CODEWITHSUNDEEP
pythontensorflowkerasprotocol-buffers
Sunshine
Sunshine

Reputation: 191

using tensorflow: google.protobuf.message.DecodeError: Wrong wire type in tag

for my project I'm trying to do inference based on my trained model stored in saved_model.pb. I doubt that the mistake is due to my code you can see over here but more likely is due to an installation problem:

from PIL import Image
import numpy as np
import scipy
from scipy import misc
import matplotlib.pyplot as plt
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

with tf.Graph().as_default() as graph: # Set default graph as graph

           with tf.Session() as sess:
                # Load the graph in graph_def
                print("load graph")

                # We load the protobuf file from the disk and parse it to retrive the unserialized graph_drf
                with gfile.FastGFile("saved_model.pb",'rb') as f:

                                from scipy.io import wavfile
                                samplerate, data = wavfile.read('sound.wav')

                                # Set FCN graph to the default graph
                                graph_def = tf.GraphDef()
                                graph_def.ParseFromString(f.read())
                                sess.graph.as_default()

                                # Import a graph_def into the current default Graph (In this case, the weights are (typically) embedded in the graph)

                                tf.import_graph_def(
                                graph_def,
                                input_map=None,
                                return_elements=None,
                                name="",
                                op_dict=None,
                                producer_op_list=None
                                )

                                # Print the name of operations in the session
                                for op in graph.get_operations():
                                        print("Operation Name :",op.name)         # Operation name
                                        print("Tensor Stats :",str(op.values()))     # Tensor name

                                # INFERENCE Here
                                l_input = graph.get_tensor_by_name('Inputs/fifo_queue_Dequeue:0') # Input Tensor
                                l_output = graph.get_tensor_by_name('upscore32/conv2d_transpose:0') # Output Tensor

                                print("Shape of input : ", tf.shape(l_input))
                                
                                f.global_variables_initializer()

                                # Run model on single image
                                Session_out = sess.run( m_output, feed_dict = {m_input : data} )

                                print("Predicted class:", class_names[Session_out[0].argmax()] )

The traceback is the following

Traceback (most recent call last):
  File "/home/pi/model_inference/test.py", line 11, in <module>
    graph_def.ParseFromString(f.read())
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/message.py", line 199, in ParseFromString
    return self.MergeFromString(serialized)
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1145, in MergeFromString
    if self._InternalParse(serialized, 0, length) != length:
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1212, in InternalParse
    pos = field_decoder(buffer, new_pos, end, self, field_dict)
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 754, in DecodeField
    if value._InternalParse(buffer, pos, new_pos) != new_pos:
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1212, in InternalParse
    pos = field_decoder(buffer, new_pos, end, self, field_dict)
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 733, in DecodeRepeatedField
    if value.add()._InternalParse(buffer, pos, new_pos) != new_pos:
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1212, in InternalParse
    pos = field_decoder(buffer, new_pos, end, self, field_dict)
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 888, in DecodeMap
    if submsg._InternalParse(buffer, pos, new_pos) != new_pos:
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/python_message.py", line 1199, in InternalParse
    buffer, new_pos, wire_type)  # pylint: disable=protected-access
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 989, in _DecodeUnknownField
    (data, pos) = _DecodeUnknownFieldSet(buffer, pos)
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 968, in _DecodeUnknownFieldSet
    (data, pos) = _DecodeUnknownField(buffer, pos, wire_type)
  File "/usr/local/lib/python3.7/dist-packages/google/protobuf/internal/decoder.py", line 993, in _DecodeUnknownField
    raise _DecodeError('Wrong wire type in tag.')
google.protobuf.message.DecodeError: Wrong wire type in tag.

Important to note is that I'm trying this out on a raspberry pi v4 (thus linux running on it). I would be glad about any hint what to do. Thanks in advance!

Upvotes: 1

Views: 14322

Answers (3)

Adam Bagby
Adam Bagby

Reputation: 1

Make sure the object IS an object and not a non-type object.

Upvotes: 0

Fred
Fred

Reputation: 61

Try using tf.saved_model.load(path) where path is the path to your saved model's folder containing the assets and variables folder.follow this link to the tensorflow object-detection api inference tutorial.

Upvotes: 2

Christian
Christian

Reputation: 2526

It looks like your file "saved_model.pb" is not a saved (wireformat) protobuffer of the message type GraphDef. Maybe you can look how that was saved and find some instructions on how to load it back? Just guessing from the name, can it be a keras model and you have to use tf.keras.models.load_model?

Upvotes: 0

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