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Hello.
I am new to openvino and using Openvino 2020.1.023
I use the LPRNet from the link below:
https://github.com/lyl8213/Plate_Recognition-LPRnet
I freeze the model using the code below:
with tf.Session(graph=tf.Graph()) as sess: # We import the meta graph in the current default Graph saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=clear_devices) graph_def = tf.get_default_graph().as_graph_def() node_list=[n.name for n in graph_def.node] # We restore the weights saver.restore(sess, input_checkpoint) # Dddd=output_node_names.split(",") # We use a built-in TF helper to export variables to constants output_graph_def = tf.graph_util.convert_variables_to_constants( sess, # The session is used to retrieve the weights tf.get_default_graph().as_graph_def(), # The graph_def is used to retrieve the nodes node_list # The output node names are used to select the usefull nodes ) # tf.train.write_graph(sess.graph_def, '.', 'tfdroid.pbtxt') # Finally we serialize and dump the output graph to the filesystem with tf.gfile.GFile(output_graph, "wb") as f: f.write(output_graph_def.SerializeToString()) print("%d ops in the final graph." % len(output_graph_def.node)) return output_graph_def
and use the command below:
python3 mo.py --input_model /home/ibox/Desktop/BusParkingDetection/frozen_mode/frozen_model.pb --output_dir /home/ibox/Desktop/ --input_shape [2,60,24,3] --input Placeholder --log_level=DEBUG
The debug text and model.pbtxt are in attach files, Really appreciate your help!
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Hello, LAI, PEI YU.
As I can see, the exception error is the same as in your other topic, so please use my recommendations from there for this one too - https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit/topic/849006
Best regards, Max.
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I saw a way to solve this problem in https://github.com/opencv/dldt/issues/98
My debug files contains Assign.... , which means it is not a frozen graph.
I examined the output node names and chose the correct one, now I am able to convert the LPRNet!
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Hello LAI, PEI YU.
We are really glad that you were able to convert the LPRNet model. Thanks for sharing this information with us.
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