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06056fe2
编写于
2月 27, 2017
作者:
L
Luo Tao
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' into layer
上级
6f63eb9f
d425a5ca
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
286 addition
and
29 deletion
+286
-29
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+85
-22
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+113
-7
python/paddle/v2/tests/test_layer.py
python/paddle/v2/tests/test_layer.py
+88
-0
未找到文件。
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
06056fe2
...
...
@@ -30,28 +30,90 @@ except ImportError:
import
copy
__all__
=
[
"full_matrix_projection"
,
"AggregateLevel"
,
"ExpandLevel"
,
"identity_projection"
,
"dotmul_projection"
,
"dotmul_operator"
,
"repeat_layer"
,
"seq_reshape_layer"
,
"table_projection"
,
"mixed_layer"
,
"data_layer"
,
"embedding_layer"
,
"fc_layer"
,
"grumemory"
,
"pooling_layer"
,
"lstmemory"
,
"last_seq"
,
"first_seq"
,
"cos_sim"
,
"hsigmoid"
,
"conv_projection"
,
"regression_cost"
,
'classification_cost'
,
"LayerOutput"
,
'img_conv_layer'
,
'img_pool_layer'
,
'batch_norm_layer'
,
'img_cmrnorm_layer'
,
'addto_layer'
,
'concat_layer'
,
'seq_concat_layer'
,
'lstm_step_layer'
,
'recurrent_group'
,
'memory'
,
'StaticInput'
,
'expand_layer'
,
'scaling_layer'
,
'scaling_projection'
,
'power_layer'
,
'interpolation_layer'
,
'bilinear_interp_layer'
,
'trans_layer'
,
'rotate_layer'
,
'sum_to_one_norm_layer'
,
'get_output_layer'
,
'LayerType'
,
'context_projection'
,
'beam_search'
,
'maxid_layer'
,
'GeneratedInput'
,
'SubsequenceInput'
,
'gru_step_layer'
,
'recurrent_layer'
,
'BaseGeneratedInput'
,
'conv_operator'
,
'conv_shift_layer'
,
'tensor_layer'
,
'selective_fc_layer'
,
'sampling_id_layer'
,
'slope_intercept_layer'
,
'trans_full_matrix_projection'
,
'linear_comb_layer'
,
'convex_comb_layer'
,
'ctc_layer'
,
'warp_ctc_layer'
,
'crf_layer'
,
'crf_decoding_layer'
,
'nce_layer'
,
'cross_entropy_with_selfnorm'
,
'cross_entropy'
,
'multi_binary_label_cross_entropy'
,
'sum_cost'
,
'rank_cost'
,
'lambda_cost'
,
'huber_cost'
,
'block_expand_layer'
,
'maxout_layer'
,
'out_prod_layer'
,
'print_layer'
,
'priorbox_layer'
,
'spp_layer'
,
'pad_layer'
,
'eos_layer'
"full_matrix_projection"
,
"AggregateLevel"
,
"ExpandLevel"
,
"identity_projection"
,
"dotmul_projection"
,
"dotmul_operator"
,
"repeat_layer"
,
"seq_reshape_layer"
,
"table_projection"
,
"mixed_layer"
,
"data_layer"
,
"embedding_layer"
,
"fc_layer"
,
"grumemory"
,
"pooling_layer"
,
"lstmemory"
,
"last_seq"
,
"first_seq"
,
"cos_sim"
,
"hsigmoid"
,
"conv_projection"
,
"regression_cost"
,
'classification_cost'
,
"LayerOutput"
,
'img_conv_layer'
,
'img_pool_layer'
,
'batch_norm_layer'
,
'img_cmrnorm_layer'
,
'addto_layer'
,
'concat_layer'
,
'seq_concat_layer'
,
'lstm_step_layer'
,
'recurrent_group'
,
'memory'
,
'StaticInput'
,
'expand_layer'
,
'scaling_layer'
,
'scaling_projection'
,
'power_layer'
,
'interpolation_layer'
,
'bilinear_interp_layer'
,
'trans_layer'
,
'rotate_layer'
,
'sum_to_one_norm_layer'
,
'get_output_layer'
,
'LayerType'
,
'context_projection'
,
'beam_search'
,
'maxid_layer'
,
'GeneratedInput'
,
'SubsequenceInput'
,
'gru_step_layer'
,
'recurrent_layer'
,
'BaseGeneratedInput'
,
'conv_operator'
,
'conv_shift_layer'
,
'tensor_layer'
,
'selective_fc_layer'
,
'sampling_id_layer'
,
'slope_intercept_layer'
,
'trans_full_matrix_projection'
,
'linear_comb_layer'
,
'convex_comb_layer'
,
'ctc_layer'
,
'warp_ctc_layer'
,
'crf_layer'
,
'crf_decoding_layer'
,
'nce_layer'
,
'cross_entropy_with_selfnorm'
,
'cross_entropy'
,
'multi_binary_label_cross_entropy'
,
'sum_cost'
,
'rank_cost'
,
'lambda_cost'
,
'huber_cost'
,
'block_expand_layer'
,
'maxout_layer'
,
'out_prod_layer'
,
'print_layer'
,
'priorbox_layer'
,
'spp_layer'
,
'pad_layer'
,
'eos_layer'
,
'layer_support'
,
]
...
...
@@ -648,6 +710,7 @@ class MixedLayerType(LayerOutput):
# update the size which might be computed inside MixedLayer
# according to the operator's output size
self
.
size
=
ml
.
config
.
size
self
.
finalized
=
True
@
wrap_name_default
(
"mixed"
)
...
...
python/paddle/v2/layer.py
浏览文件 @
06056fe2
...
...
@@ -71,9 +71,15 @@ import collections
import
paddle.trainer_config_helpers
as
conf_helps
from
paddle.trainer_config_helpers.config_parser_utils
import
\
parse_network_config
as
__parse__
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
from
paddle.trainer_config_helpers.default_decorators
import
wrap_act_default
from
paddle.trainer_config_helpers.default_decorators
import
wrap_bias_attr_default
from
paddle.trainer_config_helpers.layers
import
layer_support
import
data_type
import
activation
import
attr
__all__
=
[
'parse_network'
,
'data'
,
'fc'
,
'conv_shift'
,
'img_conv'
,
'img_pool'
,
'spp'
,
...
...
@@ -89,6 +95,13 @@ __all__ = [
'hsigmoid'
,
'eos'
]
__projection_names__
=
filter
(
lambda
x
:
x
.
endswith
(
'_projection'
),
dir
(
conf_helps
))
__all__
+=
__projection_names__
__operator_names__
=
filter
(
lambda
x
:
x
.
endswith
(
'_operator'
),
dir
(
conf_helps
))
__all__
+=
__operator_names__
def
parse_network
(
*
outputs
):
"""
...
...
@@ -106,9 +119,8 @@ def parse_network(*outputs):
class
Layer
(
object
):
def
__init__
(
self
,
name
,
parent_layers
):
def
__init__
(
self
,
name
=
None
,
parent_layers
=
None
):
assert
isinstance
(
parent_layers
,
dict
)
assert
isinstance
(
name
,
basestring
)
self
.
name
=
name
self
.
__parent_layers__
=
parent_layers
...
...
@@ -127,19 +139,25 @@ class Layer(object):
self
.
__parent_layers__
[
layer_name
])
kwargs
[
layer_name
]
=
v1_layer
if
self
.
name
not
in
context
:
if
self
.
name
is
None
:
return
self
.
to_proto_impl
(
**
kwargs
)
elif
self
.
name
not
in
context
:
context
[
self
.
name
]
=
self
.
to_proto_impl
(
**
kwargs
)
return
context
[
self
.
name
]
def
to_proto_impl
(
self
,
**
kwargs
):
raise
NotImplementedError
()
def
__convert_to_v2__
(
method_name
,
parent_names
):
wrapper
=
wrap_name_default
(
name_prefix
=
method_name
)
def
__convert_to_v2__
(
method_name
,
parent_names
,
is_default_name
=
True
):
if
is_default_name
:
wrapper
=
wrap_name_default
(
name_prefix
=
method_name
)
else
:
wrapper
=
None
class
V2LayerImpl
(
Layer
):
def
__init__
(
self
,
name
=
None
,
**
kwargs
):
def
__init__
(
self
,
**
kwargs
):
parent_layers
=
dict
()
other_kwargs
=
dict
()
for
pname
in
parent_names
:
...
...
@@ -150,6 +168,7 @@ def __convert_to_v2__(method_name, parent_names):
if
key
not
in
parent_names
:
other_kwargs
[
key
]
=
kwargs
[
key
]
name
=
kwargs
.
get
(
'name'
,
None
)
super
(
V2LayerImpl
,
self
).
__init__
(
name
,
parent_layers
)
self
.
__other_kwargs__
=
other_kwargs
...
...
@@ -162,7 +181,7 @@ def __convert_to_v2__(method_name, parent_names):
args
[
each
]
=
kwargs
[
each
]
for
each
in
self
.
__other_kwargs__
:
args
[
each
]
=
self
.
__other_kwargs__
[
each
]
return
getattr
(
conf_helps
,
method_name
)(
name
=
self
.
name
,
**
args
)
return
getattr
(
conf_helps
,
method_name
)(
**
args
)
return
V2LayerImpl
...
...
@@ -193,6 +212,78 @@ class DataLayerV2(Layer):
return
getattr
(
conf_helps
,
self
.
__method_name__
)(
name
=
self
.
name
,
**
args
)
class
MixedLayerV2
(
Layer
):
"""
This class is use to support `with` grammar. If not, the following code
could convert mixed_layer simply.
mixed = __convert_to_v2__(
'mixed_layer', name_prefix='mixed', parent_names=['input'])
"""
class
AddToSealedMixedLayerExceptionV2
(
Exception
):
pass
def
__init__
(
self
,
size
=
0
,
input
=
None
,
name
=
None
,
act
=
None
,
bias_attr
=
None
,
layer_attr
=
None
):
self
.
__method_name__
=
'mixed_layer'
self
.
finalized
=
False
self
.
__inputs__
=
[]
if
input
is
not
None
:
self
.
__inputs__
=
input
other_kwargs
=
dict
()
other_kwargs
[
'name'
]
=
name
other_kwargs
[
'size'
]
=
size
other_kwargs
[
'act'
]
=
act
other_kwargs
[
'bias_attr'
]
=
bias_attr
other_kwargs
[
'layer_attr'
]
=
layer_attr
parent_layers
=
{
"input"
:
self
.
__inputs__
}
super
(
MixedLayerV2
,
self
).
__init__
(
name
,
parent_layers
)
self
.
__other_kwargs__
=
other_kwargs
def
__iadd__
(
self
,
other
):
if
not
self
.
finalized
:
self
.
__inputs__
.
append
(
other
)
return
self
else
:
raise
MixedLayerTypeV2
.
AddToSealedMixedLayerExceptionV2
()
def
__enter__
(
self
):
assert
len
(
self
.
__inputs__
)
==
0
return
self
def
__exit__
(
self
,
*
args
,
**
kwargs
):
self
.
finalized
=
True
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
for
each
in
self
.
__other_kwargs__
:
args
[
each
]
=
self
.
__other_kwargs__
[
each
]
return
getattr
(
conf_helps
,
self
.
__method_name__
)(
**
args
)
@
wrap_name_default
(
"mixed"
)
@
wrap_act_default
(
act
=
activation
.
Linear
())
@
wrap_bias_attr_default
(
has_bias
=
False
)
@
layer_support
(
conf_helps
.
layers
.
ERROR_CLIPPING
,
conf_helps
.
layers
.
DROPOUT
)
def
mixed
(
size
=
0
,
name
=
None
,
input
=
None
,
act
=
None
,
bias_attr
=
False
,
layer_attr
=
None
):
return
MixedLayerV2
(
size
,
input
,
name
,
act
,
bias_attr
,
layer_attr
)
data
=
DataLayerV2
AggregateLevel
=
conf_helps
.
layers
.
AggregateLevel
ExpandLevel
=
conf_helps
.
layers
.
ExpandLevel
...
...
@@ -274,3 +365,18 @@ layer_list = [
]
for
l
in
layer_list
:
globals
()[
l
[
0
]]
=
__convert_to_v2__
(
l
[
1
],
l
[
2
])
# convert projection
for
prj
in
__projection_names__
:
globals
()[
prj
]
=
__convert_to_v2__
(
prj
,
parent_names
=
[
'input'
],
is_default_name
=
False
)
# convert operator
operator_list
=
[
# [V1_method_name, parent_names],
[
'dotmul_operator'
,
[
'a'
,
'b'
]],
[
'conv_operator'
,
[
'img'
,
'filter'
]]
]
for
op
in
operator_list
:
globals
()[
op
[
0
]]
=
__convert_to_v2__
(
op
[
0
],
parent_names
=
op
[
1
],
is_default_name
=
False
)
python/paddle/v2/tests/test_layer.py
浏览文件 @
06056fe2
...
...
@@ -167,5 +167,93 @@ class OtherLayerTest(unittest.TestCase):
print
layer
.
parse_network
(
pad
)
class
ProjOpTest
(
unittest
.
TestCase
):
def
test_projection
(
self
):
input
=
layer
.
data
(
name
=
'data'
,
type
=
data_type
.
dense_vector
(
784
))
word
=
layer
.
data
(
name
=
'word'
,
type
=
data_type
.
integer_value_sequence
(
10000
))
fc0
=
layer
.
fc
(
input
=
input
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc1
=
layer
.
fc
(
input
=
input
,
size
=
200
,
act
=
activation
.
Sigmoid
())
mixed0
=
layer
.
mixed
(
size
=
256
,
input
=
[
layer
.
full_matrix_projection
(
input
=
fc0
),
layer
.
full_matrix_projection
(
input
=
fc1
)
])
with
layer
.
mixed
(
size
=
200
)
as
mixed1
:
mixed1
+=
layer
.
full_matrix_projection
(
input
=
fc0
)
mixed1
+=
layer
.
identity_projection
(
input
=
fc1
)
table
=
layer
.
table_projection
(
input
=
word
)
emb0
=
layer
.
mixed
(
size
=
512
,
input
=
table
)
with
layer
.
mixed
(
size
=
512
)
as
emb1
:
emb1
+=
table
scale
=
layer
.
scaling_projection
(
input
=
fc0
)
scale0
=
layer
.
mixed
(
size
=
100
,
input
=
scale
)
with
layer
.
mixed
(
size
=
100
)
as
scale1
:
scale1
+=
scale
dotmul
=
layer
.
dotmul_projection
(
input
=
fc0
)
dotmul0
=
layer
.
mixed
(
size
=
100
,
input
=
dotmul
)
with
layer
.
mixed
(
size
=
100
)
as
dotmul1
:
dotmul1
+=
dotmul
context
=
layer
.
context_projection
(
input
=
fc0
,
context_len
=
5
)
context0
=
layer
.
mixed
(
size
=
100
,
input
=
context
)
with
layer
.
mixed
(
size
=
100
)
as
context1
:
context1
+=
context
conv
=
layer
.
conv_projection
(
input
=
input
,
filter_size
=
1
,
num_channels
=
1
,
num_filters
=
128
,
stride
=
1
,
padding
=
0
)
conv0
=
layer
.
mixed
(
input
=
conv
,
bias_attr
=
True
)
with
layer
.
mixed
(
bias_attr
=
True
)
as
conv1
:
conv1
+=
conv
print
layer
.
parse_network
(
mixed0
)
print
layer
.
parse_network
(
mixed1
)
print
layer
.
parse_network
(
emb0
)
print
layer
.
parse_network
(
emb1
)
print
layer
.
parse_network
(
scale0
)
print
layer
.
parse_network
(
scale1
)
print
layer
.
parse_network
(
dotmul0
)
print
layer
.
parse_network
(
dotmul1
)
print
layer
.
parse_network
(
conv0
)
print
layer
.
parse_network
(
conv1
)
def
test_operator
(
self
):
ipt0
=
layer
.
data
(
name
=
'data'
,
type
=
data_type
.
dense_vector
(
784
))
ipt1
=
layer
.
data
(
name
=
'word'
,
type
=
data_type
.
dense_vector
(
128
))
fc0
=
layer
.
fc
(
input
=
ipt0
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc1
=
layer
.
fc
(
input
=
ipt0
,
size
=
100
,
act
=
activation
.
Sigmoid
())
dotmul_op
=
layer
.
dotmul_operator
(
a
=
fc0
,
b
=
fc1
)
dotmul0
=
layer
.
mixed
(
input
=
dotmul_op
)
with
layer
.
mixed
()
as
dotmul1
:
dotmul1
+=
dotmul_op
conv
=
layer
.
conv_operator
(
img
=
ipt0
,
filter
=
ipt1
,
filter_size
=
1
,
num_channels
=
1
,
num_filters
=
128
,
stride
=
1
,
padding
=
0
)
conv0
=
layer
.
mixed
(
input
=
conv
)
with
layer
.
mixed
()
as
conv1
:
conv1
+=
conv
print
layer
.
parse_network
(
dotmul0
)
print
layer
.
parse_network
(
dotmul1
)
print
layer
.
parse_network
(
conv0
)
print
layer
.
parse_network
(
conv1
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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