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.

Thanx for your fast reply. So, let me explain my actual goal. I currently re-train a new custom BERT from scratch (https://github.com/google-research/bert). BERT comes also as a tensorflow Hub module (https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1), which makes it really easy to load this model in both Tensorflow and Keras.

As, you may speculate the module contains a SavedModel which means it provides the trained variables and the saved_model.pb. The saved_model.pb contain the actual graph definition which in case of the google's default BERT has a vocabulary of 30522, which actually means the graph includes such specifications. In order to be able to load my own custom BERT with a different vocab and the same specifications for the rest of the layers, I need to amend saved_model.pb, so I used this piece of code:

with tf.Session(graph=tf.Graph()) as sess:
    graph_def = tf.saved_model.loader.load(sess, ["train"], "bert")
    tf.train.write_graph(graph_def, "t", "saved_model.pbtxt", as_text=True)

Given the saved_model.pbtxt, I can now actually see the specifications of the model in raw text format and amend all the definitions of 30522 to 32000 in my case. Now, I need to do the reverse part and wrap it up again as a .pb file, because this is what Tensorflow Hub reads... I still struggle with this step and that's how I found your code :)

I would appreciate any help in order to find a working solution!

The SavedModel protobuf message is not a GraphDef, hence your error. There definitely is a way to work with protobuf messages directly without having to actually interpret them as valid Tensorflow objects - at least when conversion between binary and text formats. I can't remember right now what module it was, I'll try to have a look and post a further comment if I find it.

Ok, I think I actually found it. Look up the google.protobuf module and/or see if you find a "saved_model_pb2" file you could import (that would be the generated python wrapper for the SavedModel message definition, via which I think it should be possible to load the file and convert it between the text/binary format)

The SavedModel protobuf messag is not a GraphDef, hence your error. There definitely is a way to work with protobuf messages directly wthout having to actually interpret them as valid Tensorflow objects - at least when conversion between binary and text formats. I can't remember right now what module it was, I'll try to have a look and post a further comment if I find it.

Any solution that will lead me to saved_model.pb -> saved_model.pbtxt -> saved_model.pb, or just amending the saved_model.pb anyhow is welcome. Thanks for your help, appreciate it!

Ok, I think I actually found it. Look up the google.protobuf module and/or see if you find a "saved_model_pb2" file you could import (that would be the generated python wrapper for the SavedModel message definition, via which I think it should be possible to load the file and convert it between the text/binary format)

Could you please provide a minimal example on how to check if the google.protobuf module has a "saved_model_pb2" and load this, in order to save it back to .pbtxt. Sorry, but even the terminology google.protobufis vague for me...

I tried to implement the same but I am getting the following error

in graphdef_to_pbtxt(filename)
7 with open(filename,'rb') as f:
8 graph_def = tf.compat.v1.GraphDef()
----> 9 graph_def.ParseFromString(f.read())
10 with open('protobuf.txt', 'w') as fp:
11 fp.write(str(graph_def))

DecodeError: Error parsing message

The code I used is as follows

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

def graphdef_to_pbtxt(filename): 
    with open(filename,'rb') as f:
        graph_def = tf.compat.v1.GraphDef()
        graph_def.ParseFromString(f.read())
    with open('protobuf.txt', 'w') as fp:
        fp.write(str(graph_def))
    
graphdef_to_pbtxt('saved_model.pb')

Can anybody help me on this?

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)