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体验新版 GitCode,发现更多精彩内容 >>
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c083a60d
编写于
1月 15, 2018
作者:
G
guosheng
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 python split and glu
上级
9867a379
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
120 addition
and
12 deletion
+120
-12
doc/api/v2/fluid/layers.rst
doc/api/v2/fluid/layers.rst
+6
-0
doc/api/v2/fluid/nets.rst
doc/api/v2/fluid/nets.rst
+5
-0
paddle/operators/split_op.cc
paddle/operators/split_op.cc
+6
-0
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+61
-1
python/paddle/v2/fluid/nets.py
python/paddle/v2/fluid/nets.py
+34
-1
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
+8
-10
未找到文件。
doc/api/v2/fluid/layers.rst
浏览文件 @
c083a60d
...
...
@@ -348,3 +348,9 @@ reduce_min
.. autofunction:: paddle.v2.fluid.layers.reduce_min
:noindex:
split
-----
.. autofunction:: paddle.v2.fluid.layers.split
:noindex:
doc/api/v2/fluid/nets.rst
浏览文件 @
c083a60d
...
...
@@ -20,3 +20,8 @@ sequence_conv_pool
:noindex:
glu
---
.. autofunction:: paddle.v2.fluid.nets.glu
:noindex:
paddle/operators/split_op.cc
浏览文件 @
c083a60d
...
...
@@ -60,6 +60,12 @@ class SplitOp : public framework::OperatorWithKernel {
}
}
ctx
->
SetOutputsDim
(
"Out"
,
outs_dims
);
if
(
axis
!=
0
)
{
// Only pass LoD when not spliting along the first dim.
for
(
size_t
i
=
0
;
i
<
outs_number
;
++
i
)
{
ctx
->
ShareLoD
(
"X"
,
"Out"
,
0
,
i
);
}
}
}
};
...
...
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
c083a60d
...
...
@@ -14,7 +14,7 @@ __all__ = [
'chunk_eval'
,
'sequence_conv'
,
'conv2d'
,
'sequence_pool'
,
'pool2d'
,
'batch_norm'
,
'beam_search_decode'
,
'conv2d_transpose'
,
'sequence_expand'
,
'lstm_unit'
,
'reduce_sum'
,
'reduce_mean'
,
'reduce_max'
,
'reduce_min'
,
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
'sequence_first_step'
,
'sequence_last_step'
,
'dropout'
,
'split'
]
...
...
@@ -1504,3 +1504,63 @@ def reduce_min(input, dim=None, keep_dim=False):
'reduce_all'
:
True
if
dim
==
None
else
False
})
return
out
def
split
(
input
,
num_or_sections
,
dim
=-
1
):
"""
Splits the tensor into multiple sub-tensors.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
num_or_sections (int|list): If :attr:`num_or_sections` is an integer,
then the integer indicates the number of equal sized sub-tensors
that the tensor will be divided into. If :attr:`num_or_sections`
is a list of integers, the length of list indicates the number of
sub-tensors and the integers indicate the sizes of sub-tensors'
:attr:`dim` dimension orderly.
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dimension to split along is :math:`rank(input) + dim`.
Returns:
List: The list of segmented tensor variables.
Examples:
.. code-block:: python
# x is a Tensor variable with shape [3, 9, 5]:
x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
x0.shape # [3, 3, 5]
x1.shape # [3, 3, 5]
x2.shape # [3, 3, 5]
x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
x0.shape # [3, 2, 5]
x1.shape # [3, 3, 5]
x2.shape # [3, 4, 5]
"""
helper
=
LayerHelper
(
'split'
,
**
locals
())
input_shape
=
input
.
shape
dim
=
(
len
(
input_shape
)
+
dim
)
if
dim
<
0
else
dim
if
isinstance
(
num_or_sections
,
int
):
assert
num_or_sections
>
1
,
'num_or_sections must be more than 1.'
assert
input_shape
[
dim
]
%
num_or_sections
==
0
,
'num_or_sections must evenly divide input.shape[dim].'
num
=
num_or_sections
else
:
assert
len
(
num_or_sections
)
<
input_shape
[
dim
],
'len(num_or_sections) must not be more than input.shape[dim].'
num
=
len
(
num_or_sections
)
outs
=
[
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
for
i
in
range
(
num
)
]
helper
.
append_op
(
type
=
'split'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
outs
},
attrs
=
{
'num'
:
num_or_sections
if
isinstance
(
num_or_sections
,
int
)
else
0
,
'sections'
:
num_or_sections
if
isinstance
(
num_or_sections
,
list
)
else
[],
'axis'
:
dim
})
return
outs
python/paddle/v2/fluid/nets.py
浏览文件 @
c083a60d
import
layers
__all__
=
[
"simple_img_conv_pool"
,
"sequence_conv_pool"
]
__all__
=
[
"simple_img_conv_pool"
,
"sequence_conv_pool"
,
"glu"
]
def
simple_img_conv_pool
(
input
,
...
...
@@ -98,3 +98,36 @@ def sequence_conv_pool(input,
pool_out
=
layers
.
sequence_pool
(
input
=
conv_out
,
pool_type
=
pool_type
)
return
pool_out
def
glu
(
input
,
dim
=-
1
):
"""
The gated linear unit composed by split and elementwise multiplication.
Specifically, Split the input into two equal sized parts :math:`a` and
:math:`b` along the given dimension and then compute as following:
.. math::
{GLU}(a, b)= a \otimes \sigma(b)
Refer to `Language Modeling with Gated Convolutional Networks
<https://arxiv.org/pdf/1612.08083.pdf>`_.
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dimension to split along is :math:`rank(input) + dim`.
Returns:
Variable: The Tensor variable with half the size of input.
Examples:
.. code-block:: python
# x is a Tensor variable with shape [3, 6, 9]
fluid.nets.glu(input=x, dim=-1) # shape of output: [3, 3, 9]
"""
a
,
b
=
layers
.
split
(
input
,
num_or_sections
=
2
,
dim
=
dim
)
out
=
layers
.
elementwise_mul
(
x
=
a
,
y
=
b
)
return
out
python/paddle/v2/fluid/tests/test_reorder_lod_tensor.py
浏览文件 @
c083a60d
...
...
@@ -6,8 +6,8 @@ import numpy
class
TestReorderLoDTensor
(
unittest
.
TestCase
):
num_seq
=
5
# [name,
dim
, lod_level] pair indicating data info of source and target
data_desc
=
([
'input'
,
9
,
0
],
[
'ref'
,
5
,
1
])
# [name,
shape
, lod_level] pair indicating data info of source and target
data_desc
=
([
'input'
,
[
9
],
0
],
[
'ref'
,
[
5
]
,
1
])
@
classmethod
def
setUpClass
(
cls
):
...
...
@@ -16,10 +16,10 @@ class TestReorderLoDTensor(unittest.TestCase):
@
classmethod
def
set_program
(
cls
):
dat
=
fluid
.
layers
.
data
(
name
=
cls
.
data_desc
[
0
][
0
],
shape
=
[
cls
.
data_desc
[
0
][
1
]
])
name
=
cls
.
data_desc
[
0
][
0
],
shape
=
cls
.
data_desc
[
0
][
1
])
dat
.
stop_gradient
=
False
rank_dat
=
fluid
.
layers
.
data
(
name
=
cls
.
data_desc
[
1
][
0
],
shape
=
[
cls
.
data_desc
[
1
][
1
]
])
name
=
cls
.
data_desc
[
1
][
0
],
shape
=
cls
.
data_desc
[
1
][
1
])
table
=
fluid
.
layers
.
lod_rank_table
(
rank_dat
)
new_dat
=
fluid
.
layers
.
reorder_lod_tensor_by_rank
(
x
=
dat
,
rank_table
=
table
)
...
...
@@ -49,7 +49,7 @@ class TestReorderLoDTensor(unittest.TestCase):
self
.
data
=
{}
for
desc
in
self
.
data_desc
:
data_name
=
desc
[
0
]
data_
dim
=
desc
[
1
]
data_
shape
=
desc
[
1
]
data_lod_level
=
desc
[
2
]
data_lod
=
[]
for
i
in
range
(
data_lod_level
):
...
...
@@ -59,9 +59,9 @@ class TestReorderLoDTensor(unittest.TestCase):
size
=
self
.
num_seq
if
i
==
0
else
lod_level_i
[
-
1
])
lod_level_i
=
[
0
]
+
numpy
.
cumsum
(
lod_level_i
).
tolist
()
data_lod
.
append
(
lod_level_i
)
data_value
=
numpy
.
random
.
random
(
size
=
[
data_lod
[
-
1
][
-
1
]
if
data_lod
else
self
.
num_seq
,
data_dim
]
).
astype
(
'float32'
)
data_value
=
numpy
.
random
.
random
(
size
=
[
data_lod
[
-
1
][
-
1
]
if
data_lod
else
self
.
num_seq
]
+
data_shape
).
astype
(
'float32'
)
self
.
data
[
data_name
]
=
(
data_value
,
data_lod
)
def
set_inputs
(
self
,
place
):
...
...
@@ -163,8 +163,6 @@ class TestReorderLoDTensor(unittest.TestCase):
numpy
.
allclose
(
numpy
.
array
(
actual_grad
),
expect_grad
,
atol
=
0.001
))
self
.
assertEqual
(
expect_grad_lod
,
actual_grad
.
lod
())
global
outputs_from_tensor_implicit_lod
outputs_from_tensor_implicit_lod
=
self
.
actual_outputs
# compare outputs between LodTensors with explicit and implicit lod
# use the same data but set the input lod explicitly
...
...
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