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29616744
编写于
10月 24, 2017
作者:
W
wanghaoshuang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Rewrite sequence expand op
上级
4e8fccff
变更
6
显示空白变更内容
内联
并排
Showing
6 changed file
with
97 addition
and
264 deletion
+97
-264
paddle/framework/lod_tensor.cc
paddle/framework/lod_tensor.cc
+0
-23
paddle/framework/lod_tensor.h
paddle/framework/lod_tensor.h
+0
-3
paddle/operators/seq_expand_op.cc
paddle/operators/seq_expand_op.cc
+49
-60
paddle/operators/seq_expand_op.h
paddle/operators/seq_expand_op.h
+31
-97
python/paddle/v2/framework/tests/op_test.py
python/paddle/v2/framework/tests/op_test.py
+0
-2
python/paddle/v2/framework/tests/test_seq_expand.py
python/paddle/v2/framework/tests/test_seq_expand.py
+17
-79
未找到文件。
paddle/framework/lod_tensor.cc
浏览文件 @
29616744
...
...
@@ -112,28 +112,5 @@ void LoDTensor::ShrinkInLevel(size_t level, size_t elem_begin,
lod_
=
new_lod
;
}
Vector
<
size_t
>
expand_lod
(
Vector
<
size_t
>
level
,
Vector
<
size_t
>
indexes
,
Vector
<
size_t
>
scales
,
bool
repeat
)
{
Vector
<
size_t
>
result
;
result
.
push_back
(
level
[
0
]);
size_t
start
=
0
,
end
=
0
;
if
(
!
repeat
)
{
for
(
size_t
i
=
0
;
i
<
scales
.
size
();
++
i
)
{
result
.
push_back
(
result
.
back
()
+
scales
[
i
]
*
(
level
[
i
+
1
]
-
level
[
i
]));
}
}
else
{
for
(
size_t
i
=
0
;
i
<
scales
.
size
();
++
i
)
{
start
=
indexes
[
i
];
end
=
indexes
[
i
+
1
];
for
(
size_t
j
=
0
;
j
<
scales
[
i
];
++
j
)
{
for
(
size_t
index
=
start
;
index
<
end
-
1
;
++
index
)
{
result
.
push_back
(
result
.
back
()
+
level
[
index
+
1
]
-
level
[
index
]);
}
}
}
}
return
result
;
}
}
// namespace framework
}
// namespace paddle
paddle/framework/lod_tensor.h
浏览文件 @
29616744
...
...
@@ -136,8 +136,5 @@ class LoDTensor : public Tensor {
LoD
lod_
;
};
Vector
<
size_t
>
expand_lod
(
Vector
<
size_t
>
level
,
Vector
<
size_t
>
indexes
,
Vector
<
size_t
>
scales
,
bool
repeat
);
}
// namespace framework
}
// namespace paddle
paddle/operators/seq_expand_op.cc
浏览文件 @
29616744
...
...
@@ -27,20 +27,14 @@ class SeqExpandOp : public framework::OperatorWithKernel {
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SeqExpandOp should not be null."
);
int
repeat
=
ctx
->
Attrs
().
Get
<
int
>
(
"repeat"
);
framework
::
DDim
out_dim
;
if
(
repeat
==
0
)
{
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SeqExpandOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Y"
),
"Input(Y) of SeqExpandOp should not be null while repeat == 0."
);
framework
::
DDim
out_dim
;
out_dim
=
ctx
->
GetInputDim
(
"Y"
);
ctx
->
ShareLoD
(
"Y"
,
"Out"
);
}
else
{
out_dim
=
ctx
->
GetInputDim
(
"X"
);
out_dim
[
0
]
=
out_dim
[
0
]
*
repeat
;
}
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SeqExpandOp should not be null."
);
ctx
->
SetOutputDim
(
"Out"
,
out_dim
);
}
};
...
...
@@ -50,68 +44,63 @@ class SeqExpandOpMaker : public framework::OpProtoAndCheckerMaker {
SeqExpandOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input('X') of seq_expand op. It can be LoDTensor or base Tensor."
);
AddInput
(
"Y"
,
"The reference input('Y') of seq_expand op."
AddInput
(
"X"
,
"(Tensor or LoDTensor) The input('X') of this operator can be a "
"LoDTensor or a base Tensor."
);
AddInput
(
"Y"
,
"(LoDTensor)The reference input('Y') of seq_expand op."
"It must be a LoDTensor with k-level(k>0)."
"This reference input is essential if 'repeat' attribute is not
"
"configured.
"
"Input(X) will be expanded by LoD of input(Y) while repeat == 0
."
);
"Input(X) will be expanded according to LOD of input(Y).
"
"The element numbers of last level in input('Y')
"
"must be equal to dims[0] of input('X')
."
);
AddOutput
(
"Out"
,
"The output of seq_expand op."
"The output is a (k+1)-level LoDTensor"
"while input(X) being k-level LoDTensor."
"(Given base tensor is 0-level LoDTensor.)"
);
AddAttr
<
int
>
(
"repeat"
,
"(type:int; default value: 0)"
"Repeatting times of each element while expanding input(X)."
"It works while input(Y) is not configured."
)
.
SetDefault
(
0
);
"The lod of output will be as same as input(Y)'s lod."
);
AddComment
(
R"DOC(
Expand k-level LoDTensor to (k+1)-level LoDTensor
by lod of input(Y) or 'repeat' attribute.
Expand input(X) according to LOD of input(Y).
Case 1:
Given a 2-level LoDTensor X:
X.data = [a, b , c, d]
X.lod = [[0, 3, 4], [0, 1, 3, 4]]
and
repeat = 2
then we get 3-level LoDTensor
Out.lod = [[0, 6, 8],
[0, 3, 6, 7, 8],
[0, 1, 3, 4, 6, 7, 8]]
Out.data = [a, b, c, a, b, c, d, d]
Given 2-level a LoDTensor input(X)
X.lod = [[0, 2, 3],
[0, 1, 3, 4]]
X.data = [a, b, c, d]
X.dims = [4, 1]
and input(Y)
Y.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
then we get 2-level LoDTensor
Out.lod = [[0, 2, 4],
[0, 3, 6, 7, 8]]
Out.data = [a, a, a, b, b, b, c, d]
Out.dims = [8, 1]
Case 2:
Given 2-level a LoDTensor X
X.data = [1, 2, 3, 4]
X.lod = [[0, 3, 4], [0, 1, 3, 4]]
and
Y.lod = [[0, 6, 8],
[0, 3, 6, 7, 8],
[0,1,3,4,6,7,8]]
then we get 3-level LoDTensor
Out.data = [1, 2, 3, 1, 2, 3, 4, 4]
Out.lod = [[0, 6, 8],
[0, 3, 6, 7, 8],
[0, 1, 3, 4, 6, 7, 8]]
Given a 0-level LoDTensor input(X)
X.data = [a, b, c]
X.lod = NULL
X.dims = [3, 1]
and input(Y)
Y.lod = [[0, 2, 3, 6]]
then we get 1-level LoDTensor
Out.lod = [[0, 2, 3, 6]]
Out.data = [a, a, b, c, c, c]
Out.dims = [6, 1]
Case 3:
Given a 0-level LoDTensor
X
X.data = [
1, 2, 3, 4
]
Given a 0-level LoDTensor
input(X)
X.data = [
[a, b], [c, d], [e, f]
]
X.lod = NULL
and
repeat = 2
X.dims = [3, 2]
and input(Y)
Y.lod = [[0, 2, 3, 6]]
then we get 1-level LoDTensor
Out.data = [1, 1, 2, 2, 3, 3, 4, 4]
Out.lod = [[0, 2, 4, 6, 8]]
Out.lod = [[0, 2, 3, 6]]
Out.data = [[a,b], [a,b] [c,d], [e, f], [e, f], [e, f]]
Out.dims = [6, 2]
)DOC"
);
}
...
...
paddle/operators/seq_expand_op.h
浏览文件 @
29616744
...
...
@@ -31,93 +31,28 @@ class SeqExpandKernel : public framework::OpKernel<T> {
auto
*
out
=
context
.
Output
<
LoDTensor
>
(
"Out"
);
const
T
*
x_data
=
x
->
data
<
T
>
();
auto
x_dims
=
x
->
dims
();
auto
x_lod
=
x
->
lod
();
framework
::
Vector
<
size_t
>
level
;
size_t
num
=
(
x_lod
.
size
()
==
0
)
?
(
x
->
dims
()[
0
]
+
1
)
:
x_lod
[
0
].
size
();
for
(
int
i
=
0
;
i
<
num
;
++
i
)
{
level
.
push_back
(
i
);
}
x_lod
.
push_back
(
level
);
size_t
repeat
=
static_cast
<
size_t
>
(
context
.
Attr
<
int
>
(
"repeat"
));
framework
::
Vector
<
size_t
>
scales
;
if
(
repeat
!=
0
)
{
for
(
int
i
=
0
;
i
<
x_lod
[
0
].
size
()
-
1
;
++
i
)
{
scales
.
push_back
(
repeat
);
}
std
::
vector
<
int64_t
>
dims
=
framework
::
vectorize
(
x
->
dims
());
dims
[
0
]
=
dims
[
0
]
*
repeat
;
auto
out_dims
=
framework
::
make_ddim
(
dims
);
out
->
Resize
(
out_dims
);
}
else
{
auto
*
y
=
context
.
Input
<
LoDTensor
>
(
"Y"
);
auto
y_lod
=
y
->
lod
();
auto
y_abs_lod
=
y_lod
.
ToAbsOffset
();
auto
x_abs_lod
=
x_lod
.
ToAbsOffset
();
for
(
int
i
=
0
;
i
<
y_abs_lod
[
0
].
size
()
-
1
;
++
i
)
{
scales
.
push_back
((
y_abs_lod
[
0
][
i
+
1
]
-
y_abs_lod
[
0
][
i
])
/
(
x_abs_lod
[
0
][
i
+
1
]
-
x_abs_lod
[
0
][
i
]));
}
PADDLE_ENFORCE_EQ
(
x_dims
[
0
],
y
->
lod
().
back
().
size
()
-
1
,
"The size of last lod level in Input(Y)"
"must be equal to dims[0] of Input(X)."
);
out
->
set_lod
(
y
->
lod
());
out
->
Resize
(
y
->
dims
());
}
framework
::
Vector
<
size_t
>
indexes
;
for
(
int
size_t
i
=
0
;
i
<
x_lod
[
0
];
++
i
)
{
indexes
[
i
]
=
x_lod
[
0
];
}
framework
::
LoD
out_lod
;
auto
level0
=
framework
::
expand_lod
(
indexes
,
x_lod
[
0
],
scales
,
false
);
out_lod
.
push_back
(
level0
);
for
(
int
i
=
1
;
i
<
x_lod
.
size
();
++
i
)
{
for
(
int
j
=
0
;
j
<
indexes
.
size
();
++
j
)
{
indexes
[
j
]
=
x_lod
[
i
-
1
][
indexes
[
j
]];
}
out_lod
.
push_back
(
framework
::
expand_lod
(
x_lod
[
i
],
indexes
,
scales
,
true
));
}
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
size_t
element_len
=
framework
::
product
(
x_dims
)
/
x_dims
[
0
];
T
*
out_data
=
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
out_starts
=
out
->
lod
().
back
();
// copy data
auto
place
=
context
.
GetPlace
();
size_t
count
=
0
;
if
(
platform
::
is_cpu_place
(
place
))
{
auto
&
cpu_place
=
boost
::
get
<
platform
::
CPUPlace
>
(
place
);
for
(
size_t
i
=
0
;
i
<
scales
.
size
();
++
i
)
{
count
=
element_len
*
(
x_abs_lod
[
0
][
i
+
1
]
-
x_abs_lod
[
0
][
i
]);
for
(
size_t
j
=
0
;
j
<
scales
[
i
];
++
j
)
{
memory
::
Copy
(
cpu_place
,
out_data
,
cpu_place
,
x_data
,
sizeof
(
T
)
*
count
);
out_data
+=
count
;
}
x_data
+=
count
;
}
}
else
{
#ifdef PADDLE_WITH_CUDA
auto
&
gpu_place
=
boost
::
get
<
platform
::
GPUPlace
>
(
place
);
auto
stream
=
reinterpret_cast
<
const
platform
::
CUDADeviceContext
&>
(
context
.
device_context
())
.
stream
();
for
(
size_t
i
=
0
;
i
<
scales
.
size
();
++
i
)
{
count
=
element_len
*
(
x_abs_lod
[
0
][
i
+
1
]
-
x_abs_lod
[
0
][
i
]);
for
(
size_t
j
=
0
;
j
<
scales
[
i
];
++
j
)
{
memory
::
Copy
(
gpu_place
,
out_data
,
gpu_place
,
x_data
,
sizeof
(
T
)
*
count
,
stream
);
out_data
+=
count
;
}
x_data
+=
count
;
}
#else
PADDLE_THROW
(
"Paddle is not compiled with GPU"
);
#endif
}
out
->
set_lod
(
out_lod
);
for
(
size_t
i
=
0
;
i
<
lod
.
size
;
i
++
)
{
for
(
size_t
j
=
0
;
j
<
lod
[
i
].
size
();
j
++
)
{
LOG
(
INFO
)
<<
"lod["
<<
i
<<
"]["
<<
j
"] = "
<<
lod
[
i
][
j
];
}
for
(
size_t
i
=
0
;
i
<
out_starts
.
size
()
-
1
;
i
++
)
{
int
scale
=
out_starts
[
i
+
1
]
-
out_starts
[
i
];
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
x_t
(
x_data
,
1
,
element_len
);
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
out_t
(
out_data
,
scale
,
element_len
);
Eigen
::
array
<
int
,
2
>
cast
({
scale
,
1
});
out_t
.
device
(
place
)
=
x_t
.
broadcast
(
cast
);
x_data
+=
element_len
;
out_data
+=
element_len
*
scale
;
}
}
};
...
...
@@ -130,25 +65,24 @@ class SeqExpandGradKernel : public framework::OpKernel<T> {
auto
*
x
=
context
.
Input
<
LoDTensor
>
(
"X"
);
auto
*
out
=
context
.
Input
<
LoDTensor
>
(
"Out"
);
auto
*
d_x
=
context
.
Output
<
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
out_lod
=
out
->
lod
();
auto
out_abs_lod
=
out_lod
.
ToAbsOffset
();
auto
out_last_level
=
out
->
lod
().
back
();
d_x
->
set_lod
(
x
->
lod
());
const
T
*
d_out_data
=
d_out
->
data
<
T
>
();
auto
d_out_dims
=
d_out
->
dims
();
T
*
d_x_data
=
d_x
->
mutable_data
<
T
>
(
context
.
GetPlace
());
size_t
element_len
=
framework
::
product
(
d_out_dims
)
/
d_out_dims
[
0
];
for
(
size_t
i
=
0
;
i
<
out
->
NumElements
();
++
i
)
{
size_t
ele_count
=
out_abs_lod
[
0
][
i
+
1
]
-
out_abs_lod
[
0
][
i
];
size_t
repeat
=
out
->
NumElements
(
0
,
i
)
;
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
2
>>
d_out_t
(
d_out_data
,
static_cast
<
int
>
(
repeat
),
static_cast
<
int
>
((
ele_count
*
element_len
)
/
repeat
)
);
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
1
>>
d_x_t
(
d_x_data
,
static_cast
<
int
>
((
ele_count
*
element_len
)
/
repeat
));
for
(
size_t
i
=
0
;
i
<
out_last_level
.
size
()
-
1
;
++
i
)
{
size_t
repeat
=
out
_last_level
[
i
+
1
]
-
out_last_level
[
i
]
;
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
const
T
,
2
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
d_out_t
(
d_out_data
,
static_cast
<
int
>
(
repeat
),
element_len
);
Eigen
::
TensorMap
<
Eigen
::
Tensor
<
T
,
1
,
Eigen
::
RowMajor
,
Eigen
::
DenseIndex
>>
d_x_t
(
d_x_data
,
static_cast
<
int
>
(
element_len
));
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
d_x_t
.
device
(
place
)
=
d_out_t
.
sum
(
Eigen
::
array
<
int
,
1
>
({{
0
}}));
d_out_data
+=
(
ele_coun
t
*
element_len
);
d_x_data
+=
((
ele_count
*
element_len
)
/
repeat
)
;
d_out_data
+=
(
repea
t
*
element_len
);
d_x_data
+=
element_len
;
}
}
};
...
...
python/paddle/v2/framework/tests/op_test.py
浏览文件 @
29616744
...
...
@@ -246,8 +246,6 @@ class OpTest(unittest.TestCase):
else
:
actual
=
np
.
array
(
self
.
scope
.
find_var
(
out_name
).
get_tensor
())
expect
=
self
.
outputs
[
out_name
]
print
"actual= %s"
%
actual
print
"expect = %s"
%
expect
self
.
assertTrue
(
np
.
allclose
(
actual
,
expect
,
atol
=
atol
),
...
...
python/paddle/v2/framework/tests/test_seq_expand.py
浏览文件 @
29616744
...
...
@@ -3,66 +3,21 @@ import numpy as np
from
op_test
import
OpTest
def
repeat
(
list
,
starts
,
times
,
is_first
):
newlist
=
[
list
[
0
]]
if
is_first
:
for
i
,
time
in
enumerate
(
times
):
size
=
list
[
i
+
1
]
-
list
[
i
]
newlist
.
append
(
newlist
[
-
1
]
+
size
*
time
)
else
:
for
i
,
time
in
enumerate
(
times
):
start
=
list
.
index
(
starts
[
i
])
end
=
list
.
index
(
starts
[
i
+
1
])
+
1
for
t
in
range
(
time
):
for
index
in
range
(
start
,
end
-
1
):
newlist
.
append
(
newlist
[
-
1
]
+
list
[
index
+
1
]
-
list
[
index
])
return
newlist
def
repeat_array
(
array
,
starts
,
times
):
newlist
=
[]
for
i
,
time
in
enumerate
(
times
):
for
t
in
range
(
time
):
newlist
.
extend
(
array
[
starts
[
i
]:
starts
[
i
+
1
]])
return
newlist
def
toAbsOffset
(
lod
):
for
i
in
range
(
len
(
lod
)
-
2
,
-
1
,
-
1
):
for
j
in
range
(
len
(
lod
[
i
])):
lod
[
i
][
j
]
=
lod
[
i
+
1
][
lod
[
i
][
j
]]
return
lod
class
TestSeqExpand
(
OpTest
):
#class TestSeqExpand():
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
4
,
1
]).
astype
(
'float32'
)
self
.
inputs
=
{
'X'
:
x_data
}
self
.
repeat
=
2
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
3
,
1
]).
astype
(
'float32'
)
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
8
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
1
,
4
,
8
]]
self
.
inputs
=
{
'X'
:
x_data
,
'Y'
:
(
y_data
,
y_lod
)}
def
compute
(
self
):
x
=
self
.
inputs
[
'X'
]
print
"x= %s"
%
x
x_data
,
x_lod
=
x
if
type
(
x
)
==
tuple
else
(
x
,
None
)
n
=
1
+
x_data
.
shape
[
0
]
if
not
x_lod
else
len
(
x_lod
[
0
])
x_lod
=
[[
i
for
i
in
range
(
n
)]]
+
x_lod
x_abs_lod
=
toAbsOffset
(
x_lod
)
if
self
.
repeat
:
print
"repeat= %s"
%
self
.
repeat
self
.
attrs
=
{
'repeat'
:
self
.
repeat
}
repeats
=
(
len
(
x_lod
[
0
])
-
1
)
*
[
self
.
repeat
]
else
:
y_data
,
y_lod
=
self
.
inputs
[
'Y'
]
print
"y_lod: %s"
%
y_lod
y_abs_lod
=
toAbsOffset
(
y_lod
)
repeats
=
[((
y_abs_lod
[
0
][
i
+
1
]
-
y_abs_lod
[
0
][
i
])
/
(
x_abs_lod
[
0
][
i
+
1
]
-
x_abs_lod
[
0
][
i
]))
for
i
in
range
(
len
(
y_abs_lod
[
0
])
-
1
)]
#out_lod = [repeat(x_lod[0], x_lod[0], repeats, True)] + [
# repeat(lod, x_lod[0], repeats, False) for lod in x_lod[1:]
#]
out
=
repeat_array
(
x_data
.
tolist
(),
x_abs_lod
[
0
],
repeats
)
repeats
=
[((
y_lod
[
-
1
][
i
+
1
]
-
y_lod
[
-
1
][
i
]))
for
i
in
range
(
len
(
y_lod
[
-
1
])
-
1
)]
out
=
x_data
.
repeat
(
repeats
,
axis
=
0
)
self
.
outputs
=
{
'Out'
:
out
}
def
setUp
(
self
):
...
...
@@ -78,39 +33,22 @@ class TestSeqExpand(OpTest):
class
TestSeqExpandCase1
(
TestSeqExpand
):
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
7
,
1
]).
astype
(
'float32'
)
x_lod
=
[[
0
,
2
,
3
],
[
0
,
2
,
5
,
7
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
)}
self
.
repeat
=
2
class
TestSeqExpandCase2
(
TestSeqExpand
):
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
4
,
1
]).
astype
(
'float32'
)
self
.
inputs
=
{
'X'
:
x_data
}
self
.
repeat
=
2
class
TestSeqExpandCase3
(
TestSeqExpand
):
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
3
,
1
]).
astype
(
'float32'
)
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
8
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
1
,
4
,
8
]]
self
.
inputs
=
{
'X'
:
x_data
,
'Y'
:
(
y_data
,
y_lod
)}
self
.
repeat
=
None
class
TestSeqExpandCase4
(
TestSeqExpand
):
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
5
,
1
]).
astype
(
'float32'
)
x_lod
=
[[
0
,
2
,
5
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
13
,
1
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
2
,
5
],
[
0
,
2
,
4
,
7
,
10
,
13
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
self
.
repeat
=
None
class
TestSeqExpandCase2
(
TestSeqExpand
):
def
set_data
(
self
):
x_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
1
,
2
,
2
]).
astype
(
'float32'
)
x_lod
=
[[
0
,
1
]]
y_data
=
np
.
random
.
uniform
(
0.1
,
1
,
[
2
,
2
,
2
]).
astype
(
'float32'
)
y_lod
=
[[
0
,
2
]]
self
.
inputs
=
{
'X'
:
(
x_data
,
x_lod
),
'Y'
:
(
y_data
,
y_lod
)}
if
__name__
==
'__main__'
:
unittest
.
main
()
# TestSeqExpandCase4().setUp()
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