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0ef86cbd
编写于
7年前
作者:
E
emailweixu
提交者:
GitHub
7年前
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #2288 from emailweixu/fix_v2_api
Fix V2 API
上级
94d83fcd
97c4d23f
变更
13
隐藏空白更改
内联
并排
Showing
13 changed file
with
344 addition
and
861 deletion
+344
-861
paddle/parameter/Parameter.h
paddle/parameter/Parameter.h
+1
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+17
-14
python/paddle/trainer_config_helpers/config_parser_utils.py
python/paddle/trainer_config_helpers/config_parser_utils.py
+17
-4
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+6
-0
python/paddle/v2/config_base.py
python/paddle/v2/config_base.py
+48
-199
python/paddle/v2/evaluator.py
python/paddle/v2/evaluator.py
+3
-14
python/paddle/v2/inference.py
python/paddle/v2/inference.py
+12
-12
python/paddle/v2/layer.py
python/paddle/v2/layer.py
+204
-542
python/paddle/v2/networks.py
python/paddle/v2/networks.py
+1
-14
python/paddle/v2/tests/test_layer.py
python/paddle/v2/tests/test_layer.py
+9
-9
python/paddle/v2/tests/test_rnn_layer.py
python/paddle/v2/tests/test_rnn_layer.py
+9
-0
python/paddle/v2/tests/test_topology.py
python/paddle/v2/tests/test_topology.py
+6
-6
python/paddle/v2/topology.py
python/paddle/v2/topology.py
+11
-47
未找到文件。
paddle/parameter/Parameter.h
浏览文件 @
0ef86cbd
...
@@ -324,6 +324,7 @@ protected:
...
@@ -324,6 +324,7 @@ protected:
std
::
vector
<
std
::
shared_ptr
<
IParameterUpdaterHook
>>
updaterHooks_
;
std
::
vector
<
std
::
shared_ptr
<
IParameterUpdaterHook
>>
updaterHooks_
;
public:
public:
void
setSharedCount
(
int
cnt
)
{
sharedCount_
=
cnt
;
}
int
getSharedCount
()
{
return
sharedCount_
;
}
int
getSharedCount
()
{
return
sharedCount_
;
}
bool
isSparse
()
{
return
config_
.
is_sparse
();
}
bool
isSparse
()
{
return
config_
.
is_sparse
();
}
...
...
This diff is collapsed.
Click to expand it.
python/paddle/trainer/config_parser.py
浏览文件 @
0ef86cbd
...
@@ -3371,7 +3371,7 @@ def make_importer(config_dir, config_args):
...
@@ -3371,7 +3371,7 @@ def make_importer(config_dir, config_args):
return
Import
return
Import
settings
=
dict
(
DEFAULT_SETTING
=
dict
(
batch_size
=
None
,
batch_size
=
None
,
mini_batch_size
=
None
,
mini_batch_size
=
None
,
algorithm
=
'async_sgd'
,
algorithm
=
'async_sgd'
,
...
@@ -3404,6 +3404,8 @@ settings = dict(
...
@@ -3404,6 +3404,8 @@ settings = dict(
adam_beta2
=
0.999
,
adam_beta2
=
0.999
,
adam_epsilon
=
1e-8
,
)
adam_epsilon
=
1e-8
,
)
settings
=
copy
.
deepcopy
(
DEFAULT_SETTING
)
settings_deprecated
=
dict
(
usage_ratio
=
1.
,
)
settings_deprecated
=
dict
(
usage_ratio
=
1.
,
)
trainer_settings
=
dict
(
trainer_settings
=
dict
(
...
@@ -3544,10 +3546,8 @@ def update_g_config():
...
@@ -3544,10 +3546,8 @@ def update_g_config():
return
g_config
return
g_config
def
parse_config
(
trainer_config
,
config_arg_str
):
def
begin_parse
(
config_arg_str
=
''
):
'''
'''
@param trainer_config: can be a string of config file name or a function name
with config logic
@param config_arg_str: a string of the form var1=val1,var2=val2. It will be
@param config_arg_str: a string of the form var1=val1,var2=val2. It will be
passed to config script as a dictionary CONFIG_ARGS
passed to config script as a dictionary CONFIG_ARGS
'''
'''
...
@@ -3555,12 +3555,23 @@ def parse_config(trainer_config, config_arg_str):
...
@@ -3555,12 +3555,23 @@ def parse_config(trainer_config, config_arg_str):
for
hook
in
_parse_config_hooks
:
for
hook
in
_parse_config_hooks
:
hook
()
hook
()
config_args
=
{}
logger
.
findCaller
=
find_caller
logger
.
findCaller
=
find_caller
logger
.
fatal
=
my_fatal
logger
.
fatal
=
my_fatal
g_config
.
model_config
.
type
=
"nn"
g_config
.
model_config
.
type
=
"nn"
global
g_current_submodel
,
g_root_submodel
g_root_submodel
=
g_config
.
model_config
.
sub_models
.
add
()
g_root_submodel
.
name
=
'root'
g_root_submodel
.
is_recurrent_layer_group
=
False
g_current_submodel
=
g_root_submodel
def
parse_config
(
trainer_config
,
config_arg_str
):
begin_parse
(
config_arg_str
)
config_args
=
{}
if
config_arg_str
:
if
config_arg_str
:
config_args
=
dict
([
f
.
split
(
'='
)
for
f
in
config_arg_str
.
split
(
','
)])
config_args
=
dict
([
f
.
split
(
'='
)
for
f
in
config_arg_str
.
split
(
','
)])
...
@@ -3573,14 +3584,6 @@ def parse_config(trainer_config, config_arg_str):
...
@@ -3573,14 +3584,6 @@ def parse_config(trainer_config, config_arg_str):
extension_module
=
importlib
(
extension_module_name
)
extension_module
=
importlib
(
extension_module_name
)
g_extended_config_funcs
=
extension_module
.
get_config_funcs
(
g_config
)
g_extended_config_funcs
=
extension_module
.
get_config_funcs
(
g_config
)
g_config
.
model_config
.
type
=
'nn'
global
g_current_submodel
,
g_root_submodel
g_root_submodel
=
g_config
.
model_config
.
sub_models
.
add
()
g_root_submodel
.
name
=
'root'
g_root_submodel
.
is_recurrent_layer_group
=
False
g_current_submodel
=
g_root_submodel
if
hasattr
(
trainer_config
,
'__call__'
):
if
hasattr
(
trainer_config
,
'__call__'
):
trainer_config
.
func_globals
.
update
(
trainer_config
.
func_globals
.
update
(
make_config_environment
(
""
,
config_args
))
make_config_environment
(
""
,
config_args
))
...
...
This diff is collapsed.
Click to expand it.
python/paddle/trainer_config_helpers/config_parser_utils.py
浏览文件 @
0ef86cbd
...
@@ -12,15 +12,18 @@
...
@@ -12,15 +12,18 @@
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
import
copy
import
paddle.trainer.config_parser
as
config_parser
import
paddle.trainer.config_parser
as
config_parser
from
paddle.proto.TrainerConfig_pb2
import
OptimizationConfig
'''
'''
This file is a wrapper of formal config_parser. The main idea of this file is to
This file is a wrapper of formal config_parser. The main idea of this file is to
separete different config logic into different function, such as network configuration
separete different config logic into different function, such as network configuration
and optimizer configuration.
and optimizer configuration.
'''
'''
__all__
=
[
__all__
=
[
"parse_trainer_config"
,
"parse_network_config"
,
"parse_optimizer_config"
"parse_trainer_config"
,
"parse_network_config"
,
"parse_optimizer_config"
,
"reset_parser"
]
]
...
@@ -34,5 +37,15 @@ def parse_network_config(network_conf, config_arg_str=''):
...
@@ -34,5 +37,15 @@ def parse_network_config(network_conf, config_arg_str=''):
def
parse_optimizer_config
(
optimizer_conf
,
config_arg_str
=
''
):
def
parse_optimizer_config
(
optimizer_conf
,
config_arg_str
=
''
):
config
=
config_parser
.
parse_config
(
optimizer_conf
,
config_arg_str
)
config_parser
.
settings
=
copy
.
deepcopy
(
config_parser
.
DEFAULT_SETTING
)
return
config
.
opt_config
optimizer_conf
()
opt_config
=
OptimizationConfig
()
for
k
,
v
in
config_parser
.
settings
.
iteritems
():
if
v
is
None
:
continue
opt_config
.
__setattr__
(
k
,
v
)
return
opt_config
def
reset_parser
():
config_parser
.
begin_parse
()
This diff is collapsed.
Click to expand it.
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
0ef86cbd
...
@@ -287,6 +287,7 @@ class LayerOutput(object):
...
@@ -287,6 +287,7 @@ class LayerOutput(object):
assert
size
is
not
None
assert
size
is
not
None
assert
LayerType
.
is_layer_type
(
layer_type
)
assert
LayerType
.
is_layer_type
(
layer_type
)
self
.
name
=
name
self
.
name
=
name
self
.
full_name
=
MakeLayerNameInSubmodel
(
name
)
self
.
layer_type
=
layer_type
self
.
layer_type
=
layer_type
if
parents
is
not
None
and
type
(
parents
)
!=
list
:
if
parents
is
not
None
and
type
(
parents
)
!=
list
:
parents
=
[
parents
]
parents
=
[
parents
]
...
@@ -3491,6 +3492,11 @@ def recurrent_group(step,
...
@@ -3491,6 +3492,11 @@ def recurrent_group(step,
RecurrentLayerGroupEnd
(
name
=
name
)
RecurrentLayerGroupEnd
(
name
=
name
)
for
layer_out
in
layer_outs
:
# Thee previous full_name is the name is the rnn group
# We need a full_name outside the rnn group
layer_out
.
full_name
=
MakeLayerNameInSubmodel
(
layer_out
.
name
)
if
len
(
layer_outs
)
==
1
:
if
len
(
layer_outs
)
==
1
:
return
layer_outs
[
0
]
return
layer_outs
[
0
]
else
:
else
:
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/config_base.py
浏览文件 @
0ef86cbd
...
@@ -14,206 +14,55 @@
...
@@ -14,206 +14,55 @@
import
collections
import
collections
import
re
import
re
from
paddle.trainer_config_helpers.default_decorators
import
wrap_name_default
import
paddle.trainer_config_helpers
as
conf_helps
import
paddle.trainer_config_helpers
as
conf_helps
from
topology
import
Topology
__layer_map__
=
{}
class
LayerType
(
type
):
def
__new__
(
cls
,
name
,
bases
,
attrs
):
def
__map_docstr__
(
doc
,
name
):
method_name
=
attrs
.
get
(
'METHOD_NAME'
,
None
)
if
doc
is
None
:
if
method_name
is
not
None
:
method
=
getattr
(
conf_helps
,
method_name
)
if
method
.
__doc__
is
not
None
:
mapper
=
attrs
.
get
(
"__map_docstr__"
,
None
)
if
mapper
is
not
None
:
attrs
[
'__doc__'
]
=
LayerType
.
__map_docstr__
(
mapper
(
method
.
__doc__
),
method_name
=
method_name
,
name
=
name
)
else
:
attrs
[
'__doc__'
]
=
LayerType
.
__map_docstr__
(
method
.
__doc__
,
method_name
=
method_name
,
name
=
name
)
return
super
(
LayerType
,
cls
).
__new__
(
cls
,
name
,
bases
,
attrs
)
@
staticmethod
def
__map_docstr__
(
doc
,
name
,
method_name
):
assert
isinstance
(
doc
,
basestring
)
# replace LayerOutput to paddle.v2.config_base.Layer
doc
=
doc
.
replace
(
"LayerOutput"
,
"paddle.v2.config_base.Layer"
)
doc
=
doc
.
replace
(
'ParameterAttribute'
,
'paddle.v2.attr.ParameterAttribute'
)
doc
=
re
.
sub
(
r
'ExtraLayerAttribute[^\s]?'
,
'paddle.v2.attr.ExtraAttribute'
,
doc
)
# xxx_layer to xxx
doc
=
re
.
sub
(
r
"(?P<name>[a-z]+)_layer"
,
r
"\g<name>"
,
doc
)
# XxxxActivation to paddle.v2.Activation.Xxxx
doc
=
re
.
sub
(
r
"(?P<name>[A-Z][a-zA-Z]+)Activation"
,
r
"paddle.v2.Activation.\g<name>"
,
doc
)
# TODO(yuyang18): Add more rules if needed.
return
doc
return
doc
assert
isinstance
(
doc
,
basestring
)
# replace LayerOutput to paddle.v2.config_base.Layer
doc
=
doc
.
replace
(
"LayerOutput"
,
"paddle.v2.config_base.Layer"
)
doc
=
doc
.
replace
(
'ParameterAttribute'
,
'paddle.v2.attr.ParameterAttribute'
)
doc
=
re
.
sub
(
r
'ExtraLayerAttribute[^\s]?'
,
'paddle.v2.attr.ExtraAttribute'
,
doc
)
# xxx_layer to xxx
doc
=
re
.
sub
(
r
"(?P<name>[a-z]+)_layer"
,
r
"\g<name>"
,
doc
)
# XxxxActivation to paddle.v2.Activation.Xxxx
doc
=
re
.
sub
(
r
"(?P<name>[A-Z][a-zA-Z]+)Activation"
,
r
"paddle.v2.Activation.\g<name>"
,
doc
)
# xxx_evaluator to paddle.v2.evaluator.xxx
doc
=
re
.
sub
(
r
"(?P<name>[a-z]+)_evaluator"
,
r
"evaluator.\g<name>"
,
doc
)
# TODO(yuyang18): Add more rules if needed.
return
doc
def
__convert_to_v2__
(
f
,
name
,
module
):
def
wrapped
(
*
args
,
**
xargs
):
out
=
f
(
*
args
,
**
xargs
)
outs
=
out
if
not
isinstance
(
out
,
collections
.
Sequence
):
outs
=
[
out
]
for
l
in
outs
:
if
isinstance
(
l
,
conf_helps
.
LayerOutput
):
__layer_map__
[
l
.
full_name
]
=
l
return
out
wrapped
.
__doc__
=
__map_docstr__
(
f
.
__doc__
,
name
)
wrapped
.
__name__
=
name
wrapped
.
__module__
=
module
return
wrapped
class
Layer
(
object
):
Layer
=
conf_helps
.
LayerOutput
__metaclass__
=
LayerType
def
__init__
(
self
,
name
=
None
,
parent_layers
=
None
):
assert
isinstance
(
parent_layers
,
dict
)
self
.
name
=
name
self
.
__context__
=
{}
self
.
__parent_layers__
=
parent_layers
# some layer may have some extra parent layer
self
.
__extra_parent__
=
[]
# used for evaluator.
self
.
__children_layers__
=
[]
def
extra_parent
(
self
):
return
self
.
__extra_parent__
def
append_extra_parent
(
self
,
parent
):
self
.
__extra_parent__
.
append
(
parent
)
def
append_child
(
self
,
layer
,
parent_names
):
self
.
__children_layers__
.
append
((
layer
,
parent_names
))
def
to_proto
(
self
,
context
):
"""
function to set proto attribute
"""
self
.
__context__
=
context
# STEP: short cut if this layer is parsed before.
if
self
.
context_name
()
in
context
:
if
self
.
use_context_name
():
return
context
[
self
.
context_name
()]
else
:
return
context
[
self
.
name
]
# STEP: parse extra_parent that is not used by this layer but must
# be parsed before this layer.
for
p
in
self
.
__extra_parent__
:
p
.
to_proto
(
context
=
context
)
# STEP: parse parent that is used by this layer, get the result and
# insert into kwargs of the next layer's to_proto_impl method.
kwargs
=
dict
()
for
layer_name
in
self
.
__parent_layers__
:
if
not
isinstance
(
self
.
__parent_layers__
[
layer_name
],
collections
.
Sequence
):
v1_layer
=
self
.
__parent_layers__
[
layer_name
].
to_proto
(
context
=
context
)
else
:
v1_layer
=
map
(
lambda
x
:
x
.
to_proto
(
context
=
context
),
self
.
__parent_layers__
[
layer_name
])
kwargs
[
layer_name
]
=
v1_layer
# STEP: parse myself and add myself into context.
ret_val
=
self
.
to_proto_impl
(
**
kwargs
)
if
self
.
context_name
()
is
not
None
\
and
self
.
context_name
()
not
in
context
:
context
[
self
.
context_name
()]
=
ret_val
# STEP: parse children that should be pased after this layer.
for
layer
,
pnames
in
self
.
__children_layers__
:
drop
=
False
# child will only be parsed if all parents are in context.
for
pname
in
pnames
:
if
pname
not
in
context
:
drop
=
True
break
if
drop
:
continue
layer
.
to_proto
(
context
=
context
)
# STEP: return v1 layer result
if
self
.
context_name
()
is
None
:
return
ret_val
elif
self
.
use_context_name
():
return
context
[
self
.
context_name
()]
else
:
return
context
[
self
.
name
]
def
to_proto_impl
(
self
,
**
kwargs
):
raise
NotImplementedError
()
def
context_name
(
self
):
"""
Context name means the context which stores `to_proto_impl` result.
If multiple layer share same context_name, the `to_proto_impl` of them
will be invoked only once.
"""
return
self
.
name
def
use_context_name
(
self
):
return
False
def
calculate_size
(
self
):
"""
lazy calculate size of the layer, should be called when to_proto_impl of
this layer is called.
:return:
"""
return
self
.
__context__
[
self
.
context_name
()].
size
def
attr
(
self
):
topo
=
Topology
(
self
)
return
topo
.
get_layer_proto
(
self
.
name
)
def
__convert_to_v2__
(
method_name
,
parent_names
,
is_default_name
=
True
,
attach_parent
=
False
):
if
is_default_name
:
wrapper
=
wrap_name_default
(
name_prefix
=
method_name
)
else
:
wrapper
=
None
class
V2LayerImpl
(
Layer
):
METHOD_NAME
=
method_name
def
__init__
(
self
,
**
kwargs
):
parent_layers
=
dict
()
other_kwargs
=
dict
()
for
pname
in
parent_names
:
if
pname
in
kwargs
:
parent_layers
[
pname
]
=
kwargs
[
pname
]
if
attach_parent
:
pnames
=
[
x
.
context_name
()
for
x
in
parent_layers
.
values
()]
for
pname
in
parent_layers
:
layers
=
kwargs
[
pname
]
if
not
isinstance
(
layers
,
collections
.
Sequence
):
layers
=
[
layers
]
for
layer
in
layers
:
layer
.
append_child
(
self
,
pnames
)
for
key
in
kwargs
.
keys
():
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
if
wrapper
is
not
None
:
__init__
=
wrapper
(
__init__
)
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
,
method_name
)(
**
args
)
return
V2LayerImpl
This diff is collapsed.
Click to expand it.
python/paddle/v2/evaluator.py
浏览文件 @
0ef86cbd
...
@@ -13,8 +13,8 @@
...
@@ -13,8 +13,8 @@
# limitations under the License.
# limitations under the License.
import
paddle.trainer_config_helpers.evaluators
as
evs
import
paddle.trainer_config_helpers.evaluators
as
evs
import
inspect
from
config_base
import
__convert_to_v2__
from
config_base
import
__convert_to_v2__
import
inspect
__all__
=
[]
__all__
=
[]
...
@@ -25,21 +25,10 @@ def initialize():
...
@@ -25,21 +25,10 @@ def initialize():
for
__ev_name__
in
filter
(
lambda
x
:
x
.
endswith
(
'_evaluator'
),
evs
.
__all__
):
for
__ev_name__
in
filter
(
lambda
x
:
x
.
endswith
(
'_evaluator'
),
evs
.
__all__
):
__ev__
=
getattr
(
evs
,
__ev_name__
)
__ev__
=
getattr
(
evs
,
__ev_name__
)
if
hasattr
(
__ev__
,
'argspec'
):
argspec
=
__ev__
.
argspec
else
:
argspec
=
inspect
.
getargspec
(
__ev__
)
parent_names
=
filter
(
lambda
x
:
x
in
[
'input'
,
'label'
,
'weight'
],
argspec
.
args
)
v2_ev
=
__convert_to_v2__
(
__ev_name__
,
parent_names
=
parent_names
,
is_default_name
=
'name'
in
argspec
.
args
,
attach_parent
=
True
)
__new_name__
=
convert_to_new_name
(
__ev_name__
)
__new_name__
=
convert_to_new_name
(
__ev_name__
)
globals
()[
__new_name__
]
=
v2_ev
globals
()[
__new_name__
]
=
__convert_to_v2__
(
__ev__
,
__new_name__
,
__name__
)
globals
()[
__new_name__
].
__name__
=
__new_name__
globals
()[
__new_name__
].
__name__
=
__new_name__
__all__
.
append
(
__new_name__
)
__all__
.
append
(
__new_name__
)
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/inference.py
浏览文件 @
0ef86cbd
...
@@ -12,9 +12,9 @@ class Inference(object):
...
@@ -12,9 +12,9 @@ class Inference(object):
"""
"""
Inference combines neural network output and parameters together
Inference combines neural network output and parameters together
to do inference.
to do inference.
.. code-block:: python
.. code-block:: python
inferer = Inference(output_layer=prediction, parameters=parameters)
inferer = Inference(output_layer=prediction, parameters=parameters)
for data_batch in batches:
for data_batch in batches:
print inferer.infer(data_batch)
print inferer.infer(data_batch)
...
@@ -92,8 +92,8 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
...
@@ -92,8 +92,8 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
.. code-block:: python
.. code-block:: python
result = paddle.infer(output_layer=prediction,
result = paddle.infer(output_layer=prediction,
parameters=parameters,
parameters=parameters,
input=SomeData)
input=SomeData)
print result
print result
...
@@ -101,14 +101,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
...
@@ -101,14 +101,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
.. code-block:: python
.. code-block:: python
result = paddle.infer(output_layer=[prediction1, prediction2],
result = paddle.infer(output_layer=[prediction1, prediction2],
parameters=parameters,
parameters=parameters,
input=SomeData,
input=SomeData,
field=[id, value]])
field=[id, value]])
print result
print result
:param output_layer: output of the neural network that would be inferred
:param output_layer: output of the neural network that would be inferred
:type output_layer: paddle.v2.config_base.Layer or a list of
:type output_layer: paddle.v2.config_base.Layer or a list of
paddle.v2.config_base.Layer
paddle.v2.config_base.Layer
:param parameters: parameters of the neural network.
:param parameters: parameters of the neural network.
:type parameters: paddle.v2.parameters.Parameters
:type parameters: paddle.v2.parameters.Parameters
...
@@ -117,14 +117,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
...
@@ -117,14 +117,14 @@ def infer(output_layer, parameters, input, feeding=None, field='value'):
:type input: collections.Iterable
:type input: collections.Iterable
:param feeding: Reader dictionary. Default could generate from input
:param feeding: Reader dictionary. Default could generate from input
value.
value.
:param field: The prediction field. It should in [`value`, `id`, `prob`].
:param field: The prediction field. It should in [`value`, `id`, `prob`].
`value` and `prob` mean return the prediction probabilities,
`value` and `prob` mean return the prediction probabilities,
`id` means return the prediction labels. Default is `value`.
`id` means return the prediction labels. Default is `value`.
Note that `prob` only used when output_layer is beam_search
Note that `prob` only used when output_layer is beam_search
or max_id.
or max_id.
:type field: str
:type field: str
:return: The prediction result. If there are multiple outout_layers and fields,
:return: The prediction result. If there are multiple outout_layers and fields,
the return order is outout_layer1.field1, outout_layer2.field1, ...,
the return order is outout_layer1.field1, outout_layer2.field1, ...,
outout_layer1.field2, outout_layer2.field2 ...
outout_layer1.field2, outout_layer2.field2 ...
:rtype: numpy.ndarray
:rtype: numpy.ndarray
"""
"""
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/layer.py
浏览文件 @
0ef86cbd
...
@@ -32,392 +32,29 @@ The primary usage shows below.
...
@@ -32,392 +32,29 @@ The primary usage shows below.
"""
"""
import
collections
import
collections
import
inspect
import
copy
import
re
import
re
import
paddle.trainer_config_helpers.layers
as
v1_layers
import
paddle.trainer.config_parser
as
cp
from
paddle.proto.ModelConfig_pb2
import
ModelConfig
,
SubModelConfig
from
config_base
import
__convert_to_v2__
import
config_base
import
paddle.trainer_config_helpers
as
conf_helps
__all__
=
[
'data'
,
'parse_network'
]
from
paddle.trainer.config_parser
import
\
RecurrentLayerGroupWithoutOutLinksBegin
,
RecurrentLayerGroupSetOutLink
,
\
RecurrentLayerGroupEnd
,
model_type
from
paddle.trainer_config_helpers.config_parser_utils
import
\
parse_network_config
as
__parse__
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.default_decorators
import
wrap_name_default
from
paddle.trainer_config_helpers.layers
import
RecurrentLayerGroupSetGenerator
,
Generator
from
paddle.trainer_config_helpers.layers
import
layer_support
import
activation
import
attr
import
data_type
from
config_base
import
Layer
,
__convert_to_v2__
__all__
=
[
'parse_network'
,
'data'
]
def
__need_to_keep__
(
name
):
if
name
in
[
'StaticInput'
,
'LayerType'
,
'layer_support'
]:
return
False
return
True
def
parse_network
(
output_layers
,
extra_layers
=
None
):
def
__need_to_wrap__
(
name
):
"""
return
name
not
in
[
'AggregateLevel'
,
'ExpandLevel'
]
Parse all layers in the neural network graph and
then generate a ModelConfig object.
.. note::
This function is used internally in paddle.v2 module. User should never
invoke this method.
:param output_layers: Output layers.
:type output_layers: Layer
:param extra_layers: Some layers in the neural network graph are not in the
path of output_layers.
:type extra_layers: Layer
:return: A ModelConfig object instance.
:rtype: ModelConfig
"""
if
not
isinstance
(
output_layers
,
collections
.
Sequence
):
output_layers
=
[
output_layers
]
if
extra_layers
is
not
None
and
not
isinstance
(
extra_layers
,
collections
.
Sequence
):
extra_layers
=
[
extra_layers
]
def
__real_func__
():
"""
__real_func__ is the function that config_parser.parse invoked. It is
the plain old paddle configuration function.
"""
context
=
dict
()
real_output
=
[
each
.
to_proto
(
context
=
context
)
for
each
in
output_layers
]
if
extra_layers
is
not
None
:
extra_output
=
[
each
.
to_proto
(
context
=
context
)
for
each
in
extra_layers
]
conf_helps
.
outputs
(
real_output
)
return
__parse__
(
__real_func__
)
"""
def
__convert_name__
(
inname
):
Some layer may need some special config, and can not use __convert_to_v2__ to convert.
if
inname
==
'maxid_layer'
:
So we also need to implement some special LayerV2.
"""
class
DataLayerV2
(
Layer
):
METHOD_NAME
=
'data_layer'
def
__init__
(
self
,
name
,
type
,
**
kwargs
):
assert
isinstance
(
type
,
data_type
.
InputType
)
self
.
type
=
type
self
.
__method_name__
=
'data_layer'
self
.
__kwargs__
=
kwargs
super
(
DataLayerV2
,
self
).
__init__
(
name
=
name
,
parent_layers
=
dict
())
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
args
[
'size'
]
=
self
.
type
.
dim
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
for
each
in
self
.
__kwargs__
:
args
[
each
]
=
self
.
__kwargs__
[
each
]
return
getattr
(
conf_helps
,
self
.
__method_name__
)(
name
=
self
.
name
,
**
args
)
def
__map_docstr__
(
doc
):
doc
=
re
.
sub
(
r
'(data = [^\)]+)\).*'
,
"data = paddle.layer.data(name=
\"
input
\"
, "
"type=paddle.data_type.dense_vector(1000))"
,
doc
)
doc
=
re
.
sub
(
r
':param size:.*'
,
':param type: Data type of this data layer'
,
doc
)
doc
=
re
.
sub
(
r
':type size:.*'
,
":type size: paddle.v2.data_type.InputType"
,
doc
)
return
doc
class
MemoryV2
(
Layer
):
def
__init__
(
self
,
name
,
extra_input
=
None
,
**
kwargs
):
"""
Init memory object, if memory is inited inside recurrent_group step
function, it may depend on a boot_layer that should be initialized
outside recurrent_group, so we:
1. add RecurrentLayerInput to extra_parent of self.
2. add boot_layer to the extra_parent of RecurrentLayerInput.
:param extra_input: list of RecurrentLayerInput
:type extra_input: [RecurrentLayerInput]
"""
self
.
name
=
name
super
(
MemoryV2
,
self
).
__init__
(
name
=
name
,
parent_layers
=
dict
())
self
.
__kwargs__
=
kwargs
self
.
__boot_layer_name__
=
None
if
'boot_layer'
in
kwargs
:
begin_of_current_rnn
=
[]
# TODO(yuyang18): Fix inspect, it could be wrong when user invoke a
# function inside step.
st
=
inspect
.
stack
()
for
i
in
xrange
(
len
(
st
)):
locs
=
inspect
.
stack
()[
i
][
0
].
f_locals
keys
=
locs
.
keys
()
for
key
in
keys
:
val
=
locs
[
key
]
if
isinstance
(
val
,
RecurrentLayerInput
):
begin_of_current_rnn
.
append
(
val
)
elif
isinstance
(
val
,
collections
.
Sequence
):
for
v
in
val
:
if
isinstance
(
v
,
RecurrentLayerInput
):
begin_of_current_rnn
.
append
(
v
)
if
begin_of_current_rnn
:
break
assert
begin_of_current_rnn
is
not
None
for
extra
in
begin_of_current_rnn
:
self
.
append_extra_parent
(
extra
)
extra
.
append_extra_parent
(
kwargs
[
'boot_layer'
])
self
.
__boot_layer_name__
=
kwargs
[
'boot_layer'
].
name
def
to_proto_impl
(
self
,
**
kwargs
):
args
=
dict
()
for
each
in
kwargs
:
args
[
each
]
=
kwargs
[
each
]
for
each
in
self
.
__kwargs__
:
args
[
each
]
=
self
.
__kwargs__
[
each
]
if
self
.
__boot_layer_name__
is
not
None
:
args
[
'boot_layer'
]
=
self
.
__context__
[
self
.
__boot_layer_name__
]
size
=
args
.
get
(
'size'
,
None
)
if
size
is
not
None
:
if
callable
(
size
):
real_size
=
size
()
else
:
real_size
=
size
args
[
'size'
]
=
real_size
return
conf_helps
.
memory
(
name
=
self
.
name
,
**
args
)
def
context_name
(
self
):
return
self
.
name
+
"#memory"
def
use_context_name
(
self
):
"""
memory layer will have the same name with some layer
:return:
"""
return
True
class
StaticInputV2
(
object
):
def
__init__
(
self
,
input
,
is_seq
=
False
,
size
=
None
):
assert
isinstance
(
input
,
LayerV2
)
self
.
name
=
input
.
name
self
.
input
=
input
self
.
is_seq
=
is_seq
self
.
size
=
size
# TODO(add size check)
# assert input.size is not None or size is not None
class
BaseGeneratedInputV2
(
object
):
def
__init__
(
self
):
self
.
bos_id
=
None
self
.
eos_id
=
None
def
before_real_step
(
self
):
raise
NotImplementedError
()
def
after_real_step
(
self
,
*
args
):
raise
NotImplementedError
()
class
GeneratedInputV2
(
BaseGeneratedInputV2
):
def
__init__
(
self
,
size
,
embedding_name
,
embedding_size
):
super
(
GeneratedInputV2
,
self
).
__init__
()
self
.
size
=
size
self
.
embedding_name
=
embedding_name
self
.
embedding_size
=
embedding_size
def
after_real_step
(
self
,
input
):
return
max_id
(
input
=
input
,
name
=
'__beam_search_predict__'
)
def
before_real_step
(
self
):
predict_id
=
memory
(
name
=
'__beam_search_predict__'
,
size
=
self
.
size
,
boot_with_const_id
=
self
.
bos_id
)
trg_emb
=
embedding
(
input
=
predict_id
,
size
=
self
.
embedding_size
,
param_attr
=
attr
.
ParamAttr
(
name
=
self
.
embedding_name
))
return
trg_emb
class
RecurrentLayerGroupSetGeneratorV2
(
Layer
):
def
__init__
(
self
,
eos_name
,
max_length
,
beam_size
,
num_results_per_sample
):
self
.
eos_name
=
eos_name
self
.
max_length
=
max_length
self
.
beam_size
=
beam_size
self
.
num_results_per_sample
=
num_results_per_sample
super
(
RecurrentLayerGroupSetGeneratorV2
,
self
).
__init__
(
name
=
eos_name
,
parent_layers
=
{})
def
to_proto_impl
(
self
,
**
kwargs
):
RecurrentLayerGroupSetGenerator
(
Generator
(
eos_layer_name
=
self
.
eos_name
,
max_num_frames
=
self
.
max_length
,
beam_size
=
self
.
beam_size
,
num_results_per_sample
=
self
.
num_results_per_sample
))
return
self
def
context_name
(
self
):
return
self
.
eos_name
+
".fake"
def
use_context_name
(
self
):
return
True
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
MixedLayerV2
.
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
]
size
=
args
.
get
(
'size'
,
None
)
if
size
is
not
None
:
if
callable
(
size
):
real_size
=
size
()
else
:
real_size
=
size
args
[
'size'
]
=
real_size
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
)
mixed
.
__doc__
=
conf_helps
.
mixed_layer
.
__doc__
class
RecurrentLayerInput
(
Layer
):
def
__init__
(
self
,
recurrent_name
,
index
,
parent_layers
,
reverse
):
parents_len
=
len
(
parent_layers
)
assert
parents_len
<=
1
if
parents_len
==
0
:
self
.
__parents__
=
[]
else
:
self
.
__parents__
=
parent_layers
.
values
()[
0
]
self
.
__recurrent_name__
=
recurrent_name
self
.
__reverse__
=
reverse
name
=
self
.
__parents__
[
index
].
name
if
index
>=
0
else
self
.
context_name
()
super
(
RecurrentLayerInput
,
self
).
__init__
(
name
=
name
,
parent_layers
=
parent_layers
)
def
context_name
(
self
):
return
self
.
__recurrent_name__
+
".begin"
def
to_proto_impl
(
self
,
**
kwargs
):
model_type
(
'recurrent_nn'
)
RecurrentLayerGroupWithoutOutLinksBegin
(
name
=
self
.
__recurrent_name__
,
in_links
=
map
(
lambda
x
:
x
.
name
,
self
.
__parents__
),
seq_reversed
=
self
.
__reverse__
)
return
self
class
RecurrentLayerOutput
(
Layer
):
def
__init__
(
self
,
recurrent_name
,
index
,
parent_layers
):
assert
len
(
parent_layers
)
==
1
self
.
__parents__
=
parent_layers
.
values
()[
0
]
super
(
RecurrentLayerOutput
,
self
).
__init__
(
name
=
self
.
__parents__
[
index
].
name
,
parent_layers
=
parent_layers
)
self
.
__recurrent_name__
=
recurrent_name
def
context_name
(
self
):
return
self
.
__recurrent_name__
+
".end"
def
to_proto_impl
(
self
,
**
kwargs
):
for
l
in
self
.
__parents__
:
RecurrentLayerGroupSetOutLink
(
l
.
name
)
RecurrentLayerGroupEnd
(
name
=
self
.
__recurrent_name__
)
LayerV2
=
Layer
data
=
DataLayerV2
data
.
__name__
=
'data'
AggregateLevel
=
conf_helps
.
AggregateLevel
ExpandLevel
=
conf_helps
.
ExpandLevel
memory
=
MemoryV2
memory
.
__name__
=
'memory'
memory
.
__doc__
=
conf_helps
.
memory
.
__doc__
def
__layer_name_mapping__
(
inname
):
if
inname
in
[
'data_layer'
,
'memory'
,
'mixed_layer'
,
'recurrent_group'
]:
# Do Not handle these layers
return
elif
inname
==
'maxid_layer'
:
return
'max_id'
return
'max_id'
elif
inname
.
endswith
(
'memory'
)
or
inname
.
endswith
(
elif
inname
.
endswith
(
'memory'
)
or
inname
.
endswith
(
'_seq'
)
or
inname
.
endswith
(
'_sim'
)
or
inname
==
'hsigmoid'
:
'_seq'
)
or
inname
.
endswith
(
'_sim'
)
or
inname
==
'hsigmoid'
:
...
@@ -431,187 +68,212 @@ def __layer_name_mapping__(inname):
...
@@ -431,187 +68,212 @@ def __layer_name_mapping__(inname):
return
inname
return
inname
elif
inname
.
endswith
(
"_layer"
):
elif
inname
.
endswith
(
"_layer"
):
return
inname
[:
-
len
(
"_layer"
)]
return
inname
[:
-
len
(
"_layer"
)]
else
:
return
inname
def
__layer_name_mapping_parent_names__
(
inname
):
for
name
in
v1_layers
.
__all__
:
all_args
=
getattr
(
conf_helps
,
inname
).
argspec
.
args
obj
=
getattr
(
v1_layers
,
name
)
return
filter
(
if
not
__need_to_keep__
(
name
):
lambda
x
:
x
in
[
'input1'
,
'input2'
,
'label'
,
'input'
,
'a'
,
'b'
,
continue
'expand_as'
,
new_name
=
__convert_name__
(
name
)
'weights'
,
'vectors'
,
'weight'
,
'score'
,
'left'
,
if
callable
(
obj
)
and
__need_to_wrap__
(
name
):
'right'
,
'output_mem'
],
globals
()[
new_name
]
=
__convert_to_v2__
(
obj
,
new_name
,
__name__
)
all_args
)
def
__convert_layer__
(
_new_name_
,
_old_name_
,
_parent_names_
):
global
__all__
__all__
.
append
(
_new_name_
)
globals
()[
new_name
]
=
__convert_to_v2__
(
_old_name_
,
_parent_names_
)
globals
()[
new_name
].
__name__
=
new_name
for
each_layer_name
in
dir
(
conf_helps
):
new_name
=
__layer_name_mapping__
(
each_layer_name
)
if
new_name
is
not
None
:
parent_names
=
__layer_name_mapping_parent_names__
(
each_layer_name
)
assert
len
(
parent_names
)
!=
0
,
each_layer_name
__convert_layer__
(
new_name
,
each_layer_name
,
parent_names
)
del
parent_names
del
new_name
del
each_layer_name
@
wrap_name_default
()
def
recurrent_group
(
step
,
input
,
reverse
=
False
,
name
=
None
):
if
not
isinstance
(
input
,
collections
.
Sequence
):
input
=
[
input
]
non_static_inputs
=
filter
(
lambda
x
:
not
isinstance
(
x
,
StaticInputV2
),
input
)
actual_input
=
[
RecurrentLayerInput
(
recurrent_name
=
name
,
index
=
i
,
parent_layers
=
{
'recurrent_inputs'
:
non_static_inputs
},
reverse
=
reverse
)
for
i
in
xrange
(
len
(
non_static_inputs
))
]
extra_input
=
None
if
len
(
non_static_inputs
)
==
0
:
extra_input
=
RecurrentLayerInput
(
recurrent_name
=
name
,
index
=-
1
,
parent_layers
=
{},
reverse
=
reverse
)
def
__real_step__
(
*
args
):
rnn_input
=
list
(
args
)
static_inputs
=
filter
(
lambda
x
:
isinstance
(
x
,
StaticInputV2
),
input
)
for
static_input
in
static_inputs
:
mem_name
=
"__%s_memory__"
%
static_input
.
input
.
name
mem
=
memory
(
name
=
mem_name
,
extra_input
=
extra_input
,
is_seq
=
static_input
.
is_seq
,
size
=
static_input
.
input
.
calculate_size
,
boot_layer
=
static_input
.
input
)
with
mixed
(
name
=
mem_name
,
size
=
static_input
.
input
.
calculate_size
,
act
=
activation
.
Identity
())
as
mix
:
mix
+=
identity_projection
(
input
=
mem
)
rnn_input
.
insert
(
input
.
index
(
static_input
),
mix
)
return
step
(
*
rnn_input
)
actual_output
=
__real_step__
(
*
actual_input
)
if
not
isinstance
(
actual_output
,
collections
.
Sequence
):
actual_output
=
[
actual_output
]
retv
=
[
RecurrentLayerOutput
(
recurrent_name
=
name
,
index
=
i
,
parent_layers
=
{
'recurrent_outputs'
:
actual_output
})
for
i
in
xrange
(
len
(
actual_output
))
]
if
len
(
retv
)
==
1
:
return
retv
[
0
]
else
:
else
:
return
retv
globals
()[
new_name
]
=
obj
__all__
.
append
(
new_name
)
recurrent_group
.
__doc__
=
conf_helps
.
recurrent_group
.
__doc__
def
__data_layer__
(
name
,
type
,
**
kwargs
):
l
=
v1_layers
.
data_layer
(
name
,
type
.
dim
,
**
kwargs
)
@
wrap_name_default
()
l
.
data_type
=
type
def
beam_search
(
step
,
return
l
input
,
bos_id
,
eos_id
,
def
__map_data_docstr__
(
doc
):
beam_size
,
doc
=
re
.
sub
(
r
'(data = [^\)]+)\).*'
,
max_length
=
500
,
"data = paddle.layer.data(name=
\"
input
\"
, "
name
=
None
,
"type=paddle.data_type.dense_vector(1000))"
,
doc
)
num_results_per_sample
=
None
):
if
num_results_per_sample
is
None
:
doc
=
re
.
sub
(
r
':param size:.*'
,
':param type: Data type of this data layer'
,
num_results_per_sample
=
beam_size
doc
)
assert
num_results_per_sample
<=
beam_size
doc
=
re
.
sub
(
r
':type size:.*'
,
":type size: paddle.v2.data_type.InputType"
,
# logger.warning("num_results_per_sample should be less than beam_size")
doc
)
return
doc
if
isinstance
(
input
,
StaticInputV2
)
or
isinstance
(
input
,
BaseGeneratedInputV2
):
input
=
[
input
]
__data_layer__
.
__doc__
=
__map_data_docstr__
(
v1_layers
.
data_layer
.
__doc__
)
generated_input_index
=
-
1
real_input
=
[]
for
i
,
each_input
in
enumerate
(
input
):
assert
isinstance
(
each_input
,
StaticInputV2
)
or
isinstance
(
each_input
,
BaseGeneratedInputV2
)
if
isinstance
(
each_input
,
BaseGeneratedInputV2
):
assert
generated_input_index
==
-
1
generated_input_index
=
i
else
:
real_input
.
append
(
each_input
)
assert
generated_input_index
!=
-
1
data
=
__convert_to_v2__
(
__data_layer__
,
'name'
,
__name__
)
gipt
=
input
[
generated_input_index
]
assert
isinstance
(
gipt
,
BaseGeneratedInputV2
)
gipt
.
bos_id
=
bos_id
def
__get_used_layers__
(
output_layers
,
extra_layers
=
None
):
gipt
.
eos_id
=
eos_id
layer_names
=
set
()
parents
=
{}
def
__real_step__
(
*
args
):
def
add_parent
(
child
,
parent
):
eos_name
=
"__%s_eos_layer__"
%
name
if
child
in
parents
:
generator
=
RecurrentLayerGroupSetGeneratorV2
(
parents
[
child
].
append
(
parent
)
eos_name
,
max_length
,
beam_size
,
num_results_per_sample
)
else
:
parents
[
child
]
=
[
parent
]
def
add_additional_parents
():
for
sub_model
in
cp
.
g_config
.
model_config
.
sub_models
:
if
sub_model
.
name
==
'root'
:
continue
for
link
in
sub_model
.
in_links
:
add_parent
(
link
.
link_name
,
link
.
layer_name
)
add_parent
(
sub_model
.
name
,
link
.
layer_name
)
for
link
in
sub_model
.
out_links
:
add_parent
(
link
.
link_name
,
link
.
layer_name
)
add_parent
(
link
.
link_name
,
sub_model
.
name
)
for
mem
in
sub_model
.
memories
:
if
mem
.
boot_layer_name
:
add_parent
(
mem
.
layer_name
,
mem
.
boot_layer_name
)
add_parent
(
mem
.
link_name
,
mem
.
layer_name
)
def
dfs_travel
(
layer_name
):
if
layer_name
in
layer_names
:
return
layer_names
.
add
(
layer_name
)
layer
=
cp
.
g_layer_map
[
layer_name
]
for
inp
in
layer
.
inputs
:
dfs_travel
(
inp
.
input_layer_name
)
if
layer
.
name
in
parents
:
for
p
in
parents
[
layer
.
name
]:
dfs_travel
(
p
)
add_additional_parents
()
for
layer
in
output_layers
:
dfs_travel
(
layer
.
full_name
)
return
layer_names
def
__get_used_parameters__
(
layer_names
):
parameter_names
=
set
()
for
name
in
layer_names
:
l
=
cp
.
g_layer_map
[
name
]
for
inp
in
l
.
inputs
:
if
inp
.
input_parameter_name
:
parameter_names
.
add
(
inp
.
input_parameter_name
)
if
l
.
bias_parameter_name
:
parameter_names
.
add
(
l
.
bias_parameter_name
)
return
parameter_names
def
__get_used_submodels__
(
layer_names
):
submodel_names
=
set
()
for
submodel
in
cp
.
g_config
.
model_config
.
sub_models
:
if
submodel
.
name
in
layer_names
:
submodel_names
.
add
(
submodel
.
name
)
return
submodel_names
def
__get_used_evaluators__
(
layer_names
):
evaluator_names
=
set
()
for
e
in
cp
.
g_config
.
model_config
.
evaluators
:
used
=
True
for
name
in
e
.
input_layers
:
if
name
not
in
layer_names
:
used
=
False
break
if
used
:
evaluator_names
.
add
(
e
.
name
)
return
evaluator_names
def
__trim_submodel__
(
old_submodel
,
layer_names
,
input_layer_names
,
output_layer_names
,
evaluator_names
):
submodel
=
SubModelConfig
()
submodel
.
name
=
old_submodel
.
name
submodel
.
layer_names
.
extend
(
filter
(
lambda
x
:
x
in
layer_names
,
old_submodel
.
layer_names
))
submodel
.
input_layer_names
.
extend
(
filter
(
lambda
x
:
x
in
input_layer_names
,
submodel
.
layer_names
))
submodel
.
output_layer_names
.
extend
(
filter
(
lambda
x
:
x
in
output_layer_names
,
submodel
.
layer_names
))
submodel
.
evaluator_names
.
extend
(
filter
(
lambda
x
:
x
in
evaluator_names
,
old_submodel
.
evaluator_names
))
submodel
.
is_recurrent_layer_group
=
old_submodel
.
is_recurrent_layer_group
submodel
.
reversed
=
old_submodel
.
reversed
submodel
.
memories
.
extend
(
filter
(
lambda
x
:
x
.
link_name
in
layer_names
,
old_submodel
.
memories
))
target_inlinkid
=
(
old_submodel
.
target_inlinkid
if
old_submodel
.
HasField
(
'target_inlinkid'
)
else
-
1
)
in_links
=
[]
for
i
,
link
in
enumerate
(
old_submodel
.
in_links
):
if
link
.
link_name
in
layer_names
or
i
==
target_inlinkid
:
in_links
.
append
(
link
)
if
i
==
target_inlinkid
:
target_inlinkid
=
len
(
in_links
)
-
1
submodel
.
in_links
.
extend
(
in_links
)
submodel
.
out_links
.
extend
(
filter
(
lambda
x
:
x
.
link_name
in
layer_names
,
old_submodel
.
out_links
))
if
old_submodel
.
HasField
(
'generator'
):
submodel
.
generator
.
CopyFrom
(
old_submodel
.
generator
)
if
old_submodel
.
HasField
(
'target_inlinkid'
):
submodel
.
target_inlinkid
=
target_inlinkid
return
submodel
args
=
list
(
args
)
before_step_layer
=
gipt
.
before_real_step
()
before_step_layer
.
append_child
(
layer
=
generator
,
parent_names
=
[
before_step_layer
.
name
])
args
.
insert
(
generated_input_index
,
before_step_layer
)
predict
=
gipt
.
after_real_step
(
step
(
*
args
))
def
parse_network
(
output_layers
,
extra_layers
=
None
):
if
not
isinstance
(
output_layers
,
collections
.
Sequence
):
output_layers
=
[
output_layers
]
if
extra_layers
is
not
None
and
not
isinstance
(
extra_layers
,
collections
.
Sequence
):
extra_layers
=
[
extra_layers
]
else
:
extra_layers
=
[]
eos_layer
=
eos
(
input
=
predict
,
eos_id
=
eos_id
,
name
=
eos_name
)
layer_names
=
__get_used_layers__
(
output_layers
+
extra_layers
)
predict
.
append_child
(
layer
=
eos_layer
,
parent_names
=
[
predict
.
name
])
submodel_names
=
__get_used_submodels__
(
layer_names
)
submodel_names
.
add
(
'root'
)
parameter_names
=
__get_used_parameters__
(
layer_names
)
evaluator_names
=
__get_used_evaluators__
(
layer_names
)
input_layer_names
=
set
()
output_layer_names
=
set
()
return
predict
model_config
=
ModelConfig
()
model_config
.
type
=
cp
.
g_config
.
model_config
.
type
for
l
in
cp
.
g_config
.
model_config
.
layers
:
if
l
.
name
not
in
layer_names
:
continue
model_config
.
layers
.
extend
([
l
])
if
l
.
type
==
'data'
:
model_config
.
input_layer_names
.
append
(
l
.
name
)
input_layer_names
.
add
(
l
.
name
)
# tmp = paddle.layer.recurrent_group(
for
p
in
cp
.
g_config
.
model_config
.
parameters
:
# step=__real_step__,
if
p
.
name
in
parameter_names
:
# input=real_input,
model_config
.
parameters
.
extend
([
p
])
# reverse=False,
# name=name,
# is_generating=True)
tmp
=
recurrent_group
(
step
=
__real_step__
,
input
=
real_input
,
name
=
name
)
return
tmp
for
layer
in
output_layers
:
model_config
.
output_layer_names
.
append
(
layer
.
full_name
)
output_layer_names
.
add
(
layer
.
full_name
)
for
e
in
cp
.
g_config
.
model_config
.
evaluators
:
if
e
.
name
in
evaluator_names
:
model_config
.
evaluators
.
extend
([
e
])
beam_search
.
__doc__
=
conf_helps
.
beam_search
.
__doc__
for
s
in
cp
.
g_config
.
model_config
.
sub_models
:
if
s
.
name
in
submodel_names
:
s
=
__trim_submodel__
(
s
,
layer_names
,
input_layer_names
,
output_layer_names
,
evaluator_names
)
model_config
.
sub_models
.
extend
([
s
])
__projection_names__
=
filter
(
lambda
x
:
x
.
endswith
(
'_projection'
),
return
model_config
dir
(
conf_helps
))
__all__
+=
__projection_names__
__operator_names__
=
filter
(
lambda
x
:
x
.
endswith
(
'_operator'
),
dir
(
conf_helps
))
def
get_layer
(
name
):
__all__
+=
__operator_names__
return
config_base
.
__layer_map__
.
get
(
name
)
# convert projection
for
prj
in
__projection_names__
:
globals
()[
prj
]
=
__convert_to_v2__
(
prj
,
parent_names
=
[
'input'
],
is_default_name
=
False
)
globals
()[
prj
].
__name__
=
prj
# convert operator
cp
.
begin_parse
()
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
)
globals
()[
op
[
0
]].
__name__
=
op
[
0
]
This diff is collapsed.
Click to expand it.
python/paddle/v2/networks.py
浏览文件 @
0ef86cbd
...
@@ -24,20 +24,7 @@ def __initialize__():
...
@@ -24,20 +24,7 @@ def __initialize__():
if
each_subnetwork
in
[
'inputs'
,
'outputs'
]:
if
each_subnetwork
in
[
'inputs'
,
'outputs'
]:
continue
continue
func
=
getattr
(
conf_nw
,
each_subnetwork
)
func
=
getattr
(
conf_nw
,
each_subnetwork
)
if
hasattr
(
func
,
'argspec'
):
globals
()[
each_subnetwork
]
=
func
argspec
=
func
.
argspec
else
:
argspec
=
inspect
.
getargspec
(
func
)
if
each_subnetwork
==
'simple_attention'
:
parents
=
[
'encoded_sequence'
,
'encoded_proj'
,
'decoder_state'
]
else
:
parents
=
filter
(
lambda
x
:
x
.
startswith
(
'input'
),
argspec
.
args
)
assert
len
(
parents
)
!=
0
,
each_subnetwork
v2_subnet
=
__convert_to_v2__
(
each_subnetwork
,
parent_names
=
parents
,
is_default_name
=
'name'
in
argspec
.
args
)
globals
()[
each_subnetwork
]
=
v2_subnet
globals
()[
each_subnetwork
].
__name__
=
each_subnetwork
globals
()[
each_subnetwork
].
__name__
=
each_subnetwork
global
__all__
global
__all__
__all__
.
append
(
each_subnetwork
)
__all__
.
append
(
each_subnetwork
)
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/tests/test_layer.py
浏览文件 @
0ef86cbd
...
@@ -173,9 +173,9 @@ class OtherLayerTest(unittest.TestCase):
...
@@ -173,9 +173,9 @@ class OtherLayerTest(unittest.TestCase):
class
ProjOpTest
(
unittest
.
TestCase
):
class
ProjOpTest
(
unittest
.
TestCase
):
def
test_projection
(
self
):
def
test_projection
(
self
):
input
=
layer
.
data
(
name
=
'data'
,
type
=
data_type
.
dense_vector
(
784
))
input
=
layer
.
data
(
name
=
'data
2
'
,
type
=
data_type
.
dense_vector
(
784
))
word
=
layer
.
data
(
word
=
layer
.
data
(
name
=
'word'
,
type
=
data_type
.
integer_value_sequence
(
10000
))
name
=
'word
2
'
,
type
=
data_type
.
integer_value_sequence
(
10000
))
fc0
=
layer
.
fc
(
input
=
input
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc0
=
layer
.
fc
(
input
=
input
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc1
=
layer
.
fc
(
input
=
input
,
size
=
200
,
act
=
activation
.
Sigmoid
())
fc1
=
layer
.
fc
(
input
=
input
,
size
=
200
,
act
=
activation
.
Sigmoid
())
mixed0
=
layer
.
mixed
(
mixed0
=
layer
.
mixed
(
...
@@ -204,8 +204,8 @@ class ProjOpTest(unittest.TestCase):
...
@@ -204,8 +204,8 @@ class ProjOpTest(unittest.TestCase):
dotmul1
+=
dotmul
dotmul1
+=
dotmul
context
=
layer
.
context_projection
(
input
=
fc0
,
context_len
=
5
)
context
=
layer
.
context_projection
(
input
=
fc0
,
context_len
=
5
)
context0
=
layer
.
mixed
(
size
=
1
00
,
input
=
context
)
context0
=
layer
.
mixed
(
size
=
5
00
,
input
=
context
)
with
layer
.
mixed
(
size
=
1
00
)
as
context1
:
with
layer
.
mixed
(
size
=
5
00
)
as
context1
:
context1
+=
context
context1
+=
context
conv
=
layer
.
conv_projection
(
conv
=
layer
.
conv_projection
(
...
@@ -231,8 +231,8 @@ class ProjOpTest(unittest.TestCase):
...
@@ -231,8 +231,8 @@ class ProjOpTest(unittest.TestCase):
print
layer
.
parse_network
(
conv1
)
print
layer
.
parse_network
(
conv1
)
def
test_operator
(
self
):
def
test_operator
(
self
):
ipt0
=
layer
.
data
(
name
=
'data'
,
type
=
data_type
.
dense_vector
(
784
))
ipt0
=
layer
.
data
(
name
=
'data
1
'
,
type
=
data_type
.
dense_vector
(
784
))
ipt1
=
layer
.
data
(
name
=
'word'
,
type
=
data_type
.
dense_vector
(
128
))
ipt1
=
layer
.
data
(
name
=
'word
1
'
,
type
=
data_type
.
dense_vector
(
128
))
fc0
=
layer
.
fc
(
input
=
ipt0
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc0
=
layer
.
fc
(
input
=
ipt0
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc1
=
layer
.
fc
(
input
=
ipt0
,
size
=
100
,
act
=
activation
.
Sigmoid
())
fc1
=
layer
.
fc
(
input
=
ipt0
,
size
=
100
,
act
=
activation
.
Sigmoid
())
...
@@ -261,7 +261,7 @@ class ProjOpTest(unittest.TestCase):
...
@@ -261,7 +261,7 @@ class ProjOpTest(unittest.TestCase):
class
NetworkTests
(
unittest
.
TestCase
):
class
NetworkTests
(
unittest
.
TestCase
):
def
test_vgg
(
self
):
def
test_vgg
(
self
):
img
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
img
=
layer
.
data
(
name
=
'pixel
1
'
,
type
=
data_type
.
dense_vector
(
784
))
vgg_out
=
networks
.
small_vgg
(
vgg_out
=
networks
.
small_vgg
(
input_image
=
img
,
num_channels
=
1
,
num_classes
=
2
)
input_image
=
img
,
num_channels
=
1
,
num_classes
=
2
)
print
layer
.
parse_network
(
vgg_out
)
print
layer
.
parse_network
(
vgg_out
)
...
@@ -269,12 +269,12 @@ class NetworkTests(unittest.TestCase):
...
@@ -269,12 +269,12 @@ class NetworkTests(unittest.TestCase):
class
EvaluatorTest
(
unittest
.
TestCase
):
class
EvaluatorTest
(
unittest
.
TestCase
):
def
test_evaluator
(
self
):
def
test_evaluator
(
self
):
img
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
img
=
layer
.
data
(
name
=
'pixel
2
'
,
type
=
data_type
.
dense_vector
(
784
))
output
=
layer
.
fc
(
input
=
img
,
output
=
layer
.
fc
(
input
=
img
,
size
=
10
,
size
=
10
,
act
=
activation
.
Softmax
(),
act
=
activation
.
Softmax
(),
name
=
'fc_here'
)
name
=
'fc_here'
)
lbl
=
layer
.
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
lbl
=
layer
.
data
(
name
=
'label
2
'
,
type
=
data_type
.
integer_value
(
10
))
cost
=
layer
.
cross_entropy_cost
(
input
=
output
,
label
=
lbl
)
cost
=
layer
.
cross_entropy_cost
(
input
=
output
,
label
=
lbl
)
evaluator
.
classification_error
(
input
=
output
,
label
=
lbl
)
evaluator
.
classification_error
(
input
=
output
,
label
=
lbl
)
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/tests/test_rnn_layer.py
浏览文件 @
0ef86cbd
...
@@ -20,6 +20,8 @@ import paddle.v2.data_type as data_type
...
@@ -20,6 +20,8 @@ import paddle.v2.data_type as data_type
import
paddle.v2.layer
as
layer
import
paddle.v2.layer
as
layer
from
paddle.trainer_config_helpers.config_parser_utils
import
\
from
paddle.trainer_config_helpers.config_parser_utils
import
\
parse_network_config
as
parse_network
parse_network_config
as
parse_network
from
paddle.trainer_config_helpers.config_parser_utils
import
\
reset_parser
class
RNNTest
(
unittest
.
TestCase
):
class
RNNTest
(
unittest
.
TestCase
):
...
@@ -29,6 +31,8 @@ class RNNTest(unittest.TestCase):
...
@@ -29,6 +31,8 @@ class RNNTest(unittest.TestCase):
hidden_dim
=
8
hidden_dim
=
8
def
parse_old_rnn
():
def
parse_old_rnn
():
reset_parser
()
def
step
(
y
):
def
step
(
y
):
mem
=
conf_helps
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
mem
=
conf_helps
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
out
=
conf_helps
.
fc_layer
(
out
=
conf_helps
.
fc_layer
(
...
@@ -48,6 +52,8 @@ class RNNTest(unittest.TestCase):
...
@@ -48,6 +52,8 @@ class RNNTest(unittest.TestCase):
return
str
(
parse_network
(
test
))
return
str
(
parse_network
(
test
))
def
parse_new_rnn
():
def
parse_new_rnn
():
reset_parser
()
def
new_step
(
y
):
def
new_step
(
y
):
mem
=
layer
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
mem
=
layer
.
memory
(
name
=
"rnn_state"
,
size
=
hidden_dim
)
out
=
layer
.
fc
(
input
=
[
y
,
mem
],
out
=
layer
.
fc
(
input
=
[
y
,
mem
],
...
@@ -75,6 +81,8 @@ class RNNTest(unittest.TestCase):
...
@@ -75,6 +81,8 @@ class RNNTest(unittest.TestCase):
label_dim
=
3
label_dim
=
3
def
parse_old_rnn
():
def
parse_old_rnn
():
reset_parser
()
def
test
():
def
test
():
data
=
conf_helps
.
data_layer
(
name
=
"word"
,
size
=
dict_dim
)
data
=
conf_helps
.
data_layer
(
name
=
"word"
,
size
=
dict_dim
)
label
=
conf_helps
.
data_layer
(
name
=
"label"
,
size
=
label_dim
)
label
=
conf_helps
.
data_layer
(
name
=
"label"
,
size
=
label_dim
)
...
@@ -114,6 +122,7 @@ class RNNTest(unittest.TestCase):
...
@@ -114,6 +122,7 @@ class RNNTest(unittest.TestCase):
return
str
(
parse_network
(
test
))
return
str
(
parse_network
(
test
))
def
parse_new_rnn
():
def
parse_new_rnn
():
reset_parser
()
data
=
layer
.
data
(
data
=
layer
.
data
(
name
=
"word"
,
type
=
data_type
.
dense_vector
(
dict_dim
))
name
=
"word"
,
type
=
data_type
.
dense_vector
(
dict_dim
))
label
=
layer
.
data
(
label
=
layer
.
data
(
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/tests/test_topology.py
浏览文件 @
0ef86cbd
...
@@ -46,8 +46,8 @@ class TestTopology(unittest.TestCase):
...
@@ -46,8 +46,8 @@ class TestTopology(unittest.TestCase):
self
.
assertEqual
(
label_data_type
[
1
].
dim
,
10
)
self
.
assertEqual
(
label_data_type
[
1
].
dim
,
10
)
def
test_get_layer
(
self
):
def
test_get_layer
(
self
):
pixel
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
pixel
=
layer
.
data
(
name
=
'pixel
2
'
,
type
=
data_type
.
dense_vector
(
784
))
label
=
layer
.
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
label
=
layer
.
data
(
name
=
'label
2
'
,
type
=
data_type
.
integer_value
(
10
))
hidden
=
layer
.
fc
(
input
=
pixel
,
hidden
=
layer
.
fc
(
input
=
pixel
,
size
=
100
,
size
=
100
,
act
=
conf_helps
.
SigmoidActivation
())
act
=
conf_helps
.
SigmoidActivation
())
...
@@ -56,14 +56,14 @@ class TestTopology(unittest.TestCase):
...
@@ -56,14 +56,14 @@ class TestTopology(unittest.TestCase):
act
=
conf_helps
.
SoftmaxActivation
())
act
=
conf_helps
.
SoftmaxActivation
())
cost
=
layer
.
classification_cost
(
input
=
inference
,
label
=
label
)
cost
=
layer
.
classification_cost
(
input
=
inference
,
label
=
label
)
topo
=
topology
.
Topology
(
cost
)
topo
=
topology
.
Topology
(
cost
)
pixel_layer
=
topo
.
get_layer
(
"pixel"
)
pixel_layer
=
topo
.
get_layer
(
"pixel
2
"
)
label_layer
=
topo
.
get_layer
(
"label"
)
label_layer
=
topo
.
get_layer
(
"label
2
"
)
self
.
assertEqual
(
pixel_layer
,
pixel
)
self
.
assertEqual
(
pixel_layer
,
pixel
)
self
.
assertEqual
(
label_layer
,
label
)
self
.
assertEqual
(
label_layer
,
label
)
def
test_parse
(
self
):
def
test_parse
(
self
):
pixel
=
layer
.
data
(
name
=
'pixel'
,
type
=
data_type
.
dense_vector
(
784
))
pixel
=
layer
.
data
(
name
=
'pixel
3
'
,
type
=
data_type
.
dense_vector
(
784
))
label
=
layer
.
data
(
name
=
'label'
,
type
=
data_type
.
integer_value
(
10
))
label
=
layer
.
data
(
name
=
'label
3
'
,
type
=
data_type
.
integer_value
(
10
))
hidden
=
layer
.
fc
(
input
=
pixel
,
hidden
=
layer
.
fc
(
input
=
pixel
,
size
=
100
,
size
=
100
,
act
=
conf_helps
.
SigmoidActivation
())
act
=
conf_helps
.
SigmoidActivation
())
...
...
This diff is collapsed.
Click to expand it.
python/paddle/v2/topology.py
浏览文件 @
0ef86cbd
...
@@ -15,36 +15,13 @@
...
@@ -15,36 +15,13 @@
import
collections
import
collections
from
paddle.proto.ModelConfig_pb2
import
ModelConfig
from
paddle.proto.ModelConfig_pb2
import
ModelConfig
import
paddle.trainer_config_helpers
as
conf_helps
import
layer
as
v2_layer
import
layer
as
v2_layer
import
config_base
__all__
=
[
'Topology'
]
__all__
=
[
'Topology'
]
def
__flatten__
(
lis
):
"""
Given a list, possibly nested to any level, return it flattened.
"""
new_lis
=
[]
for
item
in
lis
:
if
isinstance
(
item
,
collections
.
Sequence
):
new_lis
.
extend
(
__flatten__
(
item
))
else
:
new_lis
.
append
(
item
)
return
new_lis
def
__bfs_travel__
(
callback
,
*
layers
):
layers
=
__flatten__
(
layers
)
for
each_layer
in
layers
:
__break__
=
callback
(
each_layer
)
if
__break__
:
return
__layers__
=
each_layer
.
__parent_layers__
.
values
()
+
\
each_layer
.
extra_parent
()
__bfs_travel__
(
callback
,
*
__layers__
)
class
Topology
(
object
):
class
Topology
(
object
):
"""
"""
Topology is used to store the information about all layers
Topology is used to store the information about all layers
...
@@ -94,31 +71,18 @@ class Topology(object):
...
@@ -94,31 +71,18 @@ class Topology(object):
:param name:
:param name:
:return:
:return:
"""
"""
result_layer
=
[
None
]
return
v2_layer
.
get_layer
(
name
)
def
__impl__
(
l
):
if
l
.
name
==
name
:
result_layer
[
0
]
=
l
return
True
# break
return
False
__bfs_travel__
(
__impl__
,
*
self
.
layers
)
if
result_layer
[
0
]
is
None
:
raise
ValueError
(
"No such layer %s"
%
name
)
return
result_layer
[
0
]
def
data_layers
(
self
):
def
data_layers
(
self
):
"""
"""
get all data layer
get all data layer
:return:
:return:
"""
"""
data_layers
=
dict
()
data_layers
=
{}
for
layer
in
self
.
proto
().
layers
:
def
__impl__
(
l
):
l
=
v2_layer
.
get_layer
(
layer
.
name
)
if
isinstance
(
l
,
v2_layer
.
DataLayerV2
):
if
l
and
l
.
layer_type
==
conf_helps
.
LayerType
.
DATA
:
data_layers
[
l
.
name
]
=
l
data_layers
[
layer
.
name
]
=
l
__bfs_travel__
(
__impl__
,
*
self
.
layers
)
return
data_layers
return
data_layers
def
data_type
(
self
):
def
data_type
(
self
):
...
@@ -127,7 +91,7 @@ class Topology(object):
...
@@ -127,7 +91,7 @@ class Topology(object):
[('image', dense_vector(768)), ('label', integer_value(10))]
[('image', dense_vector(768)), ('label', integer_value(10))]
"""
"""
data_layers
=
self
.
data_layers
()
data_layers
=
self
.
data_layers
()
return
[(
nm
,
data_layers
[
nm
].
type
)
return
[(
nm
,
data_layers
[
nm
].
data_
type
)
for
nm
in
self
.
proto
().
input_layer_names
]
for
nm
in
self
.
proto
().
input_layer_names
]
def
get_layer_proto
(
self
,
name
):
def
get_layer_proto
(
self
,
name
):
...
@@ -138,5 +102,5 @@ class Topology(object):
...
@@ -138,5 +102,5 @@ class Topology(object):
def
__check_layer_type__
(
layer
):
def
__check_layer_type__
(
layer
):
if
not
isinstance
(
layer
,
v2_layer
.
LayerV2
):
if
not
isinstance
(
layer
,
config_base
.
Layer
):
raise
ValueError
(
'layer should have type paddle.
layer
.Layer'
)
raise
ValueError
(
'layer should have type paddle.
v2.config_base
.Layer'
)
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