converter.py

# This file is useful for reading the contents of the ops generated by ruby.
# You can read any graph defination in pb/pbtxt format generated by ruby
# or by python and then convert it back and forth from human readable to binary format.

import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.platform import gfile

def pbtxt_to_graphdef(filename):
  with open(filename, 'r') as f:
    graph_def = tf.GraphDef()
    file_content = f.read()
    text_format.Merge(file_content, graph_def)
    tf.import_graph_def(graph_def, name='')
    tf.train.write_graph(graph_def, 'pbtxt/', 'protobuf.pb', as_text=False)

def graphdef_to_pbtxt(filename): 
  with gfile.FastGFile(filename,'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    tf.import_graph_def(graph_def, name='')
    tf.train.write_graph(graph_def, 'pbtxt/', 'protobuf.pbtxt', as_text=True)
  return


graphdef_to_pbtxt('graph.pb')  # here you can write the name of the file to be converted
# and then a new file will be made in pbtxt directory.

This function obtained from here will do the trick:

import tensorflow as tf
import sys
from tensorflow.python.platform import gfile
from tensorflow.core.protobuf import saved_model_pb2
from tensorflow.python.util import compat

model_filename ='saved_model.pb'
with gfile.FastGFile(model_filename, 'rb') as f:
     data = compat.as_bytes(f.read())
      sm = saved_model_pb2.SavedModel()
      sm.ParseFromString(data)
      g_in = tf.import_graph_def(sm.meta_graphs[0].graph_def)