# 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. """ Before this new package paddle.v2.layer, users would need to use functions in paddle.trainer_config_helpers.layers to configure networks. The Old Way: ========= This old way requires that the creation of a network be defined in a Python function, say network_config, and that this Python function being passed to paddle.trainer_config_helpers.parse_network_config for the creation of protobuf message description of this network. ```python def network_config(): img = paddle.trainer_config_helpers.data_layer(name="pixel", size=784) inference = paddle.trainer_config_helpers.fc_layer( input=img, size=10, act=paddle.trainer_config_helpers.SoftmaxActivation()) cost = paddle.trainer_config_helpers.classification_cost( input=inference, label=paddle.trainer_config_helpers.data_layer(name="label", size=10)) proto_desc = parse_network_config(network_config) ``` When parse_network_config executes network_config, those layer definition functions like data_layer and fc_layer would change some Python global variables, so that after the execution, parse_network_config could collect information from these global variables and generates the protobuf message. The New Way: ========= In this PR, we define a function in paddle.v2.layer which creates a Python class for each layer creation function in paddle.trainer_config_helpers.layers. Users can use create a network as follows: ```python img = paddle.v2.layer.data(name="pixel", size=784) inference = paddle.v2.layer.fc(input=img, size=10, act=paddle.v2.layer.Softmax()) cost = paddle.v2.layer.classification( input=inference, label=paddle.v2.layer.data(name="label", size=10)) parameters = paddle.v2.parameters.create(cost) ``` This new way doesn't require those invocations to layer definition functions 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 paddle.trainer_config_helpers as conf_helps import paddle.trainer.PyDataProvider2 as dp 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 import collections __all__ = [ 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', 'cross_entropy_cost' ] def parse_network(*outputs): """ parse all output layers and then generate a model config proto. :param outputs: :return: """ def __real_func__(): context = dict() real_output = [each.to_proto(context=context) for each in outputs] conf_helps.outputs(real_output) return __parse__(__real_func__) class Layer(object): def __init__(self, name, parent_layers): assert isinstance(parent_layers, dict) assert isinstance(name, basestring) 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 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, name_prefix, parent_names): if name_prefix is not None: wrapper = wrap_name_default(name_prefix=name_prefix) else: wrapper = None class V2LayerImpl(Layer): def __init__(self, name=None, **kwargs): parent_layers = dict() other_kwargs = dict() for pname in parent_names: parent_layers[pname] = kwargs[pname] for key in kwargs.keys(): if key not in parent_names: other_kwargs[key] = kwargs[key] 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)(name=self.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. """ class DataLayerV2(Layer): def __init__(self, name, data_type, **kwargs): assert isinstance(data_type, dp.InputType) self.__method_name__ = 'data_layer' self.__kwargs__ = kwargs self.__data_size__ = data_type.dim super(DataLayerV2, self).__init__(name=name, parent_layers=dict()) def to_proto_impl(self, **kwargs): args = dict() args['size'] = self.__data_size__ 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) data = DataLayerV2 fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input']) max_id = __convert_to_v2__( 'maxid_layer', name_prefix='maxid_layer', parent_names=['input']) classification_cost = __convert_to_v2__( 'classification_cost', name_prefix='classification_cost', parent_names=['input', 'label']) cross_entropy_cost = __convert_to_v2__( 'cross_entropy', name_prefix='cross_entropy', parent_names=['input', 'label']) if __name__ == '__main__': pixel = data(name='pixel', data_type=dp.dense_vector(784)) label = data(name='label', data_type=dp.integer_value(10)) hidden = fc(input=pixel, size=100, act=conf_helps.SigmoidActivation()) inference = fc(input=hidden, size=10, act=conf_helps.SoftmaxActivation()) maxid = max_id(input=inference) cost1 = classification_cost(input=inference, label=label) cost2 = cross_entropy_cost(input=inference, label=label) print parse_network(cost1) print parse_network(cost2) print parse_network(cost1, cost2) print parse_network(cost2) print parse_network(inference, maxid)