# 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 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.layers import layer_support import data_type import activation import attr __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' ] __projection_names__ = filter(lambda x: x.endswith('_projection'), dir(conf_helps)) __all__ += __projection_names__ __operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps)) __all__ += __operator_names__ 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=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, name_prefix=None, parent_names=None): if name_prefix is not None: wrapper = wrap_name_default(name_prefix=name_prefix) 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. """ 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 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 MixedLayerTypeV2.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] 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) 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']) 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']) # convert projection for prj in __projection_names__: globals()[prj] = __convert_to_v2__(prj, parent_names=['input']) # convert operator 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])