提交 6dd21658 编写于 作者: Y Yu Yang

Add config_base.py for Layer

上级 91f13e48
import collections
from paddle.trainer_config_helpers.default_decorators import wrap_name_default
import paddle.trainer_config_helpers as conf_helps
class Layer(object):
def __init__(self, name=None, parent_layers=None):
assert isinstance(parent_layers, dict)
self.name = name
self.__parent_layers__ = parent_layers
def to_proto(self, context):
"""
function to set proto attribute
"""
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
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, 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, **kwargs):
parent_layers = dict()
other_kwargs = dict()
for pname in parent_names:
if kwargs.has_key(pname):
parent_layers[pname] = kwargs[pname]
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
......@@ -65,10 +65,7 @@ to be in a Python function but could be anywhere.
Also, the creation of a protobuf message is hidden in the invocation of
paddle.v2.parameters.create, no longer exposed to users.
"""
import collections
import inspect
from config_base import Layer, __convert_to_v2__
import paddle.trainer_config_helpers as conf_helps
from paddle.trainer_config_helpers.config_parser_utils import \
parse_network_config as __parse__
......@@ -107,74 +104,6 @@ def parse_network(*outputs):
return __parse__(__real_func__)
class Layer(object):
def __init__(self, name=None, parent_layers=None):
assert isinstance(parent_layers, dict)
self.name = name
self.__parent_layers__ = parent_layers
def to_proto(self, context):
"""
function to set proto attribute
"""
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
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, 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, **kwargs):
parent_layers = dict()
other_kwargs = dict()
for pname in parent_names:
if kwargs.has_key(pname):
parent_layers[pname] = kwargs[pname]
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
"""
Some layer may need some special config, and can not use __convert_to_v2__ to convert.
So we also need to implement some special LayerV2.
......
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