# Copyright (c) 2019 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. from x2paddle.core.graph import GraphNode, Graph from tensorflow.python.platform import gfile import tensorflow as tf import copy class TFGraphNode(GraphNode): def __init__(self, layer, layer_name=None): super(TFGraphNode, self).__init__(layer, layer_name) self.layer_type = layer.op class TFGraph(Graph): def __init__(self, model): super(TFGraph, self).__init__(model) self.multi_output_ops = [ 'Split', 'Unpack'] def build(self): for layer in self.model.node: self.node_map[layer.name] = TFGraphNode(layer) for layer_name, node in self.node_map.items(): for in_node in node.layer.input: if in_node not in self.node_map: if in_node.strip().split(':')[0] in self.node_map: self.connect(in_node, layer_name) else: raise Exception('input[{}] of node[{}] does not exist in node_map'.format(in_node, layer_name)) else: if self.node_map[in_node].layer_type in self.multi_output_ops: in_node += ":0" self.connect(in_node, layer_name) super(TFGraph, self).build() class TFParser(object): def __init__(self, pb_model, in_nodes=None, out_nodes=None, in_shapes=None): assert in_nodes is not None, "in_nodes should not be None" assert out_nodes is not None, "out_nodes should not be None" assert in_shapes is not None, "in_shapes should not be None" assert len(in_shapes) == len(in_nodes), "length of in_shapes and in_nodes should be equal" sess = tf.Session() with gfile.FastGFile(pb_model, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) sess.graph.as_default() tf.import_graph_def(graph_def, name='') sess.run(tf.global_variables_initializer()) self.tf_graph = TFGraph(sess.graph._as_graph_def(add_shapes=True)[0]) self.tf_graph.build()