Given a Tensor Flow Model Graph, How to Find the Input Node and Output Node Names

Given a tensor flow model graph, how to find the input node and output node names

Try this:

run python

>>> import tensorflow as tf
>>> gf = tf.GraphDef()
>>> gf.ParseFromString(open('/your/path/to/graphname.pb','rb').read())

and then

>>> [n.name + '=>' +  n.op for n in gf.node if n.op in ( 'Softmax','Placeholder')]

Then, you can get result similar to this:

['Mul=>Placeholder', 'final_result=>Softmax']

But I'm not sure it's the problem of node names regarding the error messages.
I guess you provided wrong arguements when loading the graph file or your generated graph file is something wrong?

Check this part:

E/AndroidRuntime(16821): java.lang.IllegalArgumentException: Incompatible 
shapes: [1,224,224,3] vs. [32,1,1,2048]

UPDATE:
Sorry,
if you're using (re)trained graph , then try this:

[n.name + '=>' +  n.op for n in gf.node if n.op in ( 'Softmax','Mul')]

It seems that (re)trained graph saves input/output op name as "Mul" and "Softmax", while optimized and/or quantized graph saves them as "Placeholder" and "Softmax".

BTW, using retrained graph in mobile environment is not recommended according to Peter Warden's post: https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/ . It's better to use quantized or memmapped graph due to performance and file size issue, I couldn't find out how to load memmapped graph in android though...:(
(no problem loading optimized / quantized graph in android)

Get input and output node name from .ckpt and .meta files tensorflow

The .meta file contains information about the different node in the tensorflow graph. This has been better explained here.

The values of the different variables in the graph at that moment are stored separately in the checkpoint folder in checkpoint.data-xxxx-of-xxxx file.

There is no concept of an input or output node in the normal checkpoint process, as opposed to the case of a frozen model. Freezing a model outputs a subset of the whole tensorflow graph. This subset of the main graph has only those nodes present on which the output node is dependent on. Because freezing a model is done for serving purposes, it converts the tensorflow variables to constants, eliminating the need for storing additional information like gradients of the different variables at each step.

If you still want to identify the nodes you would be interested in, you can restore your graph from the .meta file and visualize it in tensorboard.

import tensorflow as tf
from tensorflow.summary import FileWriter

sess = tf.Session()
tf.train.import_meta_graph("your-meta-graph-file.meta")
FileWriter("__tb", sess.graph)

This will create a __tb folder in your current directory and you can then view the graph by issuing the following command.

tensorboard --logdir __tb

Here is a link to the screenshot of some model with a node selected. You can get the name of the node from the top right corner.

How to find the exact name of the output node in .pb file?

• Question 26 in the ‘Model Optimizer Frequently Asked Questions’ page addresses the error that you are facing. See https://docs.openvinotoolkit.org/2020.3/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html

• I would suggest you try the following potential methods that have been shared in Github, available at the following links:

  https://github.com/tensorflow/tensorflow/issues/3986#issuecomment-304619868
  https://github.com/tensorflow/tensorflow/issues/3986#issuecomment-368282326
  https://github.com/tensorflow/tensorflow/issues/3986#issuecomment-470083664
  https://github.com/tensorflow/tensorflow/issues/3986#issuecomment-508897579

• Do let us know if any of these works for you.

What are input and output node names in inception v3 with slim library?

(A). If you will make it to bottom of this link. You would find this somewhere(specific to inceptionV3) :

input_layer=input
output_layer=InceptionV3/Predictions/Reshape_1

(B). Another way is to print all tensors of the model and get input/output tensor

from tensorflow.python.tools.inspect_checkpoint import print_tensors_in_checkpoint_file
ckpt_path="model.ckpt"
print_tensors_in_checkpoint_file(file_name=ckpt_path, tensor_name='', all_tensors=True, all_tensor_names=True)

(C). If you need to print tensor names of .pb file. You can use this simple code.

Check what would work for you.

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