# 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 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 import data_type __all__ = [ 'parse_network', 'data', 'fc', 'max_id', 'classification_cost', 'cross_entropy_cost', 'cross_entropy_with_selfnorm_cost', 'regression_cost', 'multi_binary_label_cross_entropy_cost', 'rank_cost', 'lambda_cost', 'sum_cost', 'huber_cost', 'memory', 'embedding', 'recurrent_group' ] 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 is None: return self.to_proto_impl(**kwargs) # memory may have the same name with some layer if isinstance(self, MemoryV2): return self.to_proto_impl(**kwargs) # store v1 API's layer_output in context with the key of it's name. 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: 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] 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, 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) class MemoryV2(Layer): def __init__(self, name, size, **kwargs): self.name = name self.size = size parent_names = ['boot_layer'] 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] super(MemoryV2, self).__init__(name=name, parent_layers=parent_layers) self.__kwargs__ = other_kwargs 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] return conf_helps.memory(name=self.name, size=self.size, **args) class LayerOutputV2(Layer): """ LayerOutputV2 is used to store the result of LayerOutput in v1 api. It will not store it's parents because layer_output has been parsed already. """ def __init__(self, layer_output): assert isinstance(layer_output, conf_helps.LayerOutput) self.layer_output = layer_output super(LayerOutputV2, self).__init__( name=layer_output.name, parent_layers=dict()) def to_proto_impl(self): return self.layer_output class RecurrentGroupV2(Layer): def __init__(self, name, **kwargs): self.__parent_names__ = ['input'] other_kwargs = dict() parent_layers = dict() for pname in self.__parent_names__: if kwargs.has_key(pname): parent_layers[pname] = kwargs[pname] for key in kwargs.keys(): if key not in self.__parent_names__: other_kwargs[key] = kwargs[key] self.__kwargs__ = other_kwargs super(RecurrentGroupV2, self).__init__( name=name, parent_layers=parent_layers) wrapper = wrap_name_default(name_prefix='recurrent_group') __init__ = wrapper(__init__) def to_proto_impl(self, **kwargs): def in_args_converter(*in_args): if not isinstance(in_args, collections.Sequence): in_args = [in_args] return [LayerOutputV2(input) for input in in_args] args = dict() for each in kwargs: args[each] = kwargs[each] for each in self.__kwargs__: args[each] = self.__kwargs__[each] return conf_helps.recurrent_group( name=self.name, in_args_converter=in_args_converter, **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', parent_names=['input']) classification_cost = __convert_to_v2__( 'classification_cost', name_prefix='classification_cost', parent_names=['input', 'label', 'weight']) regression_cost = __convert_to_v2__( 'regression_cost', name_prefix='regression_cost', parent_names=['input', 'label', 'weight']) cross_entropy_cost = __convert_to_v2__( 'cross_entropy', name_prefix='cross_entropy', parent_names=['input', 'label']) embedding = __convert_to_v2__( 'embedding_layer', name_prefix='embedding', parent_names=['input']) last_seq = __convert_to_v2__( 'last_seq', name_prefix='last_seq', parent_names=['input']) recurrent_group = RecurrentGroupV2 memory = MemoryV2 cross_entropy_with_selfnorm_cost = __convert_to_v2__( 'cross_entropy_with_selfnorm', name_prefix='cross_entropy_with_selfnorm', parent_names=['input', 'label']) multi_binary_label_cross_entropy_cost = __convert_to_v2__( 'multi_binary_label_cross_entropy', name_prefix='multi_binary_label_cross_entropy', parent_names=['input', 'label']) rank_cost = __convert_to_v2__( 'rank_cost', name_prefix='rank_cost', parent_names=['left', 'right', 'label', 'weight']) lambda_cost = __convert_to_v2__( 'lambda_cost', name_prefix='lambda_cost', parent_names=['input', 'score']) sum_cost = __convert_to_v2__( 'sum_cost', name_prefix='sum_cost', parent_names=['input']) huber_cost = __convert_to_v2__( 'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label'])