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9536c4e3
编写于
1月 23, 2018
作者:
W
whs
提交者:
GitHub
1月 23, 2018
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Merge pull request #7595 from wanghaoshuang/block_expand_py
Add python API for im2sequence op
上级
d76fcb6f
d498d93c
变更
3
隐藏空白更改
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并排
Showing
3 changed file
with
141 addition
and
0 deletion
+141
-0
doc/api/v2/fluid/layers.rst
doc/api/v2/fluid/layers.rst
+5
-0
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+126
-0
python/paddle/v2/fluid/tests/test_layers.py
python/paddle/v2/fluid/tests/test_layers.py
+10
-0
未找到文件。
doc/api/v2/fluid/layers.rst
浏览文件 @
9536c4e3
...
@@ -505,6 +505,11 @@ swish
...
@@ -505,6 +505,11 @@ swish
.. autofunction:: paddle.v2.fluid.layers.swish
.. autofunction:: paddle.v2.fluid.layers.swish
:noindex:
:noindex:
im2sequence
------
.. autofunction:: paddle.v2.fluid.layers.im2sequence
:noindex:
edit_distance
edit_distance
---------------
---------------
.. autofunction:: paddle.v2.fluid.layers.edit_distance_error
.. autofunction:: paddle.v2.fluid.layers.edit_distance_error
...
...
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
9536c4e3
...
@@ -59,6 +59,7 @@ __all__ = [
...
@@ -59,6 +59,7 @@ __all__ = [
'warpctc'
,
'warpctc'
,
'sequence_reshape'
,
'sequence_reshape'
,
'transpose'
,
'transpose'
,
'im2sequence'
,
'nce'
,
'nce'
,
]
]
...
@@ -2391,3 +2392,128 @@ def transpose(x, perm, name=None):
...
@@ -2391,3 +2392,128 @@ def transpose(x, perm, name=None):
outputs
=
{
'Out'
:
[
out
]},
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'axis'
:
perm
})
attrs
=
{
'axis'
:
perm
})
return
out
return
out
def
im2sequence
(
input
,
filter_size
=
1
,
stride
=
1
,
padding
=
0
,
name
=
None
):
"""
Extracts image patches from the input tensor to form a tensor of shape
{input.batch_size * output_height * output_width, filter_size_H *
filter_size_W * input.channels} which is similar with im2col.
This op use filter / kernel to scan images and convert these images to
sequences. After expanding, the number of time step are
output_height * output_width for an image, in which output_height and
output_width are calculated by below equation:
.. math::
output\_size = 1 +
\
(2 * padding + img\_size - block\_size + stride - 1) / stride
And the dimension of each time step is block_y * block_x * input.channels.
Args:
input (Variable): The input should be a tensor in NCHW format.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it can
contain two integers like (padding_H, padding_W) which means
padding_up = padding_down = padding_H and
padding_left = padding_right = padding_W. Or it can use
(padding_up, padding_left, padding_down, padding_right) to indicate
paddings of four direction. Otherwise, a scalar padding means
padding_up = padding_down = padding_left = padding_right = padding
Default: padding = 0.
name (int): The name of this layer. It is optional.
Returns:
output: The output is a LoDTensor with shape
{input.batch_size * output_height * output_width,
filter_size_H * filter_size_W * input.channels}.
If we regard output as a matrix, each row of this matrix is
a step of a sequence.
Examples:
As an example:
.. code-block:: text
Given:
x = [[[[ 6. 2. 1.]
[ 8. 3. 5.]
[ 0. 2. 6.]]
[[ 2. 4. 4.]
[ 6. 3. 0.]
[ 6. 4. 7.]]]
[[[ 6. 7. 1.]
[ 5. 7. 9.]
[ 2. 4. 8.]]
[[ 1. 2. 1.]
[ 1. 3. 5.]
[ 9. 0. 8.]]]]
x.dims = {2, 2, 3, 3}
And:
filter = [2, 2]
stride = [1, 1]
padding = [0, 0]
Then:
output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 2. 1. 3. 5. 4. 4. 3. 0.]
[ 8. 3. 0. 2. 6. 3. 6. 4.]
[ 3. 5. 2. 6. 3. 0. 4. 7.]
[ 6. 7. 5. 7. 1. 2. 1. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.lod = [[0, 4, 8]]
The simple usage is:
.. code-block:: python
output = fluid.layers.im2sequence(input=layer, stride=[1, 1], filter_size=[2, 2])
"""
if
isinstance
(
filter_size
,
int
):
filter_size
=
[
filter_size
,
filter_size
]
if
isinstance
(
stride
,
int
):
stride
=
[
stride
,
stride
]
if
isinstance
(
padding
,
int
):
padding
=
[
padding
,
padding
]
if
len
(
padding
)
==
2
:
padding
.
append
(
padding
[
0
])
padding
.
append
(
padding
[
1
])
helper
=
LayerHelper
(
'im2sequence'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'im2sequence'
,
inputs
=
{
'X'
:
input
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'kernels'
:
filter_size
,
'strides'
:
stride
,
'paddings'
:
padding
,
})
return
out
python/paddle/v2/fluid/tests/test_layers.py
浏览文件 @
9536c4e3
...
@@ -226,6 +226,16 @@ class TestBook(unittest.TestCase):
...
@@ -226,6 +226,16 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
out
)
self
.
assertIsNotNone
(
out
)
print
(
str
(
program
))
print
(
str
(
program
))
def
test_im2sequence
(
self
):
print
(
"test_im2sequence"
)
program
=
Program
()
with
program_guard
(
program
):
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
3
,
128
,
128
],
dtype
=
'float32'
)
output
=
layers
.
im2sequence
(
input
=
x
,
stride
=
[
1
,
1
],
filter_size
=
[
2
,
2
])
self
.
assertIsNotNone
(
output
)
print
(
str
(
program
))
@
decorators
.
prog_scope
()
@
decorators
.
prog_scope
()
def
test_nce
(
self
):
def
test_nce
(
self
):
window_size
=
5
window_size
=
5
...
...
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