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
马赛克医院
Paddle
提交
bf33b191
P
Paddle
项目概览
马赛克医院
/
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
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
bf33b191
编写于
1月 18, 2018
作者:
D
dangqingqing
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add bipartite matching operator and unit testing.
上级
38c61053
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
278 addition
and
0 deletion
+278
-0
paddle/operators/bipartite_match_op.cc
paddle/operators/bipartite_match_op.cc
+178
-0
python/paddle/v2/fluid/tests/test_bipartite_match_op.py
python/paddle/v2/fluid/tests/test_bipartite_match_op.py
+100
-0
未找到文件。
paddle/operators/bipartite_match_op.cc
0 → 100644
浏览文件 @
bf33b191
/* 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/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
LoDTensor
=
framework
::
LoDTensor
;
class
BipartiteMatchOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"DisMat"
),
"Input(DisMat) of BipartiteMatch should not be null."
);
auto
dims
=
ctx
->
GetInputDim
(
"DisMat"
);
PADDLE_ENFORCE_EQ
(
dims
.
size
(),
2
,
"The rank of Input(DisMat) must be 2."
);
ctx
->
SetOutputDim
(
"ColToRowMatchIndices"
,
dims
);
ctx
->
SetOutputDim
(
"ColToRowMatchDis"
,
dims
);
}
};
template
<
typename
T
>
class
BipartiteMatchKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
// The match_indices must be initialized to -1 at first.
// The match_dis must be initialized to 0 at first.
void
BipartiteMatch
(
const
Tensor
&
dis
,
int
*
match_indices
,
T
*
match_dis
)
const
{
int64_t
row
=
dis
.
dims
()[
0
];
int64_t
col
=
dis
.
dims
()[
1
];
auto
*
dis_data
=
dis
.
data
<
T
>
();
std
::
vector
<
int
>
row_pool
;
for
(
int
i
=
0
;
i
<
row
;
++
i
)
{
row_pool
.
push_back
(
i
);
}
while
(
row_pool
.
size
()
>
0
)
{
int
max_idx
=
-
1
;
int
max_row_idx
=
-
1
;
T
max_dis
=
-
1
;
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
if
(
match_indices
[
j
]
!=
-
1
)
{
continue
;
}
for
(
int
k
=
0
;
k
<
row_pool
.
size
();
++
k
)
{
int
m
=
row_pool
[
k
];
// distance is 0 between m-th row and j-th column
if
(
dis_data
[
m
*
col
+
j
]
<
1e-6
)
{
continue
;
}
if
(
dis_data
[
m
*
col
+
j
]
>
max_dis
)
{
max_idx
=
j
;
max_row_idx
=
m
;
max_dis
=
dis_data
[
m
*
col
+
j
];
}
}
}
if
(
max_idx
==
-
1
)
{
// Cannot find good match.
break
;
}
else
{
PADDLE_ENFORCE_EQ
(
match_indices
[
max_idx
],
-
1
);
match_indices
[
max_idx
]
=
max_row_idx
;
match_dis
[
max_idx
]
=
max_dis
;
// Erase the row index.
row_pool
.
erase
(
std
::
find
(
row_pool
.
begin
(),
row_pool
.
end
(),
max_row_idx
));
}
}
}
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
dis_mat
=
context
.
Input
<
LoDTensor
>
(
"DisMat"
);
auto
*
match_indices
=
context
.
Output
<
Tensor
>
(
"ColToRowMatchIndices"
);
auto
*
match_dis
=
context
.
Output
<
Tensor
>
(
"ColToRowMatchDis"
);
auto
&
dev_ctx
=
context
.
device_context
<
platform
::
CPUDeviceContext
>
();
auto
col
=
dis_mat
->
dims
()[
1
];
int64_t
n
=
dis_mat
->
lod
().
size
()
==
0
?
1
:
static_cast
<
int64_t
>
(
dis_mat
->
lod
().
back
().
size
()
-
1
);
match_indices
->
mutable_data
<
int
>
({
n
,
col
},
context
.
GetPlace
());
match_dis
->
mutable_data
<
T
>
({
n
,
col
},
context
.
GetPlace
());
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
int
>
iset
;
iset
(
dev_ctx
,
match_indices
,
static_cast
<
int
>
(
-
1
));
math
::
SetConstant
<
platform
::
CPUDeviceContext
,
T
>
tset
;
tset
(
dev_ctx
,
match_dis
,
static_cast
<
T
>
(
0
));
int
*
indices
=
match_indices
->
data
<
int
>
();
T
*
dis
=
match_dis
->
data
<
T
>
();
if
(
n
==
1
)
{
BipartiteMatch
(
*
dis_mat
,
indices
,
dis
);
}
else
{
auto
lod
=
dis_mat
->
lod
().
back
();
for
(
size_t
i
=
0
;
i
<
lod
.
size
()
-
1
;
++
i
)
{
Tensor
one_ins
=
dis_mat
->
Slice
(
lod
[
i
],
lod
[
i
+
1
]);
BipartiteMatch
(
one_ins
,
indices
+
i
*
col
,
dis
+
i
*
col
);
}
}
}
};
class
BipartiteMatchOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
BipartiteMatchOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"DisMat"
,
"(LoDTensor or Tensor) this input is a 2-D LoDTensor with shape "
"[K, M]. It is pair-wise distance matrix between the entities "
"represented by each row and each column. For example, assumed one "
"entity is A with shape [K], another entity is B with shape [M]. The "
"DisMat[i][j] is the distance between A[i] and B[j]. The bigger "
"the distance is, the more similar the pairs are. Please note, "
"This tensor can contain LoD information to represent a batch of "
"inputs. One instance of this batch can contain different numbers of "
"entities."
);
AddOutput
(
"ColToRowMatchIndices"
,
"(Tensor) A 2-D Tensor with shape [N, M] in int type. "
"N is the batch size. If ColToRowMatchIndices[i][j] is -1, it "
"means B[j] does not match any entity in i-th instance. "
"Otherwise, it means B[j] is matched to row "
"RowToColMatchIndices[i][j] in i-th instance. The row number of "
"i-th instance is saved in RowToColMatchIndices[i][j]."
);
AddOutput
(
"ColToRowMatchDis"
,
"(Tensor) A 2-D Tensor with shape [N, M] in float type. "
"N is batch size. If ColToRowMatchIndices[i][j] is -1, "
"ColToRowMatchDis[i][j] is also -1.0. Otherwise, assumed "
"RowToColMatchIndices[i][j] = d, and the row offsets of each "
"instance are called LoD. Then "
"ColToRowMatchDis[i][j] = DisMat[d+LoD[i]][j]"
);
AddComment
(
R"DOC(
This operator is a greedy bipartite matching algorithm, which is used to
obtain the matching with the (greedy) maximum distance based on the input
distance matrix. There are two outputs to save matched indices and distance.
And this operator only calculate matched indices from column to row.
A simple description, this algothrim matched the best (maximum distance)
row entity to the column entity and the matched indices are not duplicated
in each row of ColToRowMatchIndices. If the column entity is not matched
any row entity, set -1 in ColToRowMatchIndices.
Please note that the input DisMat can be LoDTensor (with LoD) or Tensor.
If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size.
If Tensor, the height of ColToRowMatchIndices is 1.
)DOC"
);
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
bipartite_match
,
ops
::
BipartiteMatchOp
,
ops
::
BipartiteMatchOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
bipartite_match
,
ops
::
BipartiteMatchKernel
<
float
>
,
ops
::
BipartiteMatchKernel
<
double
>
);
python/paddle/v2/fluid/tests/test_bipartite_match_op.py
0 → 100644
浏览文件 @
bf33b191
# Copyright (c) 2018 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.
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
bipartite_match
(
distance
,
match_indices
,
match_dis
):
"""Bipartite Matching algorithm.
Arg:
distance (numpy.array) : The distance of two entries with shape [M, N].
match_indices (numpy.array): the matched indices from column to row
with shape [1, N], it must be initialized to -1.
match_dis (numpy.array): The matched distance from column to row
with shape [1, N], it must be initialized to 0.
"""
match_pair
=
[]
row
,
col
=
distance
.
shape
for
i
in
range
(
row
):
for
j
in
range
(
col
):
match_pair
.
append
((
i
,
j
,
distance
[
i
][
j
]))
match_sorted
=
sorted
(
match_pair
,
key
=
lambda
tup
:
tup
[
2
],
reverse
=
True
)
row_indices
=
-
1
*
np
.
ones
((
row
,
),
dtype
=
np
.
int
)
idx
=
0
for
i
,
j
,
dis
in
match_sorted
:
if
idx
>=
row
:
break
if
match_indices
[
j
]
==
-
1
and
row_indices
[
i
]
==
-
1
and
dis
>
0
:
match_indices
[
j
]
=
i
row_indices
[
i
]
=
j
match_dis
[
j
]
=
dis
idx
+=
1
def
batch_bipartite_match
(
distance
,
lod
):
"""Bipartite Matching algorithm for batch input.
Arg:
distance (numpy.array) : The distance of two entries with shape [M, N].
lod (list of int): The offsets of each input in this batch.
"""
n
=
len
(
lod
)
-
1
m
=
distance
.
shape
[
1
]
match_indices
=
-
1
*
np
.
ones
((
n
,
m
),
dtype
=
np
.
int
)
match_dis
=
np
.
zeros
((
n
,
m
),
dtype
=
np
.
float32
)
for
i
in
range
(
len
(
lod
)
-
1
):
bipartite_match
(
distance
[
lod
[
i
]:
lod
[
i
+
1
],
:],
match_indices
[
i
,
:],
match_dis
[
i
,
:])
return
match_indices
,
match_dis
class
TestBipartiteMatchOpForWithLoD
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
'bipartite_match'
lod
=
[[
0
,
5
,
11
,
23
]]
dis
=
np
.
random
.
random
((
23
,
217
)).
astype
(
'float32'
)
match_indices
,
match_dis
=
batch_bipartite_match
(
dis
,
lod
[
0
])
self
.
inputs
=
{
'DisMat'
:
(
dis
,
lod
)}
self
.
outputs
=
{
'ColToRowMatchIndices'
:
(
match_indices
),
'ColToRowMatchDis'
:
(
match_dis
),
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestBipartiteMatchOpWithoutLoD
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
'bipartite_match'
lod
=
[[
0
,
8
]]
dis
=
np
.
random
.
random
((
8
,
17
)).
astype
(
'float32'
)
match_indices
,
match_dis
=
batch_bipartite_match
(
dis
,
lod
[
0
])
self
.
inputs
=
{
'DisMat'
:
dis
}
self
.
outputs
=
{
'ColToRowMatchIndices'
:
(
match_indices
),
'ColToRowMatchDis'
:
(
match_dis
),
}
def
test_check_output
(
self
):
self
.
check_output
()
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