Listing operations in frozen .pb TensorFlow graphs in GraphDef format (see comments for SavedModel)

dump_operations.py

import argparse
import os
import sys
from typing import Iterable
import tensorflow as tf


parser = argparse.ArgumentParser()
parser.add_argument('file', type=str, help='The file name of the frozen graph.')
args = parser.parse_args()

if not os.path.exists(args.file):
    parser.exit(1, 'The specified file does not exist: {}'.format(args.file))

graph_def = None
graph = None

# Assuming a `.pb` file in `GraphDef` format. 
# See comments on https://gist.github.com/sunsided/88d24bf44068fe0fe5b88f09a1bee92a/
# for inspecting SavedModel graphs instead.
print('Loading graph definition ...', file=sys.stderr)
try:
    with tf.gfile.GFile(args.file, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
except BaseException as e:
    parser.exit(2, 'Error loading the graph definition: {}'.format(str(e)))

print('Importing graph ...', file=sys.stderr)
try:
    assert graph_def is not None
    with tf.Graph().as_default() as graph:  # type: tf.Graph
        tf.import_graph_def(
            graph_def,
            input_map=None,
            return_elements=None,
            name='',
            op_dict=None,
            producer_op_list=None
        )
except BaseException as e:
    parser.exit(2, 'Error importing the graph: {}'.format(str(e)))

print()
print('Operations:')
assert graph is not None
ops = graph.get_operations()  # type: Iterable[tf.Operation]
for op in ops:
    print('- {0:20s} "{1}" ({2} outputs)'.format(op.type, op.name, len(op.outputs)))

print()
print('Sources (operations without inputs):')
for op in ops:
    if len(op.inputs) > 0:
        continue
    print('- {0}'.format(op.name))

print()
print('Operation inputs:')
for op in ops:
    if len(op.inputs) == 0:
        continue
    print('- {0:20}'.format(op.name))
    print('  {0}'.format(', '.join(i.name for i in op.inputs)))

print()
print('Tensors:')
for op in ops:
    for out in op.outputs:
        print('- {0:20} {1:10} "{2}"'.format(str(out.shape), out.dtype.name, out.name))

I got (tf1.x-cpu) PS C:\> python Python 3.7.8 | packaged by conda-forge | (default, Jul 31 2020, 01:53:57) [MSC v.1916 64 bit (AMD64)] on win32 and

>> tf.__version__

'1.14.0' but (tf1.x-cpu) PS C:\> python .\dump_operations.py .\saved_model\saved_model.pb and it ends Loading graph definition ... WARNING:tensorflow:From .\dump_operations.py:20: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead. WARNING:tensorflow:From .\dump_operations.py:21: The name tf.GraphDef is deprecated. Please use tf.compat.v1.GraphDef instead. Error loading the graph definition: Wrong wire type in tag.

…

On Wed, Sep 9, 2020 at 7:03 PM Markus Mayer ***@***.***> wrote: ***@***.**** commented on this gist. ------------------------------ It's been a while, but any Python 3 and some TensorFlow >= 1.8 and < 2 should do. — You are receiving this because you commented. Reply to this email directly, view it on GitHub <https://gist.github.com/88d24bf44068fe0fe5b88f09a1bee92a#gistcomment-3447795>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ALGLVQX7E2IU4VUM4Y6ZH2LSE6YM3ANCNFSM4HOJE6JA> .

I just toyed around with it a bit and here is my best guess at what's happening. 🙂

First of all - to get rid of the warnings, try replacing import tensorflow as tf with import tensorflow.compat.v1 as tf.

I'm assuming the culprit is this: The code above is loading a frozen model containing a GraphDef (defined in graph.proto) - however, these are not compatible with loading graphs stored as a SavedModel (defined in saved_model.proto).

Specifically this block assumes the GraphDef format, tries to decode it as such (this is where it blows up) and then imports the result into a new graph:

import tensorflow.compat.v1 as tf

model_file = "path/to/model/file.pb"

with tf.gfile.GFile(model_file, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

assert graph_def is not None
with tf.Graph().as_default() as graph:  # type: tf.Graph
    tf.import_graph_def(
        graph_def,
        input_map=None,
        return_elements=None,
        name='',
        op_dict=None,
        producer_op_list=None
    )

If you have a SavedModel however, there's a much quicker way to achieve the same result using a saved_model.loader. Assuming the tf.tag_constants.SERVING tag:

import tensorflow.compat.v1 as tf

model_path = "path/to/model"  # <-- sneaky one, expects a `saved_model.pb` file in there

graph = tf.Graph()
with tf.Session(graph=graph) as sess:
    tf.saved_model.loader.load(sess, [tf.tag_constants.SERVING], model_path)

This method is deprecated according to the tf.compat.v1.saved_model.load documentation, but upgrading shouldn't be too hard.

Once you have the graph populated with the mode, the rest of the code works as before.