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211f83fa
编写于
7月 04, 2017
作者:
Z
zlx
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电子邮件补丁
差异文件
set depthwise conv layer interface in python
上级
6fd41f7b
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
147 addition
and
0 deletion
+147
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+57
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+90
-0
未找到文件。
python/paddle/trainer/config_parser.py
浏览文件 @
211f83fa
...
...
@@ -1741,6 +1741,59 @@ class ParameterReluLayer(LayerBase):
self
.
create_input_parameter
(
0
,
input_layer
.
size
/
partial_sum
)
@
config_layer
(
'depthwise_conv'
)
class
DepthwiseConvLayer
(
LayerBase
):
layer_type
=
'depthwise_conv'
def
__init__
(
self
,
name
,
inputs
=
[],
bias
=
True
,
num_filters
=
None
,
shared_biases
=
False
,
**
xargs
):
super
(
DepthwiseConvLayer
,
self
).
__init__
(
name
,
self
.
layer_type
,
0
,
inputs
=
inputs
,
**
xargs
)
if
num_filters
is
not
None
:
self
.
config
.
num_filters
=
num_filters
use_gpu
=
int
(
g_command_config_args
.
get
(
"use_gpu"
,
0
))
parallel_nn
=
int
(
g_command_config_args
.
get
(
"parallel_nn"
,
0
))
# Automatically select cudnn_type for GPU and exconv for CPU
# if set type=conv, but still reserve the way user specify
# exconv or cudnn_conv manually.
self
.
layer_type
=
"depthwise_conv"
# need to specify layer in config
self
.
config
.
type
=
self
.
layer_type
if
shared_biases
is
not
None
:
self
.
config
.
shared_biases
=
shared_biases
for
input_index
in
xrange
(
len
(
self
.
inputs
)):
input_layer
=
self
.
get_input_layer
(
input_index
)
conv_conf
=
self
.
config
.
inputs
[
input_index
].
conv_conf
#set the groups
self
.
inputs
[
input_index
].
conv
.
groups
=
self
.
inputs
[
input_index
].
conv
.
channels
parse_conv
(
self
.
inputs
[
input_index
].
conv
,
input_layer
.
name
,
conv_conf
,
num_filters
)
psize
=
self
.
calc_parameter_size
(
conv_conf
)
self
.
create_input_parameter
(
input_index
,
psize
)
self
.
set_cnn_layer
(
name
,
conv_conf
.
output_y
,
conv_conf
.
output_x
,
self
.
config
.
num_filters
)
psize
=
self
.
config
.
size
if
shared_biases
:
psize
=
self
.
config
.
num_filters
self
.
create_bias_parameter
(
bias
,
psize
,
[
psize
,
1
])
def
calc_parameter_size
(
self
,
conv_conf
):
return
self
.
config
.
num_filters
*
conv_conf
.
filter_channels
\
*
(
conv_conf
.
filter_size
*
conv_conf
.
filter_size_y
)
@
config_layer
(
'conv'
)
class
ConvLayerBase
(
LayerBase
):
layer_type
=
'conv'
...
...
@@ -3145,6 +3198,10 @@ def ParameterHook(type, **kwargs):
if
sparsity_ratio
is
not
None
:
hook
.
sparsity_ratio
=
sparsity_ratio
return
hook
elif
type
==
'dpruning'
:
hook
=
ParameterUpdaterHookConfig
()
hook
.
type
=
type
return
hook
else
:
return
None
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
211f83fa
...
...
@@ -57,6 +57,7 @@ __all__ = [
'classification_cost'
,
'LayerOutput'
,
'img_conv_layer'
,
'img_depthwise_conv_layer'
,
'img_pool_layer'
,
'batch_norm_layer'
,
'img_cmrnorm_layer'
,
...
...
@@ -148,6 +149,7 @@ class LayerType(object):
HSIGMOID
=
'hsigmoid'
CONV_LAYER
=
'conv'
CONVTRANS_LAYER
=
'convt'
DEPTHWISE_CONV_LAYER
=
'depthwise_conv'
EXCONV_LAYER
=
'exconv'
EXCONVTRANS_LAYER
=
'exconvt'
CUDNNCONV_LAYER
=
'cudnn_conv'
...
...
@@ -2085,6 +2087,94 @@ def hsigmoid(input,
name
,
LayerType
.
HSIGMOID
,
parents
=
parents
,
size
=
l
.
config
.
size
)
@
wrap_name_default
(
"depthwise_conv"
)
@
wrap_param_attr_default
()
@
wrap_bias_attr_default
()
@
wrap_act_default
(
act
=
ReluActivation
())
@
layer_support
(
DROPOUT
)
def
img_depthwise_conv_layer
(
input
,
filter_size
,
num_filters
,
name
=
None
,
num_channels
=
None
,
act
=
None
,
groups
=
1
,
stride
=
1
,
padding
=
0
,
bias_attr
=
None
,
param_attr
=
None
,
shared_biases
=
True
,
layer_attr
=
None
,
filter_size_y
=
None
,
stride_y
=
None
,
padding_y
=
None
,
trans
=
False
,
layer_type
=
None
):
if
num_channels
is
None
:
assert
input
.
num_filters
is
not
None
num_channels
=
input
.
num_filters
if
filter_size_y
is
None
:
if
isinstance
(
filter_size
,
collections
.
Sequence
):
assert
len
(
filter_size
)
==
2
filter_size
,
filter_size_y
=
filter_size
else
:
filter_size_y
=
filter_size
if
stride_y
is
None
:
if
isinstance
(
stride
,
collections
.
Sequence
):
assert
len
(
stride
)
==
2
stride
,
stride_y
=
stride
else
:
stride_y
=
stride
if
padding_y
is
None
:
if
isinstance
(
padding
,
collections
.
Sequence
):
assert
len
(
padding
)
==
2
padding
,
padding_y
=
padding
else
:
padding_y
=
padding
if
param_attr
.
attr
.
get
(
'initial_smart'
):
# special initial for conv layers.
init_w
=
(
2.0
/
(
filter_size
**
2
*
num_channels
))
**
0.5
param_attr
.
attr
[
"initial_mean"
]
=
0.0
param_attr
.
attr
[
"initial_std"
]
=
init_w
param_attr
.
attr
[
"initial_strategy"
]
=
0
param_attr
.
attr
[
"initial_smart"
]
=
False
lt
=
LayerType
.
DEPTHWISE_CONV_LAYER
l
=
Layer
(
name
=
name
,
inputs
=
Input
(
input
.
name
,
conv
=
Conv
(
filter_size
=
filter_size
,
padding
=
padding
,
stride
=
stride
,
channels
=
num_channels
,
groups
=
groups
,
filter_size_y
=
filter_size_y
,
padding_y
=
padding_y
,
stride_y
=
stride_y
),
**
param_attr
.
attr
),
active_type
=
act
.
name
,
num_filters
=
num_filters
,
bias
=
ParamAttr
.
to_bias
(
bias_attr
),
shared_biases
=
shared_biases
,
type
=
lt
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
,
lt
,
parents
=
[
input
],
activation
=
act
,
num_filters
=
num_filters
,
size
=
l
.
config
.
size
)
@
wrap_name_default
(
"conv"
)
@
wrap_param_attr_default
()
@
wrap_bias_attr_default
()
...
...
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