You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
116 lines
3.4 KiB
116 lines
3.4 KiB
# 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 paddle.trainer_config_helpers as conf_helps
|
|
import layer as v2_layer
|
|
import config_base
|
|
import cPickle
|
|
|
|
__all__ = ['Topology']
|
|
|
|
|
|
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):
|
|
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:
|
|
"""
|
|
return v2_layer.get_layer(name)
|
|
|
|
def data_layers(self):
|
|
"""
|
|
get all data layer
|
|
:return:
|
|
"""
|
|
data_layers = {}
|
|
for layer in self.proto().layers:
|
|
l = v2_layer.get_layer(layer.name)
|
|
if l and l.layer_type == conf_helps.LayerType.DATA:
|
|
data_layers[layer.name] = l
|
|
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].data_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 serialize_for_inference(self, stream):
|
|
protobin = self.proto().SerializeToString()
|
|
data_type = self.data_type()
|
|
cPickle.dump({
|
|
'protobin': protobin,
|
|
'data_type': data_type
|
|
}, stream, cPickle.HIGHEST_PROTOCOL)
|
|
|
|
|
|
def __check_layer_type__(layer):
|
|
if not isinstance(layer, config_base.Layer):
|
|
raise ValueError('layer should have type paddle.v2.config_base.Layer')
|