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9dc28179
编写于
10月 25, 2018
作者:
B
barrierye
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差异文件
add similarity_focus op
上级
a7f94ec7
3cab25a5
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
107 addition
and
6 deletion
+107
-6
paddle/fluid/operators/math/jit_kernel_rnn.cc
paddle/fluid/operators/math/jit_kernel_rnn.cc
+2
-0
paddle/fluid/operators/similarity_focus_op.cc
paddle/fluid/operators/similarity_focus_op.cc
+2
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+53
-4
python/paddle/fluid/tests/unittests/test_similarity_focus_op.py
.../paddle/fluid/tests/unittests/test_similarity_focus_op.py
+50
-0
未找到文件。
paddle/fluid/operators/math/jit_kernel_rnn.cc
浏览文件 @
9dc28179
...
...
@@ -136,6 +136,7 @@ static std::shared_ptr<const VActKernel<T>> GetActKernel(
return
nullptr
;
}
#ifdef __AVX__
template
<
jit
::
cpu_isa_t
isa
>
static
std
::
unique_ptr
<
AVXAct
>
GetAVXAct
(
const
std
::
string
&
type
)
{
if
(
type
==
"sigmoid"
)
{
...
...
@@ -150,6 +151,7 @@ static std::unique_ptr<AVXAct> GetAVXAct(const std::string& type) {
PADDLE_THROW
(
"Not support type: %s"
,
type
);
return
nullptr
;
}
#endif
/* LSTM JitKernel */
template
<
typename
T
,
jit
::
cpu_isa_t
isa
,
jit_block
>
...
...
paddle/fluid/operators/similarity_focus_op.cc
浏览文件 @
9dc28179
...
...
@@ -35,9 +35,9 @@ class SimilarityFocusOpMaker : public framework::OpProtoAndCheckerMaker {
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
1. Extract the
4
-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 cas
r
, if the shape of input X
it will get the matrix T=X[:, a, :, :]. In this cas
e
, 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)
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
9dc28179
...
...
@@ -7502,9 +7502,9 @@ 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
1. Extract the
4
-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 cas
r
, if the shape of input X
it will get the matrix T=X[:, a, :, :]. In this cas
e
, 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)
...
...
@@ -7514,6 +7514,55 @@ def similarity_focus(input, axis, indexes, name=None):
Refer to `Similarity Focus Layer <http://www.aclweb.org/anthology/N16-1108>`_
.. code-block:: text
* Example :
Given a 4-D tensor x with the shape (BatchSize, C, A, B), where C is
the number of channels and the shape of feature map is (A, B):
x.shape = (2, 3, 2, 2)
x.data = [[[[0.8, 0.1],
[0.4, 0.5]],
[[0.9, 0.7],
[0.9, 0.9]],
[[0.8, 0.9],
[0.1, 0.2]]],
[[[0.2, 0.5],
[0.3, 0.4]],
[[0.9, 0.7],
[0.8, 0.4]],
[[0.0, 0.2],
[0.4, 0.7]]]]
Given axis: 1 (the axis of the channel)
Given indexes: [0]
then we get a 4-D tensor out with the same shape of input x:
out.shape = (2, 3, 2, 2)
out.data = [[[[1.0, 0.0],
[0.0, 1.0]],
[[1.0, 0.0],
[0.0, 1.0]],
[[1.0, 0.0],
[0.0, 1.0]]],
[[[0.0, 1.0],
[1.0, 0.0]],
[[0.0, 1.0],
[1.0, 0.0]],
[[0.0, 1.0],
[1.0, 0.0]]]]
Args:
input(Variable): The input tensor variable(default float). It should
be a 4-D tensor with shape [BatchSize, A, B, C].
...
...
@@ -7528,8 +7577,8 @@ def similarity_focus(input, axis, indexes, name=None):
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, 1
0])
name='data', shape=[
2, 3, 2, 2
], dtype='float32')
x = fluid.layers.layer_norm(input=data, axis=1, indexes=[0])
"""
helper
=
LayerHelper
(
'similarity_focus'
,
**
locals
())
# check attrs
...
...
python/paddle/fluid/tests/unittests/test_similarity_focus_op.py
浏览文件 @
9dc28179
...
...
@@ -20,6 +20,56 @@ import paddle.fluid.core as core
from
op_test
import
OpTest
class
TestSimilarityFocusOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
batch_size
=
2
x_dim
,
y_dim
,
z_dim
=
3
,
2
,
2
self
.
inputs
=
{
'X'
:
np
.
array
([[[[
0.8
,
0.1
],
[
0.4
,
0.5
]],
[[
0.9
,
0.7
],
[
0.9
,
0.9
]],
[[
0.8
,
0.9
],
[
0.1
,
0.2
]]],
[[[
0.2
,
0.5
],
[
0.3
,
0.4
]],
[[
0.9
,
0.7
],
[
0.8
,
0.4
]],
[[
0.0
,
0.2
],
[
0.4
,
0.7
]]]]),
}
self
.
attrs
=
{
'axis'
:
1
,
'indexes'
:
[
0
],
}
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_axis1
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"similarity_focus"
...
...
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