google.protobuf.message.DecodeError: Truncated message.

[Enjia]

I have transformed my pytorch model to tf model, and when I started loading the saved_model.pb file:

from tensorflow.python.platform import gfile import tensorflow as tf import tensorboard

model = './saved_model.pb' graph = tf.get_default_graph() graph_def = graph.as_graph_def()

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

I got the following problem hint: google.protobuf.message.DecodeError: Truncated message.

I searched for some methods but they didn't work, BTW as far as I known it's an intractable problem. Would anybody give me some advice? Thanks!

[namizzz]

Hi @Enjia ,have you tried this way to load your tfmodel? It may work.

[Enjia]

@namizzz Thanks, it worked indeed. Meanwhile I checked the generated tf_resnet50.py file, I found the last two lines of the codes say:

ResNetnFinalLayernfcnnLinearnfcn442 = tf.layers.dense(ResNetnFinalLayernfcnnLinearnfcn442_flatten, 12, kernel_initializer = tf.constant_initializer(__weights_dict['ResNetnFinalLayernfcnnLinearnfcn442']['weights']), bias_initializer = tf.constant_initializer(__weights_dict['ResNetnFinalLayernfcnnLinearnfcn442']['bias']), use_bias = True) and return input, ResNetnFinalLayernfcnnLinearnfcn442 As the section of "Reuse the converted Tensorflow model" shows, how can I get the finally output (xxxxxxxxxfc442, what a expatiatory name) through operation "sess.graph.get_tensor_by_name('xxx:0')" or any other methods because it seems there's no name for last fc442 layer in graph. Thanks again!

[namizzz]

Hi @Enjia ,you can add name by yourself first then dump out the model file.

[Enjia]

@namizzz Thank you so much! I have tested my classification model and prediction score was identical to the original model.

[Enjia]

Hi @namizzz thanks for the solutions several days ago. Nowadays I am deploying my pb model to a webserver using Flask. The webserver will allow user to upload an image and then will generate a predictionmy. My thought is that if webserver has to load weights for each request and then do the inference, it will take a lot more time. So, I shall create the graph and load the weights and keep that in the memory so that I can quickly serve each incoming request. In consideration of this situation, I have to use the following code:

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

    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def,
            input_map=None,
            return_elements=None,
            name=""
            )
    return graph

As I have mentioned some days ago, there's a problem with graph_def.ParseFromString(f.read()), that is "google.protobuf.message.DecodeError: Error parsing message"

The whole workflow code:

import sys
import os
import flask
from flask import render_template, send_from_directory, request, redirect,url_for
from werkzeug import secure_filename
from flask import jsonify
import base64
import StringIO
import tensorflow as tf 
import numpy as np
import cv2
app = flask.Flask(__name__)
UPLOAD_FOLDER='static'
def load_graph(trained_model):   
    with tf.gfile.GFile(trained_model, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def,
            input_map=None,
            return_elements=None,
            name=""
            )
    return graph
@app.route('/')
def index():
    return "Webserver is running"
@app.route('/demo',methods=['POST','GET'])
def demo():
    if request.method == 'POST':
        upload_file = request.files['file']
        filename = secure_filename(upload_file.filename)
        upload_file.save(os.path.join(UPLOAD_FOLDER, filename))
        image_size=128
        num_channels=3
        images = []
        image = cv2.imread(os.path.join(UPLOAD_FOLDER, filename))
        image = cv2.resize(image, (image_size, image_size), cv2.INTER_LINEAR)
        images.append(image)
        images = np.array(images, dtype=np.uint8)
        images = images.astype('float32')
        images = np.multiply(images, 1.0/255.0)
        x_batch = images.reshape(1, image_size,image_size,num_channels)
        graph =app.graph
        y_pred = graph.get_tensor_by_name("y_pred:0")
        x= graph.get_tensor_by_name("x:0")
        y_test_images = np.zeros((1, 2))    
        sess= tf.Session(graph=graph)
        feed_dict_testing = {x: x_batch}
        result=sess.run(y_pred, feed_dict=feed_dict_testing)
        out={"cat":str(result[0][0]),"dog":str(result[0][1])}
        return jsonify(out)
   return '''
    <!doctype html>
    <html lang="en">
    <head>
      <title>Running my first AI Demo</title>
    </head>
    <body>
    <div class="site-wrapper">
        <div class="cover-container">
            <nav id="main">
                <a href="http://localhost:5000/demo" >HOME</a>
            </nav>
          <div class="inner cover">
          </div>
          <div class="mastfoot">
          <hr />
            <div class="container">
              <div style="margin-top:5%">
                    <h1 style="color:black">Dogs Cats Classification Demo</h1>
                    <h4 style="color:black">Upload new Image </h4>
                    <form method=post enctype=multipart/form-data>
                     <p><input type=file name=file>
                    <input type=submit style="color:black;" value=Upload>
                    </form>
                </div>  
            </div>
            </div>
     </div>
   </div>
</body>
</html>
    '''
app.graph=load_graph('./saved_model.pb')  
if __name__ == '__main__':
    app.run(host="0.0.0.0", port=int("5000"), debug=True, use_reloader=False)

Can you give me some advise to solve this protobuf issue or replace my load_graph function with other solutions so that avoid using sentences like "graph_def.ParseFromString(f.read())"? Thanks a lot!

[namizzz]

Hi @Enjia

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

    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def,
            input_map=None,
            return_elements=None,
            name=""
            )
    return graph

It's a way to load fronzen protobuf TF model.

In MMdnn, we export TF model using API SavedModelBuilder.This is the right way to load the model:

export_dir = "./tf_resnet152"
with tf.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.TRAINING], export_dir)

    x = sess.graph.get_tensor_by_name('input:0')
    y = sess.graph.get_tensor_by_name('xxxxxx:0')
    ......
    _y = sess.run(y, feed_dict={x: _x})

This two .pb are different.

If you want to save time of loading model saved by API SavedModelBuilder, it's better to ask in stackoverflow or tensorflow. Thanks!

[Enjia]

@namizzz I see, I will modify the code, thanks for your advice anyway!