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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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.__contex__ = {}
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.context_name() is None:
return self.to_proto_impl(**kwargs)
elif self.context_name() not in context:
context[self.context_name()] = self.to_proto_impl(**kwargs)
self.__contex__ = context
if 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.__contex__[self.context_name()].size
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
