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9dc3ed40
编写于
6月 06, 2018
作者:
F
fengjiayi
提交者:
GitHub
6月 06, 2018
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Merge pull request #11198 from JiayiFeng/dev_resize_short
Add image_resize_short and refine resize API
上级
df87e63b
f3db005c
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1
隐藏空白更改
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1 changed file
with
75 addition
and
15 deletion
+75
-15
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+75
-15
未找到文件。
python/paddle/fluid/layers/nn.py
浏览文件 @
9dc3ed40
...
...
@@ -81,6 +81,8 @@ __all__ = [
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'random_crop'
,
...
...
@@ -3929,22 +3931,25 @@ def dice_loss(input, label, epsilon=0.00001):
return
reduce_mean
(
dice_score
)
def
resize_bilinear
(
input
,
out_shape
=
None
,
scale
=
None
,
name
=
None
):
def
image_resize
(
input
,
out_shape
=
None
,
scale
=
None
,
name
=
None
,
resample
=
'BILINEAR'
):
"""
The mathematical meaning of resize bilinear layer is
Bilinear interpolation.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this layer) on a rectilinear 2D grid.
Resize a batch of images.
For details, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
The input must be a tensor of the shape (num_batches, channels, in_h, in_w),
and the resizing only applies on the last two dimensions(hight and width).
Supporting resample methods:
'BILINEAR' : Bilinear interpolation
Args:
input (Variable): The input tensor of
resize bilinear
layer,
input (Variable): The input tensor of
image resize
layer,
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_shape(list|tuple|Variable|None): Output shape of
resize bilinear
out_shape(list|tuple|Variable|None): Output shape of
image resize
layer, the shape is (out_h, out_w).
Default: None
scale(float|None): The multiplier for the input height or width.
...
...
@@ -3953,6 +3958,8 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
resample(str): The resample method. It can only be 'BILINEAR' currently.
Default: 'BILINEAR'
Returns:
out (Variable): The output is a 4-D tensor of the shape
...
...
@@ -3961,8 +3968,12 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
Examples:
.. code-block:: python
out = fluid.layers.
resize_bilinear
(input, out_shape=[12, 12])
out = fluid.layers.
image_resize
(input, out_shape=[12, 12])
"""
resample_methods
=
{
'BILINEAR'
:
'bilinear_interp'
}
if
resample
not
in
resample_methods
:
raise
ValueError
(
"The 'resample' of image_resize can only be 'BILINEAR' currently."
)
if
out_shape
is
None
and
scale
is
None
:
raise
ValueError
(
"One of out_shape and scale must not be None"
)
helper
=
LayerHelper
(
'bilinear_interp'
,
**
locals
())
...
...
@@ -3990,7 +4001,7 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
out
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"bilinear_interp"
,
type
=
resample_methods
[
resample
]
,
inputs
=
inputs
,
outputs
=
{
"Out"
:
out
},
attrs
=
{
"out_h"
:
out_h
,
...
...
@@ -3998,6 +4009,55 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
return
out
def
resize_bilinear
(
input
,
out_shape
=
None
,
scale
=
None
,
name
=
None
):
"""
This is an alias of layer 'image_resize' with bilinear interpolation.
The mathematical meaning of resize bilinear layer is
Bilinear interpolation.
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this layer) on a rectilinear 2D grid.
For details, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
"""
return
image_resize
(
input
,
out_shape
,
scale
,
name
,
'BILINEAR'
)
def
image_resize_short
(
input
,
out_short_len
,
resample
=
'BILINEAR'
):
"""
Resize a batch of images. The short edge of input images will be
resized to the given 'out_short_len'. The long edge of input images
will be resized proportionately to make images' length-width ratio
constant.
Args:
input (Variable): The input tensor of image resize layer,
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_short_len(int): The length of output images' short edge.
Returns:
out (Variable): The output is a 4-D tensor of the shape
(num_batches, channls, out_h, out_w).
"""
in_shape
=
input
.
shape
if
len
(
in_shape
)
!=
4
:
raise
ValueError
(
"The rank of input must be 4 (num_batches, channels, in_h, in_w)."
)
hw
=
in_shape
[
2
:
4
]
short_idx
=
hw
.
index
(
min
(
hw
))
long_idx
=
1
-
short_idx
out_shape
=
list
(
hw
)
out_shape
[
short_idx
]
=
out_short_len
out_shape
[
long_idx
]
=
int
(
float
(
out_shape
[
long_idx
])
*
(
float
(
out_short_len
)
/
float
(
hw
[
short_idx
]))
+
0.5
)
return
image_resize
(
input
=
input
,
out_shape
=
out_shape
,
resample
=
resample
)
def
gather
(
input
,
index
):
"""
Output is obtained by gathering entries of the outer-most dimension
...
...
@@ -4005,7 +4065,7 @@ def gather(input, index):
.. math::
Out = X[Index]
Out = X[Index]
.. code-block:: text
...
...
@@ -4013,8 +4073,8 @@ def gather(input, index):
Given:
X = [[1, 2],
[3, 4],
X = [[1, 2],
[3, 4],
[5, 6]]
Index = [1, 2]
...
...
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