DecodeError: Truncated message.while running Graph.ParseFromString() - tensorflow

Question

I downloaded the code from tensorflow/tensorflow/image_retraining/retrain.py and modified a little to retrain the model based on my need(like path to find training images folder and location to save the model and labels etc). While running the retrain.py file I get the following message towards the end of the execution
An exception has occurred, use %tb to see the full traceback.
SystemExit
On viewing the stack trace using %tb I get
Traceback (most recent call last):
File "<ipython-input-11-06ad74d82e7c>", line 1, in <module>
runfile('C:/Users/Srikanth1.R/Desktop/Desktop/My_Folder/Inage analytics/hub-master/examples/image_retraining/retrain.py', wdir='C:/Users/Srikanth1.R/Desktop/Desktop/My_Folder/Inage analytics/hub-master/examples/image_retraining')
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
execfile(filename, namespace)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Srikanth1.R/Desktop/Desktop/My_Folder/Inage analytics/hub-master/examples/image_retraining/retrain.py", line 2424, in <module>
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\platform\app.py", line 134, in run
SystemExit
But although I get a model.pb file.
I used this file in my prediction of new images. But while Parsing the model.pb file the console is throwing the following error
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
execfile(filename, namespace)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/Srikanth1.R/Desktop/Desktop/My_Folder/Car_damage_prediction/tensorflow-master/tensorflow-master/tensorflow/examples/label_image/label_image.py", line 117, in <module>
graph = load_graph(model_file)
File "C:/Users/Srikanth1.R/Desktop/Desktop/My_Folder/Car_damage_prediction/tensorflow-master/tensorflow-master/tensorflow/examples/label_image/label_image.py", line 31, in load_graph
graph_def.ParseFromString(f.read())
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\message.py", line 185, in ParseFromString
self.MergeFromString(serialized)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\python_message.py", line 1083, in MergeFromString
if self._InternalParse(serialized, 0, length) != length:
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\python_message.py", line 1120, in InternalParse
pos = field_decoder(buffer, new_pos, end, self, field_dict)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\decoder.py", line 633, in DecodeField
if value._InternalParse(buffer, pos, new_pos) != new_pos:
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\python_message.py", line 1120, in InternalParse
pos = field_decoder(buffer, new_pos, end, self, field_dict)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\decoder.py", line 612, in DecodeRepeatedField
if value.add()._InternalParse(buffer, pos, new_pos) != new_pos:
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\python_message.py", line 1120, in InternalParse
pos = field_decoder(buffer, new_pos, end, self, field_dict)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\decoder.py", line 743, in DecodeMap
if submsg._InternalParse(buffer, pos, new_pos) != new_pos:
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\python_message.py", line 1109, in InternalParse
new_pos = local_SkipField(buffer, new_pos, end, tag_bytes)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\decoder.py", line 850, in SkipField
return WIRETYPE_TO_SKIPPER[wire_type](buffer, pos, end)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\decoder.py", line 799, in _SkipGroup
new_pos = SkipField(buffer, pos, end, tag_bytes)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\decoder.py", line 850, in SkipField
return WIRETYPE_TO_SKIPPER[wire_type](buffer, pos, end)
File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\google\protobuf\internal\decoder.py", line 814, in _SkipFixed32
raise _DecodeError('Truncated message.')
DecodeError: Truncated message.
This is my code for prediction on test images
def load_graph(model_file):
graph = tf.Graph()
graph_def = tf.GraphDef()
with open(model_file, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
def read_tensor_from_image_file(file_name,
input_height=299,
input_width=299,
input_mean=0,
input_std=255):
input_name = "file_reader"
output_name = "normalized"
file_reader = tf.read_file(file_name, input_name)
if file_name.endswith(".png"):
image_reader = tf.image.decode_png(
file_reader, channels=3, name="png_reader")
elif file_name.endswith(".gif"):
image_reader = tf.squeeze(
tf.image.decode_gif(file_reader, name="gif_reader"))
elif file_name.endswith(".bmp"):
image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")
else:
image_reader = tf.image.decode_jpeg(
file_reader, channels=3, name="jpeg_reader")
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0)
resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
sess = tf.Session()
result = sess.run(normalized)
return result
def load_labels(label_file):
label = []
proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
for l in proto_as_ascii_lines:
label.append(l.rstrip())
return label
if __name__ == "__main__":
file_name = "C:\\Users\\Srikanth1.R\\Desktop\\Car Images\\car.jpg"
model_file = "C:\\Users\\Srikanth1.R\\Desktop\\Desktop\\My_Folder\\Inage analytics\\hub-master\\examples\\image_retraining\\tmp\\saved_model\\saved_model.pb"
label_file = "C:\\Users\\Srikanth1.R\\Desktop\\Desktop\\My_Folder\\Inage analytics\\hub-master\\examples\\image_retraining\\tmp\\output_labels.txt"
input_height = 299
input_width = 299
input_mean = 0
input_std = 255
input_layer = "input"
output_layer = "InceptionV3/Predictions/Reshape_1"
parser = argparse.ArgumentParser()
parser.add_argument("--image", help="image to be processed")
parser.add_argument("--graph", help="graph/model to be executed")
parser.add_argument("--labels", help="name of file containing labels")
parser.add_argument("--input_height", type=int, help="input height")
parser.add_argument("--input_width", type=int, help="input width")
parser.add_argument("--input_mean", type=int, help="input mean")
parser.add_argument("--input_std", type=int, help="input std")
parser.add_argument("--input_layer", help="name of input layer")
parser.add_argument("--output_layer", help="name of output layer")
args = parser.parse_args()
if args.graph:
model_file = args.graph
if args.image:
file_name = args.image
if args.labels:
label_file = args.labels
if args.input_height:
input_height = args.input_height
if args.input_width:
input_width = args.input_width
if args.input_mean:
input_mean = args.input_mean
if args.input_std:
input_std = args.input_std
if args.input_layer:
input_layer = args.input_layer
if args.output_layer:
output_layer = args.output_layer
graph = load_graph(model_file)
t = read_tensor_from_image_file(
file_name,
input_height=input_height,
input_width=input_width,
input_mean=input_mean,
input_std=input_std)
input_name = "import/" + input_layer
output_name = "import/" + output_layer
input_operation = graph.get_operation_by_name(input_name)
output_operation = graph.get_operation_by_name(output_name)
with tf.Session(graph=graph) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
top_k = results.argsort()[-5:][::-1]
labels = load_labels(label_file)
for i in top_k:
print(labels[i], results[i])
Is the above mentioned DecodeError in some way related to the error I get while running retrain.py ?
Or are both the errors independent ?
Can anyone please tell me how to solve the above errors?
Thank you in advance ?

Answer 1

You can also use below mentioned approach for prediction,
with tf.Session(graph=tf.Graph()) as sess:
tf.saved_model.loader.load(
sess, [tf.saved_model.tag_constants.SERVING], <path for .pb file>)
sess.run(...)
If you still want to use,
def load_graph(model_file):
graph = tf.Graph()
graph_def = tf.GraphDef()
with open(model_file, "rb") as f:
graph_def.ParseFromString(f.read())
with graph.as_default():
tf.import_graph_def(graph_def)
return graph
make sure model_file, should be a frozen graph.
Please refer link, Frozen Graph for more details.