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afba3a2d
编写于
11月 11, 2016
作者:
Y
Yu Yang
浏览文件
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差异文件
Merge branch 'develop' of github.com:baidu/Paddle into feature/fix_pydataprovider_multiple_obj_bugs
上级
ae7452f4
3fc338ad
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
3 addition
and
4 deletion
+3
-4
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+3
-4
未找到文件。
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
afba3a2d
...
@@ -1827,7 +1827,6 @@ def img_pool_layer(input, pool_size, name=None,
...
@@ -1827,7 +1827,6 @@ def img_pool_layer(input, pool_size, name=None,
@
layer_support
()
@
layer_support
()
def
spp_layer
(
input
,
name
=
None
,
num_channels
=
None
,
pool_type
=
None
,
def
spp_layer
(
input
,
name
=
None
,
num_channels
=
None
,
pool_type
=
None
,
pyramid_height
=
None
,
img_width
=
None
,
layer_attr
=
None
):
pyramid_height
=
None
,
img_width
=
None
,
layer_attr
=
None
):
pass
"""
"""
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
The details please refer to
The details please refer to
...
@@ -1864,7 +1863,7 @@ def spp_layer(input, name=None, num_channels=None, pool_type=None,
...
@@ -1864,7 +1863,7 @@ def spp_layer(input, name=None, num_channels=None, pool_type=None,
if
(
isinstance
(
pool_type
,
AvgPooling
)
or
isinstance
(
pool_type
,
MaxPooling
)):
if
(
isinstance
(
pool_type
,
AvgPooling
)
or
isinstance
(
pool_type
,
MaxPooling
)):
type_name
+=
'-projection'
type_name
+=
'-projection'
Layer
(
l
=
Layer
(
name
=
name
,
name
=
name
,
type
=
LayerType
.
SPP_LAYER
,
type
=
LayerType
.
SPP_LAYER
,
inputs
=
Input
(
input
.
name
,
inputs
=
Input
(
input
.
name
,
...
@@ -1875,8 +1874,8 @@ def spp_layer(input, name=None, num_channels=None, pool_type=None,
...
@@ -1875,8 +1874,8 @@ def spp_layer(input, name=None, num_channels=None, pool_type=None,
),
),
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
)
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
)
)
)
return
LayerOutput
(
name
,
LayerType
.
SPP_LAYER
,
parents
=
[
input
],
return
LayerOutput
(
name
,
layer_type
=
LayerType
.
SPP_LAYER
,
parents
=
[
input
],
num_filters
=
num_channels
)
num_filters
=
num_channels
,
size
=
l
.
config
.
size
)
def
__img_norm_layer__
(
name
,
input
,
size
,
norm_type
,
scale
,
power
,
def
__img_norm_layer__
(
name
,
input
,
size
,
norm_type
,
scale
,
power
,
...
...
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