from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import tensorflow as tf

from tensorflow.python.framework import graph_util

# create a session

sess = tf.Session()

src = sys.argv[]

dst = sys.argv[]

# import best model

saver = tf.train.import_meta_graph('model.ckpt.meta') # graph

saver.restore(sess, 'model.ckpt') # variables

# get graph definition

gd = sess.graph.as_graph_def()

# fix batch norm nodes

for node in gd.node:

  if node.op == 'RefSwitch':

    node.op = 'Switch'

    for index in xrange(len(node.input)):

      if 'moving_' in node.input[index]:

        node.input[index] = node.input[index] + '/read'

  elif node.op == 'AssignSub':

    node.op = 'Sub'

    if 'use_locking' in node.attr: del node.attr['use_locking']

# generate protobuf

converted_graph_def = graph_util.convert_variables_to_constants(sess, gd, ["logits_set"])

tf.train.write_graph(converted_graph_def, '/path/to/save/', 'model.pb', as_text=False)

def loadModel(path_name):

    graph = tf.get_default_graph()

    graph_def = graph.as_graph_def()

    graph_def.ParseFromString(tf.gfile.FastGFile(path_name, 'rb').read())

    tf.import_graph_def(graph_def, name='graph')

    return graph_def

import tensorflow as tf

import sys

name = sys.argv[]

path = sys.argv[]

model = name

graph = tf.get_default_graph()

graph_def = graph.as_graph_def()

graph_def.ParseFromString(tf.gfile.FastGFile(model, 'rb').read())

tf.import_graph_def(graph_def, name='graph')

summaryWriter = tf.summary.FileWriter(path, graph)

    sess = K.get_session()

    from tensorflow.python.framework import graph_util,graph_io

    init_graph = sess.graph.as_graph_def()

    main_graph = graph_util.convert_variables_to_constants(sess,init_graph,out_nodes)