Conversion from h5 to Tensorflow pb file

I trained a yolov3 model (not the tiny version), and now I am trying to convert the saved yolo h5 file to Tensorflow pb, and so far I haven’t had luck. This is how I am trying to do the conversion:

import tensorflow as tf
from tensorflow.python.framework import graph_io
from tensorflow.keras.models import load_model


# Clear any previous session.
tf.keras.backend.clear_session()

def freeze_graph(graph, session, output, save_pb_dir='.', save_pb_name='yoloModel.pb', save_pb_as_text=False):
    with graph.as_default():
        graphdef_inf = tf.graph_util.remove_training_nodes(graph.as_graph_def())
        graphdef_frozen = tf.graph_util.convert_variables_to_constants(session, graphdef_inf, output)
        graph_io.write_graph(graphdef_frozen, save_pb_dir, save_pb_name, as_text=save_pb_as_text)
        return graphdef_frozen

tf.keras.backend.set_learning_phase(0)

model = load_model('yoloModel.h5')

session = tf.keras.backend.get_session()

INPUT_NODE = [t.op.name for t in model.inputs]
OUTPUT_NODE = [t.op.name for t in model.outputs]
print(INPUT_NODE, OUTPUT_NODE)
frozen_graph = freeze_graph(session.graph, session, [out.op.name for out in model.outputs], save_pb_dir=save_pb_dir)

I have seen other issues where people asked the same question and seems like some of them solved their problem. But I could not really understand some of the comments since they were in Chinese.

Could anyone who succeeded in converting the file, share his/her script?

Best, Frank

[tiancailin]

try this? https://github.com/amir-abdi/keras_to_tensorflow

[frankbrewer]

@tiancailin I tried your suggestion, but I guess according to output h5 file does not have the model information?

(conda-tensorflow) C:\Users\Usr2192\Desktop\conversionTry\amir-abdi>python keras_to_tensorflow.py --input_model="trainedWeights.h5" --output_model="out.pb"
Using TensorFlow backend.
E0109 13:46:07.891747 12844 keras_to_tensorflow.py:95] Input file specified only holds the weights, and not the model definition. Save the model using model.save(filename.h5) which will contain the network architecture as well as its weights. If the model is saved using the model.save_weights(filename) function, either input_model_json or input_model_yaml flags should be set to to import the network architecture prior to loading the weights.
Check the keras documentation for more details (https://keras.io/getting-started/faq/)
Traceback (most recent call last):
  File "keras_to_tensorflow.py", line 182, in <module>
    app.run(main)
  File "C:\Users\Usr2192\Anaconda3\envs\conda-tensorflow\lib\site-packages\absl\app.py", line 299, in run
    _run_main(main, args)
  File "C:\Users\Usr2192\Anaconda3\envs\conda-tensorflow\lib\site-packages\absl\app.py", line 250, in _run_main
    sys.exit(main(argv))
  File "keras_to_tensorflow.py", line 128, in main
    model = load_model(FLAGS.input_model, FLAGS.input_model_json, FLAGS.input_model_yaml)
  File "keras_to_tensorflow.py", line 106, in load_model
    raise wrong_file_err
  File "keras_to_tensorflow.py", line 63, in load_model
    model = keras.models.load_model(input_model_path)
  File "C:\Users\Usr2192\Anaconda3\envs\conda-tensorflow\lib\site-packages\keras\engine\saving.py", line 492, in load_wrapper
    return load_function(*args, **kwargs)
  File "C:\Users\Usr2192\Anaconda3\envs\conda-tensorflow\lib\site-packages\keras\engine\saving.py", line 584, in load_model
    model = _deserialize_model(h5dict, custom_objects, compile)
  File "C:\Users\Usr2192\Anaconda3\envs\conda-tensorflow\lib\site-packages\keras\engine\saving.py", line 270, in _deserialize_model
    model_config = h5dict['model_config']
  File "C:\Users\Usr2192\Anaconda3\envs\conda-tensorflow\lib\site-packages\keras\utils\io_utils.py", line 318, in __getitem__
    raise ValueError('Cannot create group in read-only mode.')
ValueError: Cannot create group in read-only mode.