How to convert keras(h5) file to a tflite file?

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不思量自难忘° 2021-01-31 19:25

I got an keras(h5) file. I need to convert it to tflite?? I researched, First i need to go via h5 -> pb -> tflite (because h5 - tflite sometimes results in some issue)

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  • 2021-01-31 19:37

    Only some specific version of Tensorflow and Keras works properly in all the os. I even tried toco command line but it has issues too. Use tensorflow==1.13.0-rc1 and keras==2.1.3

    and then after this will work

    from tensorflow.contrib import lite
    converter = lite.TFLiteConverter.from_keras_model_file( 'model.h5' ) # Your model's name
    model = converter.convert()
    file = open( 'model.tflite' , 'wb' ) 
    file.write( model )
    
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  • 2021-01-31 19:43

    Convert RetinaNet to tflite

    import tensorflow as tf
    from keras_retinanet.models import load_model
    from keras.layers import Input
    from keras.models import Model
    
    def get_file_size(file_path):
        size = os.path.getsize(file_path)
        return size
        
    def convert_bytes(size, unit=None):
        if unit == "KB":
            return print('File size: ' + str(round(size / 1024, 3)) + ' Kilobytes')
        elif unit == "MB":
            return print('File size: ' + str(round(size / (1024 * 1024), 3)) + ' Megabytes')
        else:
            return print('File size: ' + str(size) + ' bytes')
    
    def convert_model_to_tflite(model_path = "/content/drive/MyDrive/Model/resnet152_csv_180_inference.h5", filename = "converted_model.tflite"):
      model = load_model(model_path)
      fixed_input = Input((416,416,3))
      fixed_model = Model(fixed_input,model(fixed_input))
      converter = tf.lite.TFLiteConverter.from_keras_model(model)
      converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
        tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
      ]
      tflite_model = converter.convert()
      open(filename, "wb").write(tflite_model)
      print(convert_bytes(get_file_size("converted_model.tflite"), "MB"))
    
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  • 2021-01-31 19:45

    Converting a GraphDef from the session.

    converter = lite.TFLiteConverter.from_session(sess, in_tensors, out_tensors)
    tflite_model = converter.convert()
    open("converted_model.tflite", "wb").write(tflite_model)
    

    Converting a GraphDef from file.

    converter = lite.TFLiteConverter.from_frozen_graph(
    graph_def_file, input_arrays, output_arrays)
    tflite_model = converter.convert()
    open("converted_model.tflite", "wb").write(tflite_model)
    

    Converting a SavedModel.

    converter = lite.TFLiteConverter.from_saved_model(saved_model_dir)
    tflite_model = converter.convert()
    
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  • 2021-01-31 19:47

    If You are using Google Colab Notebook try this:

    import tensorflow as tf
    converter = tf.lite.TFLiteConverter.from_keras_model_file('model.h5') 
    tfmodel = converter.convert() 
    open ('model.tflite' , "wb") .write(tfmodel)
    
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  • 2021-01-31 19:49
    from tensorflow.contrib import lite
    converter = lite.TFLiteConverter.from_keras_model_file( 'model.h5')
    tfmodel = converter.convert()
    open ("model.tflite" , "wb") .write(tfmodel)
    

    You can use the TFLiteConverter to directly convert .h5 files to .tflite file. This does not work on Windows.

    For Windows, use this Google Colab notebook to convert. Upload the .h5 file and it will convert it .tflite file.

    Follow, if you want to try it yourself :

    1. Create a Google Colab Notebook. In the left top corner, click the "UPLOAD" button and upload your .h5 file.
    2. Create a code cell and insert this code.

      from tensorflow.contrib import lite
      converter = lite.TFLiteConverter.from_keras_model_file( 'model.h5' ) # Your model's name
      model = converter.convert()
      file = open( 'model.tflite' , 'wb' ) 
      file.write( model )
      
    3. Run the cell. You will get a model.tflite file. Right click on the file and select "DOWNLOAD" option.

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  • 2021-01-31 19:54

    This worked for me on Windows 10 using Tensorflow 2.1.0 and Keras 2.3.1

    import tensorflow as tf
    
    model = tf.keras.models.load_model('model.h5')
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    tflite_model = converter.convert()
    open("converted_model.tflite", "wb").write(tflite_model)
    
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