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107 lines
3.2 KiB
107 lines
3.2 KiB
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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from paddle.proto.ModelConfig_pb2 import ModelConfig
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import paddle.trainer_config_helpers as conf_helps
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import layer as v2_layer
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import config_base
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__all__ = ['Topology']
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class Topology(object):
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"""
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Topology is used to store the information about all layers
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and network configs.
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"""
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def __init__(self, layers, extra_layers=None):
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def __check__(layers):
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if not isinstance(layers, collections.Sequence):
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layers = [layers]
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for layer in layers:
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__check_layer_type__(layer)
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return layers
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layers = __check__(layers)
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self.layers = layers
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if extra_layers is not None:
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extra_layers = __check__(extra_layers)
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self.__model_config__ = v2_layer.parse_network(
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layers, extra_layers=extra_layers)
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if extra_layers is not None:
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self.layers.extend(extra_layers)
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assert isinstance(self.__model_config__, ModelConfig)
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def use_sparse_updater(self):
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"""
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check if any parameter require to use sparse_update
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:return:
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"""
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use_sparse = False
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for parameter in self.__model_config__.parameters:
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if parameter.sparse_update or parameter.sparse_remote_update:
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use_sparse = True
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break
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return use_sparse
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def proto(self):
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return self.__model_config__
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def get_layer(self, name):
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"""
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get v2.Layer Class instance by layer name
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:param name:
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:return:
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"""
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return v2_layer.get_layer(name)
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def data_layers(self):
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"""
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get all data layer
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:return:
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"""
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data_layers = {}
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for layer in self.proto().layers:
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l = v2_layer.get_layer(layer.name)
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if l and l.layer_type == conf_helps.LayerType.DATA:
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data_layers[layer.name] = l
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return data_layers
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def data_type(self):
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"""
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get data_type from proto, such as:
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[('image', dense_vector(768)), ('label', integer_value(10))]
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"""
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data_layers = self.data_layers()
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return [(nm, data_layers[nm].data_type)
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for nm in self.proto().input_layer_names]
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def get_layer_proto(self, name):
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for layer in self.__model_config__.layers:
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if layer.name == name:
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return layer
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return None
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def __check_layer_type__(layer):
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if not isinstance(layer, config_base.Layer):
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raise ValueError('layer should have type paddle.v2.config_base.Layer')
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