01f1ea
...
@@ -14,12 +14,11 @@
...
@@ -14,12 +14,11 @@
#define EIGEN_USE_GPU
#define EIGEN_USE_GPU
#include "paddle/operators/sequence_
avg_
pool_op.h"
#include "paddle/operators/sequence_pool_op.h"
namespace
ops
=
paddle
::
operators
;
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
REGISTER_OP_GPU_KERNEL
(
sequence_avg_pool
,
sequence_pool
,
ops
::
SequencePoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
SequenceAvgPoolKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
REGISTER_OP_GPU_KERNEL
(
sequence_
avg_
pool_grad
,
sequence_pool_grad
,
ops
::
Sequence
Avg
PoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
ops
::
SequencePoolGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/sequence_
avg_
pool_op.h
→
paddle/operators/sequence_pool_op.h
浏览文件 @
1b01f1ea
...
@@ -28,54 +28,85 @@ template <typename T, int MajorType = Eigen::RowMajor,
...
@@ -28,54 +28,85 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename
IndexType
=
Eigen
::
DenseIndex
>
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
using
EigenMatrix
=
framework
::
EigenMatrix
<
T
,
MajorType
,
IndexType
>
;
enum
SeqPoolType
{
AVERAGE
=
0
,
SUM
=
1
,
SQRT
=
2
,
// square_root_n
MAX
=
3
,
LAST
=
4
,
FIRST
=
5
};
template
<
typename
Place
,
typename
T
>
template
<
typename
Place
,
typename
T
>
class
Sequence
Avg
PoolKernel
:
public
framework
::
OpKernel
{
class
SequencePoolKernel
:
public
framework
::
OpKernel
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
int
strategy
=
context
.
Attr
<
int
>
(
"strategy"
);
auto
dims
=
in
->
dims
();
auto
dims
=
in
->
dims
();
auto
lod
=
in
->
lod
();
auto
lod
=
in
->
lod
()
[
0
]
;
int64_t
w
=
in
->
numel
()
/
dims
[
0
];
int64_t
w
=
in
->
numel
()
/
dims
[
0
];
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
[
0
]
.
size
())
-
1
;
++
i
)
{
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
.
size
())
-
1
;
++
i
)
{
Tensor
in_t
=
in
->
Slice
<
T
>
(
static_cast
<
int
>
(
lod
[
0
][
i
]),
Tensor
in_t
=
static_cast
<
int
>
(
lod
[
0
]
[
i
+
1
]));
in
->
Slice
<
T
>
(
static_cast
<
int
>
(
lod
[
i
]),
static_cast
<
int
>
(
lod
[
i
+
1
]));
Tensor
out_t
=
out
->
Slice
<
T
>
(
i
,
i
+
1
);
Tensor
out_t
=
out
->
Slice
<
T
>
(
i
,
i
+
1
);
int64_t
h
=
static_cast
<
int64_t
>
(
lod
[
0
][
i
+
1
]
-
lod
[
0
]
[
i
]);
int64_t
h
=
static_cast
<
int64_t
>
(
lod
[
i
+
1
]
-
lod
[
i
]);
auto
in_e
=
EigenMatrix
<
T
>::
From
(
in_t
,
framework
::
make_ddim
({
h
,
w
}));
auto
in_e
=
EigenMatrix
<
T
>::
From
(
in_t
,
framework
::
make_ddim
({
h
,
w
}));
auto
out_e
=
EigenVector
<
T
>::
Flatten
(
out_t
);
auto
out_e
=
EigenVector
<
T
>::
Flatten
(
out_t
);
out_e
.
device
(
place
)
=
in_e
.
mean
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
switch
(
strategy
)
{
case
AVERAGE
:
out_e
.
device
(
place
)
=
in_e
.
mean
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
break
;
case
SUM
:
out_e
.
device
(
place
)
=
in_e
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
break
;
default:
LOG
(
FATAL
)
<<
"unsupported pooling strategy"
;
}
}
}
}
}
};
};
template
<
typename
Place
,
typename
T
>
template
<
typename
Place
,
typename
T
>
class
Sequence
Avg
PoolGradKernel
:
public
framework
::
OpKernel
{
class
SequencePoolGradKernel
:
public
framework
::
OpKernel
{
public:
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
in
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
out_g
=
context
.
Input
<
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
in_g
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
*
in_g
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
int
strategy
=
context
.
Attr
<
int
>
(
"strategy"
);
auto
dims
=
in
->
dims
();
auto
dims
=
in
->
dims
();
auto
lod
=
in
->
lod
();
auto
lod
=
in
->
lod
()
[
0
]
;
int64_t
w
=
in
->
numel
()
/
dims
[
0
];
int64_t
w
=
in
->
numel
()
/
dims
[
0
];
in_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
in_g
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
[
0
]
.
size
())
-
1
;
++
i
)
{
for
(
int
i
=
0
;
i
<
static_cast
<
int
>
(
lod
.
size
())
-
1
;
++
i
)
{
auto
in_g_t
=
in_g
->
Slice
<
T
>
(
static_cast
<
int
>
(
lod
[
0
][
i
]),
auto
in_g_t
=
in_g
->
Slice
<
T
>
(
static_cast
<
int
>
(
lod
[
i
]),
static_cast
<
int
>
(
lod
[
0
][
i
+
1
]));
static_cast
<
int
>
(
lod
[
i
+
1
]));
auto
out_g_t
=
out_g
->
Slice
<
T
>
(
i
,
i
+
1
);
auto
out_g_t
=
out_g
->
Slice
<
T
>
(
i
,
i
+
1
);
int64_t
h
=
static_cast
<
int64_t
>
(
lod
[
0
][
i
+
1
]
-
lod
[
0
]
[
i
]);
int64_t
h
=
static_cast
<
int64_t
>
(
lod
[
i
+
1
]
-
lod
[
i
]);
auto
in_g_e
=
EigenMatrix
<
T
>::
From
(
in_g_t
,
{
h
,
w
});
auto
in_g_e
=
EigenMatrix
<
T
>::
From
(
in_g_t
,
{
h
,
w
});
auto
out_g_e
=
EigenMatrix
<
T
>::
From
(
out_g_t
,
{
1
,
w
});
auto
out_g_e
=
EigenMatrix
<
T
>::
From
(
out_g_t
,
{
1
,
w
});
Eigen
::
DSizes
<
int
,
2
>
bcast
(
h
,
1
);
Eigen
::
DSizes
<
int
,
2
>
bcast
(
h
,
1
);
in_g_e
.
device
(
place
)
=
(
out_g_e
/
static_cast
<
T
>
(
h
)).
broadcast
(
bcast
);
switch
(
strategy
)
{
case
AVERAGE
:
in_g_e
.
device
(
place
)
=
(
out_g_e
/
static_cast
<
T
>
(
h
)).
broadcast
(
bcast
);
break
;
case
SUM
:
in_g_e
.
device
(
place
)
=
(
out_g_e
).
broadcast
(
bcast
);
break
;
default:
LOG
(
FATAL
)
<<
"unsupported pooling strategy"
;
}
}
}
}
}
};
};
...
...
python/paddle/v2/framework/tests/test_seq_pool.py
浏览文件 @
1b01f1ea
...
@@ -3,20 +3,37 @@ import numpy as np
...
@@ -3,20 +3,37 @@ import numpy as np
from
op_test
import
OpTest
from
op_test
import
OpTest
class
TestSeqAvgPool1D
(
OpTest
):
class
SeqPoolType
(
OpTest
):
def
setUp
(
self
):
AVERAGE
=
0
self
.
op_type
=
'sequence_avg_pool'
SUM
=
1
SQRT
=
2
MAX
=
3
LAST
=
4
FIRST
=
5
class
TestSeqAvgPool
(
OpTest
):
def
set_data
(
self
):
self
.
op_type
=
'sequence_pool'
# one level, batch size is 4
# one level, batch size is 4
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
23
]).
astype
(
'float32'
)
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
23
]).
astype
(
'float32'
)
lod
=
[[
0
,
4
,
5
,
8
,
11
]]
lod
=
[[
0
,
4
,
5
,
8
,
11
]]
self
.
inputs
=
{
'X'
:
(
x
,
lod
)}
out
=
np
.
zeros
((
4
,
23
)).
astype
(
'float32'
)
out
=
np
.
zeros
((
4
,
23
)).
astype
(
'float32'
)
self
.
outputs
=
{
'Out'
:
out
}
def
compute
(
self
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
AVERAGE
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
for
i
in
range
(
4
):
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
out
[
i
]
=
sub_x
.
mean
(
axis
=
0
)
out
[
i
]
=
sub_x
.
mean
(
axis
=
0
)
self
.
inputs
=
{
'X'
:
(
x
,
lod
)}
def
setUp
(
self
):
self
.
outputs
=
{
'Out'
:
out
}
self
.
set_data
()
self
.
compute
()
def
test_check_output
(
self
):
def
test_check_output
(
self
):
self
.
check_output
()
self
.
check_output
()
...
@@ -25,26 +42,44 @@ class TestSeqAvgPool1D(OpTest):
...
@@ -25,26 +42,44 @@ class TestSeqAvgPool1D(OpTest):
self
.
check_grad
([
"X"
],
"Out"
)
self
.
check_grad
([
"X"
],
"Out"
)
class
TestSeqAvgPool2D
(
OpTest
):
class
TestSeqAvgPool2D
(
TestSeqAvgPool
):
def
set
Up
(
self
):
def
set
_data
(
self
):
self
.
op_type
=
'sequence_
avg_
pool'
self
.
op_type
=
'sequence_pool'
# one level, batch size is 4
# one level, batch size is 4
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
3
,
17
]).
astype
(
'float32'
)
x
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
3
,
17
]).
astype
(
'float32'
)
lod
=
[[
0
,
4
,
5
,
8
,
13
]]
lod
=
[[
0
,
4
,
5
,
8
,
13
]]
self
.
inputs
=
{
'X'
:
(
x
,
lod
)}
out
=
np
.
zeros
((
4
,
3
,
17
)).
astype
(
'float32'
)
out
=
np
.
zeros
((
4
,
3
,
17
)).
astype
(
'float32'
)
self
.
outputs
=
{
'Out'
:
out
}
def
compute
(
self
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
AVERAGE
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
for
i
in
range
(
4
):
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
out
[
i
]
=
np
.
reshape
(
sub_x
.
mean
(
axis
=
0
),
(
3
,
17
))
out
[
i
]
=
np
.
reshape
(
sub_x
.
mean
(
axis
=
0
),
(
3
,
17
))
self
.
inputs
=
{
'X'
:
(
x
,
lod
)}
self
.
outputs
=
{
'Out'
:
out
}
def
test_check_output
(
self
):
class
TestSeqSumPool
(
TestSeqAvgPool
):
self
.
check_output
()
def
compute
(
self
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
SUM
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:]
out
[
i
]
=
sub_x
.
sum
(
axis
=
0
)
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Out"
)
class
TestSeqSumPool2D
(
TestSeqAvgPool2D
):
def
compute
(
self
):
self
.
attrs
=
{
'strategy'
:
SeqPoolType
.
SUM
}
x
,
lod
=
self
.
inputs
[
'X'
]
out
=
self
.
outputs
[
'Out'
]
for
i
in
range
(
4
):
sub_x
=
np
.
reshape
(
x
[
lod
[
0
][
i
]:
lod
[
0
][
i
+
1
],
:],
(
-
1
,
3
*
17
))
out
[
i
]
=
np
.
reshape
(
sub_x
.
sum
(
axis
=
0
),
(
3
,
17
))
if
__name__
==
'__main__'
:
if
__name__
==
'__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