Using Tensorflow 2.X model on OpenCV - c++

Question

I have to use a Tensorflow 2.X model with the OpenCV framework (v.4.X with C++).
To do this, I need a single .pb file or a .pb and a .pbtxt file, instead of a Tensorflow Saved Model like the one I have.
So my question is: Is there a way to convert a Saved Model in a format that OpenCV could read? Like, maybe, a caffe model?
I tried with MMdnn but it gives me a strange error:
Traceback (most recent call last):
File "/usr/local/bin/mmconvert", line 8, in <module>
sys.exit(_main())
File "/usr/local/lib/python3.5/dist-packages/mmdnn/conversion/_script/convert.py", line 102, in _main
ret = convertToIR._convert(ir_args)
File "/usr/local/lib/python3.5/dist-packages/mmdnn/conversion/_script/convertToIR.py", line 62, in _convert
from mmdnn.conversion.tensorflow.tensorflow_parser import TensorflowParser
File "/usr/local/lib/python3.5/dist-packages/mmdnn/conversion/tensorflow/tensorflow_parser.py", line 15, in <module>
from tensorflow.tools.graph_transforms import TransformGraph
ImportError: No module named 'tensorflow.tools.graph_transforms'
And I suppose it is because it was developed and tested with Tensorflow 1.X.
Edit: I also have the relative Keras model (now that it is integrated with Tensorflow 2), but it is incompatible with OpenCV DNN framework too. Trying converting it with MMdnn I get this error:
Traceback (most recent call last):
File "/usr/local/bin/mmconvert", line 8, in <module>
sys.exit(_main())
File "/usr/local/lib/python3.5/dist-packages/mmdnn/conversion/_script/convert.py", line 102, in _main
ret = convertToIR._convert(ir_args)
File "/usr/local/lib/python3.5/dist-packages/mmdnn/conversion/_script/convertToIR.py", line 46, in _convert
parser = Keras2Parser(model)
File "/usr/local/lib/python3.5/dist-packages/mmdnn/conversion/keras/keras2_parser.py", line 126, in __init__
model = self._load_model(model[0], model[1])
File "/usr/local/lib/python3.5/dist-packages/mmdnn/conversion/keras/keras2_parser.py", line 78, in _load_model
'DepthwiseConv2D': layers.DepthwiseConv2D})
File "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py", line 664, in model_from_json
return deserialize(config, custom_objects=custom_objects)
File "/usr/local/lib/python3.5/dist-packages/keras/layers/__init__.py", line 168, in deserialize
printable_module_name='layer')
File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object
list(custom_objects.items())))
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1056, in from_config
process_layer(layer_data)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/network.py", line 1042, in process_layer
custom_objects=custom_objects)
File "/usr/local/lib/python3.5/dist-packages/keras/layers/__init__.py", line 168, in deserialize
printable_module_name='layer')
File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 149, in deserialize_keras_object
return cls.from_config(config['config'])
File "/usr/local/lib/python3.5/dist-packages/keras/engine/base_layer.py", line 1179, in from_config
return cls(**config)
File "/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/layers/convolutional.py", line 484, in __init__
**kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/layers/convolutional.py", line 117, in __init__
self.kernel_initializer = initializers.get(kernel_initializer)
File "/usr/local/lib/python3.5/dist-packages/keras/initializers.py", line 515, in get
return deserialize(identifier)
File "/usr/local/lib/python3.5/dist-packages/keras/initializers.py", line 510, in deserialize
printable_module_name='initializer')
File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 140, in deserialize_keras_object
': ' + class_name)
ValueError: Unknown initializer: GlorotUniform
Edit 04/2021: Now the ONNX converter mentioned in the comments works properly with OpenCV 4.5.1 (Version 4.5.0 has a bug with some ONNX networks).

Answer 1

If you have the .h5 file, you can try this approach instead of MMdnn, using TensorFlow. The function converts the current session into a static computation graph to capture current states. Then you can write the graph in .pb format using tf.train.write_graph.
You can load the pretrained model with model = load_model('./model/keras_model.h5') before you freeze the graph. There is also a blog post for further explanation.