# 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.proto.ModelConfig_pb2 import ModelConfig import layer as v2_layer __all__ = ['Topology'] def __flatten__(lis): """ Given a list, possibly nested to any level, return it flattened. """ new_lis = [] for item in lis: if isinstance(item, collections.Sequence): new_lis.extend(__flatten__(item)) else: new_lis.append(item) return new_lis def __bfs_travel__(callback, *layers): layers = __flatten__(layers) for each_layer in layers: __break__ = callback(each_layer) if __break__: return __layers__ = each_layer.__parent_layers__.values() + \ each_layer.extra_parent() __bfs_travel__(callback, *__layers__) class Topology(object): """ Topology is used to store the information about all layers and network configs. """ def __init__(self, layers, extra_layers=None): def __check__(layers): if not isinstance(layers, collections.Sequence): __check_layer_type__(layers) layers = [layers] for layer in layers: __check_layer_type__(layer) return layers layers = __check__(layers) self.layers = layers if extra_layers is not None: extra_layers = __check__(extra_layers) self.__model_config__ = v2_layer.parse_network( layers, extra_layers=extra_layers) if extra_layers is not None: self.layers.extend(extra_layers) assert isinstance(self.__model_config__, ModelConfig) def use_sparse_updater(self): """ check if any parameter require to use sparse_update :return: """ use_sparse = False for parameter in self.__model_config__.parameters: if parameter.sparse_update or parameter.sparse_remote_update: use_sparse = True break return use_sparse def proto(self): return self.__model_config__ def get_layer(self, name): """ get v2.Layer Class instance by layer name :param name: :return: """ result_layer = [None] def __impl__(l): if l.name == name: result_layer[0] = l return True # break return False __bfs_travel__(__impl__, *self.layers) if result_layer[0] is None: raise ValueError("No such layer %s" % name) return result_layer[0] def data_layers(self): """ get all data layer :return: """ data_layers = dict() def __impl__(l): if isinstance(l, v2_layer.DataLayerV2): data_layers[l.name] = l __bfs_travel__(__impl__, *self.layers) return data_layers def data_type(self): """ get data_type from proto, such as: [('image', dense_vector(768)), ('label', integer_value(10))] """ data_layers = self.data_layers() return [(nm, data_layers[nm].type) for nm in self.proto().input_layer_names] def get_layer_proto(self, name): for layer in self.__model_config__.layers: if layer.name == name: return layer return None def __check_layer_type__(layer): if not isinstance(layer, v2_layer.LayerV2): raise ValueError('layer should have type paddle.layer.Layer')