tensorflow ckpt模型和pb模型获取节点名称,及ckpt转pb模型实例_Python

tensorflow ckpt模型和pb模型获取节点名称,及ckpt转pb模型实例

2020-04-03 19:36三寸光阴___ Python

今天小编就为大家分享一篇tensorflow ckpt模型和pb模型获取节点名称,及ckpt转pb模型实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

ckpt

				?

									from tensorflow.python import pywrap_tensorflow 

									checkpoint_path = 'model.ckpt-8000'

									reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path) 

									var_to_shape_map = reader.get_variable_to_shape_map() 

									for key in var_to_shape_map: 

									 print("tensor_name: ", key)

				?

									import tensorflow as tf

									import os

									model_name = './mobilenet_v2_140_inf_graph.pb'

									def create_graph():

									 with tf.gfile.FastGFile(model_name, 'rb') as f:

									  graph_def = tf.GraphDef()

									  graph_def.ParseFromString(f.read())

									  tf.import_graph_def(graph_def, name='')

									create_graph()

									tensor_name_list = [tensor.name for tensor in tf.get_default_graph().as_graph_def().node]

									for tensor_name in tensor_name_list:

									 print(tensor_name,'\n')

ckpt转pb

				?

									def freeze_graph(input_checkpoint,output_graph):

									 '''

									 :param input_checkpoint:

									 :param output_graph: PB模型保存路径

									 :return:

									 '''

									 output_node_names = "xxx"

									 saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)

									 graph = tf.get_default_graph()

									 input_graph_def = graph.as_graph_def()

									 with tf.Session() as sess:

									  saver.restore(sess, input_checkpoint)

									  output_graph_def = graph_util.convert_variables_to_constants( 

									   sess=sess,

									   input_graph_def=input_graph_def,# 等于:sess.graph_def

									   output_node_names=output_node_names.split(","))

									  with tf.gfile.GFile(output_graph, "wb") as f:

									   f.write(output_graph_def.SerializeToString()) 

									  print("%d ops in the final graph." % len(output_graph_def.node)) 

									  for op in graph.get_operations():

									   print(op.name, op.values())