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体验新版 GitCode,发现更多精彩内容 >>
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869a6f9c
编写于
3月 19, 2018
作者:
Y
yangyaming
浏览文件
操作
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电子邮件补丁
差异文件
Add python wrapper.
上级
ea788fc5
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
155 addition
and
15 deletion
+155
-15
paddle/fluid/operators/lod_reset_op.cc
paddle/fluid/operators/lod_reset_op.cc
+48
-13
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+98
-2
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+9
-0
未找到文件。
paddle/fluid/operators/lod_reset_op.cc
浏览文件 @
869a6f9c
...
...
@@ -30,7 +30,7 @@ class LoDResetOp : public framework::OperatorWithKernel {
if
(
!
ctx
->
HasInput
(
"Y"
))
{
auto
level0
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"target_lod"
);
PADDLE_ENFORCE_GT
(
level0
.
size
(),
1
,
"If Input(Y)
is
not provided, the target lod should be "
"If Input(Y) not provided, the target lod should be "
"specified by attribute `target_lod`."
);
}
ctx
->
SetOutputDim
(
"Out"
,
ctx
->
GetInputDim
(
"X"
));
...
...
@@ -54,9 +54,10 @@ class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
"could be a Tensor or LoDTensor, where the data of output "
"variable inherits from."
);
AddInput
(
"Y"
,
"(Tensor, LoDTensor, optional) If provided, lod of Input(Y) would "
"be considered as the target lod first, otherwise data of "
"Input(Y) would be considered as the target lod."
)
"(Tensor, LoDTensor, optional) If provided and Y is LoDTensor, "
"lod of Input(Y) would be considered as the target lod first, "
"otherwise data of Input(Y) would be considered as the "
"target lod."
)
.
AsDispensable
();
AddOutput
(
"Out"
,
"(LoDTensor) Output variable of LoDResetOp which should be a "
...
...
@@ -67,25 +68,59 @@ class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment
(
R"DOC(LoDReset operator
Set LoD of `X` to a new one specified by `Y` or attribute `target_lod`. When `Y`
provided, `Y.lod` would be considered as target LoD first, otherwise `Y.data`
would be considered as target LoD. If `Y` is not provided, target LoD should be
specified by attribute `target_lod`. If target LoD is specified by `Y.data` or
`target_lod`, only one level LoD is supported.
provided and `Y` is a LoDTensor, `Y.lod` would be considered as target LoD
first, otherwise `Y.data` would be considered as target LoD. If `Y` is not
provided, target LoD should be specified by attribute `target_lod`.
If target LoD is specified by `Y.data` or `target_lod`, only one level LoD
is supported.
An example
:
Example 1
:
Given a 1-level LoDTensor input(X)
X.lod = [[ 0,
2, 5
6 ]]
Given a 1-level LoDTensor input(X)
:
X.lod = [[ 0,
2, 5
6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
target_lod
: [0, 4, 6]
attr(target_lod)
: [0, 4, 6]
then we get a
n 1-level LoDTensor
then we get a
1-level LoDTensor:
Out.lod = [[ 0, 4, 6 ]]
Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
Out.dims = [6, 1]
Example 2:
Given a 1-level LoDTensor input(X):
X.lod = [[ 0, 2, 5 6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
input(Y) is a Tensor:
Y.data = [[0, 2, 6]]
Y.dims = [1, 3]
then we get a 1-level LoDTensor:
Out.lod = [[ 0, 2, 6 ]]
Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
Out.dims = [6, 1]
Example 3:
Given a 1-level LoDTensor input(X):
X.lod = [[ 0, 2, 5 6 ]]
X.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
X.dims = [6, 1]
input(Y) is a 2-level LoDTensor:
Y.lod = [[0, 2, 4], [0, 2, 5, 6]]
Y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
Y.dims = [6, 1]
then we get a 2-level LoDTensor:
Out.lod = [[0, 2, 4], [0, 2, 5, 6]]
Out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
Out.dims = [6, 1]
)DOC"
);
}
};
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
869a6f9c
...
...
@@ -73,6 +73,7 @@ __all__ = [
'smooth_l1'
,
'one_hot'
,
'autoincreased_step_counter'
,
'lod_reset'
,
]
...
...
@@ -2225,7 +2226,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
keep_dim (bool|False): Whether to reserve the reduced dimension in the
output Tensor. The result tensor will have one fewer dimension
than the :attr:`input` unless :attr:`keep_dim` is true.
name(str|None): A name for this layer(optional). If set None, the
name(str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
...
...
@@ -2241,7 +2242,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_prod(x) # [0.0002268]
fluid.layers.reduce_prod(x, dim=0) # [0.02, 0.06, 0.3, 0.63]
fluid.layers.reduce_prod(x, dim=-1) # [0.027, 0.0084]
fluid.layers.reduce_prod(x, dim=1,
fluid.layers.reduce_prod(x, dim=1,
keep_dim=True) # [[0.027], [0.0084]]
"""
helper
=
LayerHelper
(
'reduce_prod'
,
**
locals
())
...
...
@@ -3292,3 +3293,98 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1):
counter
.
stop_gradient
=
True
return
counter
def
lod_reset
(
x
,
y
,
target_lod
=
None
):
"""
LoD Reset Operator. Set LoD of **x** to a new one specified by **y** or
**target_lod**. When **y** provided, **y.lod** would be considered as target
LoD first, otherwise **y.data** would be considered as target LoD. If **y**
is not provided, target LoD should be specified by **target_lod**.
If target LoD is specified by **Y.data** or **target_lod**, only one level
LoD is supported.
.. code-block:: text
* Example 1:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
target_lod: [0, 4, 6]
then we get a 1-level LoDTensor:
out.lod = [[ 0, 4, 6 ]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
* Example 2:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a Tensor:
y.data = [[0, 2, 6]]
y.dims = [1, 3]
then we get a 1-level LoDTensor:
out.lod = [[ 0, 2, 6 ]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
* Example 3:
Given a 1-level LoDTensor x:
x.lod = [[ 0, 2, 5 6 ]]
x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
x.dims = [6, 1]
y is a 2-level LoDTensor:
y.lod = [[0, 2, 4], [0, 2, 5, 6]]
y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
y.dims = [6, 1]
then we get a 2-level LoDTensor:
out.lod = [[0, 2, 4], [0, 2, 5, 6]]
out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
out.dims = [6, 1]
Args:
x (Variable): Input variable which could be a Tensor or LodTensor.
y (Variable|None): If provided, output's LoD would be derived from y.
target_lod (list|tuple|None): One level LoD which should be considered
as target LoD when y not provided.
Returns:
Variable: Output variable with LoD specified by this operator.
Raises:
ValueError: If y and target_lod are both None.
Examples:
.. code-block:: python
x = layers.data(name='x', shape=[10])
y = layers.data(name='y', shape=[10, 20], lod_level=2)
out = layers.lod_reset(x=x, y=y)
"""
helper
=
LayerHelper
(
"lod_reset"
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
if
y
is
not
None
:
helper
.
append_op
(
type
=
"lod_reset"
,
inputs
=
{
'X'
:
x
,
'Y'
:
y
},
outputs
=
{
'Out'
:
out
})
elif
target_lod
is
not
None
:
helper
.
append_op
(
type
=
"lod_reset"
,
inputs
=
{
'X'
:
x
},
attrs
=
{
'target_lod'
:
target_lod
},
outputs
=
{
'Out'
:
out
})
else
:
raise
ValueError
(
"y and target_lod should not be both None."
)
return
out
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
869a6f9c
...
...
@@ -327,6 +327,15 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
loss
)
print
(
str
(
program
))
def
test_lod_reset
(
self
):
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
10
],
dtype
=
'float32'
)
y
=
layers
.
data
(
name
=
'y'
,
shape
=
[
10
,
20
],
dtype
=
'float32'
,
lod_level
=
2
)
print
(
layers
.
lod_reset
(
x
=
x
,
y
=
y
))
print
(
str
(
program
))
if
__name__
==
'__main__'
:
unittest
.
main
()
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