# 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. """ `paddle.v2.layer` is a part of model config packages in paddle.v2. In API v2, we want to make Paddle a plain Python package. The model config package defined the way how to configure a neural network topology in Paddle Python code. The primary usage shows below. .. code-block:: python import paddle.v2 as paddle img = paddle.layer.data(name='img', type=paddle.data_type.dense_vector(784)) hidden = paddle.layer.fc(input=img, size=200) prediction = paddle.layer.fc(input=hidden, size=10, act=paddle.activation.Softmax()) # use prediction instance where needed. parameters = paddle.v2.parameters.create(cost) """ 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__ 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 __all__ = ['parse_network', 'data'] __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__) """ 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) LayerV2 = Layer data = DataLayerV2 AggregateLevel = conf_helps.layers.AggregateLevel ExpandLevel = conf_helps.layers.ExpandLevel def __layer_name_mapping__(inname): if inname in ['data_layer', 'memory', 'mixed_layer']: # Do Not handle these layers return elif inname == 'maxid_layer': return 'max_id' elif inname.endswith('memory') or inname.endswith( '_seq') or inname.endswith('_sim') or inname == 'hsigmoid': return inname elif inname in [ 'cross_entropy', 'multi_binary_label_cross_entropy', 'cross_entropy_with_selfnorm' ]: return inname + "_cost" elif inname.endswith('_cost'): return inname elif inname.endswith("_layer"): return inname[:-len("_layer")] def __layer_name_mapping_parent_names__(inname): all_args = getattr(conf_helps, inname).argspec.args return filter( lambda x: x in ['input1', 'input2','label', 'input', 'a', 'b', 'expand_as', 'weights', 'vectors', 'weight', 'score', 'left', 'right'], all_args) def __convert_layer__(_new_name_, _old_name_, _parent_names_): global __all__ __all__.append(_new_name_) globals()[new_name] = __convert_to_v2__(_old_name_, _parent_names_) for each_layer_name in dir(conf_helps): new_name = __layer_name_mapping__(each_layer_name) if new_name is not None: parent_names = __layer_name_mapping_parent_names__(each_layer_name) assert len(parent_names) != 0, each_layer_name __convert_layer__(new_name, each_layer_name, parent_names) del parent_names del new_name del each_layer_name # convert projection for prj in __projection_names__: globals()[prj] = __convert_to_v2__( prj, parent_names=['input'], is_default_name=False) # 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], is_default_name=False)