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体验新版 GitCode,发现更多精彩内容 >>
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a7f94ec7
编写于
10月 25, 2018
作者:
B
barrierye
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
add similarity_focus op
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4 changed file
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and
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+475
-0
paddle/fluid/operators/similarity_focus_op.cc
paddle/fluid/operators/similarity_focus_op.cc
+83
-0
paddle/fluid/operators/similarity_focus_op.h
paddle/fluid/operators/similarity_focus_op.h
+168
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+56
-0
python/paddle/fluid/tests/unittests/test_similarity_focus_op.py
.../paddle/fluid/tests/unittests/test_similarity_focus_op.py
+168
-0
未找到文件。
paddle/fluid/operators/similarity_focus_op.cc
0 → 100644
浏览文件 @
a7f94ec7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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/fluid/operators/similarity_focus_op.h"
namespace
paddle
{
namespace
operators
{
class
SimilarityFocusOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor, default Tensor<float>), a 4-D tensor with shape,"
" [BatchSize, X, Y, Z]"
);
AddOutput
(
"Out"
,
"(Tensor, default Tensor<float>), the similarity focus mask"
" with the same shape of input X."
);
AddAttr
<
int
>
(
"axis"
,
"(int32), indicating the dimension to be select. It can"
" only be 1, 2, or 3."
);
AddAttr
<
std
::
vector
<
int
>>
(
"indexes"
,
"(std::vector<int32>), indicating the indexes"
" of the selected dimension."
);
AddComment
(
R"DOC(
SimilarityFocus Operator.
Generate a similarity focus mask with the same shape of input using the following method:
1. Extract the 3-D matrix(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this casr, if the shape of input X
is (BatchSize, A, B, C), the shape of matrix T is (BatchSize, B, C).
2. For each index, find the largest numbers in the matrix T, so that the same
row and same column has at most one number(obviously there will be min(B, C)
numbers), and mark the corresponding position of the 3-D similarity focus mask
as 1, otherwise as 0. Do elementwise-or for each index.
3. Broadcast the 3-D similarity focus mask to the same shape of input X.
Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_
)DOC"
);
}
};
class
SimilarityFocusOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) should be not null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) should be not null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
4
,
"Input(X)'s rank should be 4."
);
ctx
->
SetOutputDim
(
"Out"
,
x_dims
);
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
type
()),
platform
::
CPUPlace
());
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
similarity_focus
,
ops
::
SimilarityFocusOp
,
ops
::
SimilarityFocusOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
similarity_focus
,
ops
::
SimilarityFocusKernel
<
float
>
,
ops
::
SimilarityFocusKernel
<
double
>
);
paddle/fluid/operators/similarity_focus_op.h
0 → 100644
浏览文件 @
a7f94ec7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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 <algorithm>
#include <cstring>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
>
class
SimilarityFocusKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
Tensor
*
out
=
context
.
Output
<
Tensor
>
(
"Out"
);
const
Tensor
*
x
=
context
.
Input
<
Tensor
>
(
"X"
);
T
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
const
T
*
x_data
=
x
->
data
<
T
>
();
int
axis
=
context
.
Attr
<
int
>
(
"axis"
);
std
::
vector
<
int
>
indexes
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"indexes"
);
int64_t
batch_size
=
x
->
dims
()[
0
];
int64_t
dim
[
4
];
for
(
int
i
=
1
;
i
<=
3
;
++
i
)
{
dim
[
i
]
=
x
->
dims
()[
i
];
}
if
(
indexes
.
size
()
<
1
)
{
PADDLE_THROW
(
"Indexes' size can not be 0."
);
}
for
(
auto
index
:
indexes
)
{
if
(
dim
[
axis
]
<
index
)
{
PADDLE_THROW
(
"Index exceeds tensor shape limit."
);
}
}
int64_t
array_size
=
1
;
for
(
int
i
=
1
;
i
<=
3
;
++
i
)
{
if
(
i
!=
axis
)
{
array_size
*=
dim
[
i
];
}
}
std
::
vector
<
std
::
pair
<
T
,
int64_t
>>
array
(
array_size
);
bool
(
*
cmp
)(
std
::
pair
<
T
,
int64_t
>
,
std
::
pair
<
T
,
int64_t
>
)
=
[](
std
::
pair
<
T
,
int64_t
>
x
,
std
::
pair
<
T
,
int64_t
>
y
)
{
return
x
.
first
>
y
.
first
;
};
int64_t
(
*
compute_index
)(
int64_t
*
,
int
,
int
,
int
,
int
)
=
[](
int64_t
*
dim
,
int
d1
,
int
d2
,
int
d3
,
int
d4
)
{
return
d1
*
dim
[
1
]
*
dim
[
2
]
*
dim
[
3
]
+
d2
*
dim
[
2
]
*
dim
[
3
]
+
d3
*
dim
[
3
]
+
d4
;
};
memset
(
out_data
,
0
,
sizeof
(
T
)
*
batch_size
*
dim
[
1
]
*
dim
[
2
]
*
dim
[
3
]);
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
for
(
auto
index
:
indexes
)
{
if
(
axis
==
1
)
{
for
(
int
j
=
0
;
j
<
dim
[
2
];
++
j
)
{
for
(
int
k
=
0
;
k
<
dim
[
3
];
++
k
)
{
array
[
j
*
dim
[
3
]
+
k
]
=
std
::
make_pair
(
x_data
[
compute_index
(
dim
,
i
,
index
,
j
,
k
)],
j
*
dim
[
3
]
+
k
);
}
}
std
::
sort
(
array
.
begin
(),
array
.
end
(),
cmp
);
int
tag_num
=
0
;
std
::
vector
<
bool
>
tag2
(
dim
[
2
]),
tag3
(
dim
[
3
]);
for
(
auto
x
:
array
)
{
int
idx2
=
x
.
second
/
dim
[
3
];
int
idx3
=
x
.
second
%
dim
[
3
];
if
(
tag2
[
idx2
]
||
tag3
[
idx3
])
{
continue
;
}
tag_num
++
;
tag2
[
idx2
]
=
true
;
tag3
[
idx3
]
=
true
;
for
(
int
j
=
0
;
j
<
dim
[
1
];
++
j
)
{
out_data
[
compute_index
(
dim
,
i
,
j
,
idx2
,
idx3
)]
=
1
;
}
if
(
tag_num
==
std
::
min
(
dim
[
2
],
dim
[
3
]))
{
break
;
}
}
}
else
if
(
axis
==
2
)
{
for
(
int
j
=
0
;
j
<
dim
[
1
];
++
j
)
{
for
(
int
k
=
0
;
k
<
dim
[
3
];
++
k
)
{
array
[
j
*
dim
[
3
]
+
k
]
=
std
::
make_pair
(
x_data
[
compute_index
(
dim
,
i
,
j
,
index
,
k
)],
j
*
dim
[
3
]
+
k
);
}
}
std
::
sort
(
array
.
begin
(),
array
.
end
(),
cmp
);
int
tag_num
=
0
;
std
::
vector
<
bool
>
tag1
(
dim
[
1
]),
tag3
(
dim
[
3
]);
for
(
auto
x
:
array
)
{
int
idx1
=
x
.
second
/
dim
[
3
];
int
idx3
=
x
.
second
%
dim
[
3
];
if
(
tag1
[
idx1
]
||
tag3
[
idx3
])
{
continue
;
}
tag_num
++
;
tag1
[
idx1
]
=
true
;
tag3
[
idx3
]
=
true
;
for
(
int
j
=
0
;
j
<
dim
[
2
];
++
j
)
{
out_data
[
compute_index
(
dim
,
i
,
idx1
,
j
,
idx3
)]
=
1
;
}
if
(
tag_num
==
std
::
min
(
dim
[
1
],
dim
[
3
]))
{
break
;
}
}
}
else
if
(
axis
==
3
)
{
for
(
int
j
=
0
;
j
<
dim
[
1
];
++
j
)
{
for
(
int
k
=
0
;
k
<
dim
[
2
];
++
k
)
{
array
[
j
*
dim
[
2
]
+
k
]
=
std
::
make_pair
(
x_data
[
compute_index
(
dim
,
i
,
j
,
k
,
index
)],
j
*
dim
[
2
]
+
k
);
}
}
std
::
sort
(
array
.
begin
(),
array
.
end
(),
cmp
);
int
tag_num
=
0
;
std
::
vector
<
bool
>
tag1
(
dim
[
1
]),
tag2
(
dim
[
2
]);
for
(
auto
x
:
array
)
{
int
idx1
=
x
.
second
/
dim
[
2
];
int
idx2
=
x
.
second
%
dim
[
2
];
if
(
tag1
[
idx1
]
||
tag2
[
idx2
])
{
continue
;
}
tag_num
++
;
tag1
[
idx1
]
=
true
;
tag2
[
idx2
]
=
true
;
for
(
int
j
=
0
;
j
<
dim
[
3
];
++
j
)
{
out_data
[
compute_index
(
dim
,
i
,
idx1
,
idx2
,
j
)]
=
1
;
}
if
(
tag_num
==
std
::
min
(
dim
[
1
],
dim
[
2
]))
{
break
;
}
}
}
else
{
PADDLE_THROW
(
"Axis must be 1 or 2 or 3"
);
}
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/nn.py
浏览文件 @
a7f94ec7
...
...
@@ -155,6 +155,7 @@ __all__ = [
'sigmoid_cross_entropy_with_logits'
,
'maxout'
,
'affine_channel'
,
'similarity_focus'
,
]
...
...
@@ -7494,3 +7495,58 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
attrs
=
{
"data_layout"
:
data_layout
},
outputs
=
{
"Out"
:
out
})
return
out
def
similarity_focus
(
input
,
axis
,
indexes
,
name
=
None
):
"""
**SimilarityFocus Operator**
Generate a similarity focus mask with the same shape of input using the following method:
1. Extract the 3-D matrix(here the first dimension is BatchSize) corresponding
to the axis according to the indexes. For example, if axis=1 and indexes=[a],
it will get the matrix T=X[:, a, :, :]. In this casr, if the shape of input X
is (BatchSize, A, B, C), the shape of matrix T is (BatchSize, B, C).
2. For each index, find the largest numbers in the matrix T, so that the same
row and same column has at most one number(obviously there will be min(B, C)
numbers), and mark the corresponding position of the 3-D similarity focus mask
as 1, otherwise as 0. Do elementwise-or for each index.
3. Broadcast the 3-D similarity focus mask to the same shape of input X.
Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_
Args:
input(Variable): The input tensor variable(default float). It should
be a 4-D tensor with shape [BatchSize, A, B, C].
axis(int): Indicating the dimension to be select. It can only be
1, 2, or 3.
indexes(list): indicating the indexes of the selected dimension.
Returns:
Variable: A tensor variable with the same shape and same type
as the input.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[128, 13, 48, 48], dtype='float32')
x = fluid.layers.layer_norm(input=data, axis=1, indexes=[9, 10])
"""
helper
=
LayerHelper
(
'similarity_focus'
,
**
locals
())
# check attrs
if
isinstance
(
axis
,
int
)
is
False
:
raise
TypeError
(
"axis must be int type."
)
if
isinstance
(
indexes
,
list
)
is
False
:
raise
TypeError
(
"indexes must be list type."
)
if
axis
!=
1
and
axis
!=
2
and
axis
!=
3
:
raise
ValueError
(
"axis must be 1, 2 or 3."
)
if
len
(
indexes
)
==
0
:
raise
ValueError
(
"indexes can not be empty."
)
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'similarity_focus'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
"axis"
:
axis
,
"indexes"
:
indexes
})
return
out
python/paddle/fluid/tests/unittests/test_similarity_focus_op.py
0 → 100755
浏览文件 @
a7f94ec7
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
class
TestSimilarityFocusOp_axis1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
batch_size
=
3
x_dim
,
y_dim
,
z_dim
=
4
,
5
,
6
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
(
batch_size
,
x_dim
,
y_dim
,
z_dim
)).
astype
(
"float32"
),
}
self
.
attrs
=
{
'axis'
:
1
,
'indexes'
:
[
0
,
3
],
}
output
=
None
for
batch
in
range
(
batch_size
):
res
=
np
.
zeros
((
1
,
y_dim
,
z_dim
)).
astype
(
"float32"
).
reshape
(
-
1
)
for
index
in
self
.
attrs
[
'indexes'
]:
channel
=
self
.
inputs
[
'X'
][
batch
,
index
,
:,
:].
reshape
(
-
1
).
copy
(
)
tag1
=
[
0
for
i
in
range
(
y_dim
)]
tag2
=
[
0
for
i
in
range
(
z_dim
)]
cnt
=
0
for
i
in
range
(
channel
.
size
):
index
=
channel
.
argmax
()
idx1
=
index
/
z_dim
idx2
=
index
%
z_dim
if
tag1
[
idx1
]
+
tag2
[
idx2
]
==
0
:
tag1
[
idx1
]
=
1
tag2
[
idx2
]
=
1
res
[
index
]
=
1
cnt
+=
1
if
cnt
==
min
(
y_dim
,
z_dim
):
break
channel
[
index
]
=
-
1
res
=
res
.
reshape
(
1
,
y_dim
,
z_dim
)
res
=
res
.
repeat
([
x_dim
],
axis
=
0
)
res
=
res
.
reshape
(
1
,
x_dim
,
y_dim
,
z_dim
)
if
output
is
not
None
:
output
=
np
.
concatenate
((
output
,
res
),
axis
=
0
)
else
:
output
=
res
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestSimilarityFocusOp_axis2
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
batch_size
=
6
x_dim
,
y_dim
,
z_dim
=
7
,
8
,
9
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
(
batch_size
,
x_dim
,
y_dim
,
z_dim
)).
astype
(
"float32"
),
}
self
.
attrs
=
{
'axis'
:
2
,
'indexes'
:
[
0
,
3
,
5
],
}
output
=
None
for
batch
in
range
(
batch_size
):
res
=
np
.
zeros
((
x_dim
,
1
,
z_dim
)).
astype
(
"float32"
).
reshape
(
-
1
)
for
index
in
self
.
attrs
[
'indexes'
]:
channel
=
self
.
inputs
[
'X'
][
batch
,
:,
index
,
:].
reshape
(
-
1
).
copy
(
)
tag1
=
[
0
for
i
in
range
(
x_dim
)]
tag2
=
[
0
for
i
in
range
(
z_dim
)]
cnt
=
0
for
i
in
range
(
channel
.
size
):
index
=
channel
.
argmax
()
idx1
=
index
/
z_dim
idx2
=
index
%
z_dim
if
tag1
[
idx1
]
+
tag2
[
idx2
]
==
0
:
tag1
[
idx1
]
=
1
tag2
[
idx2
]
=
1
res
[
index
]
=
1
cnt
+=
1
if
cnt
==
min
(
x_dim
,
z_dim
):
break
channel
[
index
]
=
-
1
res
=
res
.
reshape
(
x_dim
,
1
,
z_dim
)
res
=
res
.
repeat
([
y_dim
],
axis
=
1
)
res
=
res
.
reshape
(
1
,
x_dim
,
y_dim
,
z_dim
)
if
output
is
not
None
:
output
=
np
.
concatenate
((
output
,
res
),
axis
=
0
)
else
:
output
=
res
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
class
TestSimilarityFocusOp_axis3
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
batch_size
=
64
x_dim
,
y_dim
,
z_dim
=
48
,
48
,
13
self
.
inputs
=
{
'X'
:
np
.
random
.
random
(
(
batch_size
,
x_dim
,
y_dim
,
z_dim
)).
astype
(
"float32"
),
}
self
.
attrs
=
{
'axis'
:
3
,
'indexes'
:
[
0
,
2
,
7
,
9
],
}
output
=
None
for
batch
in
range
(
batch_size
):
res
=
np
.
zeros
((
x_dim
,
y_dim
,
1
)).
astype
(
"float32"
).
reshape
(
-
1
)
for
index
in
self
.
attrs
[
'indexes'
]:
channel
=
self
.
inputs
[
'X'
][
batch
,
:,
:,
index
].
reshape
(
-
1
).
copy
(
)
tag1
=
[
0
for
i
in
range
(
x_dim
)]
tag2
=
[
0
for
i
in
range
(
y_dim
)]
cnt
=
0
for
i
in
range
(
channel
.
size
):
index
=
channel
.
argmax
()
idx1
=
index
/
y_dim
idx2
=
index
%
y_dim
if
tag1
[
idx1
]
+
tag2
[
idx2
]
==
0
:
tag1
[
idx1
]
=
1
tag2
[
idx2
]
=
1
res
[
index
]
=
1
cnt
+=
1
if
cnt
==
min
(
x_dim
,
y_dim
):
break
channel
[
index
]
=
-
1
res
=
res
.
reshape
(
x_dim
,
y_dim
,
1
)
res
=
res
.
repeat
([
z_dim
],
axis
=
2
)
res
=
res
.
reshape
(
1
,
x_dim
,
y_dim
,
z_dim
)
if
output
is
not
None
:
output
=
np
.
concatenate
((
output
,
res
),
axis
=
0
)
else
:
output
=
res
self
.
outputs
=
{
'Out'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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