converte tensorflow_lite to savedModel

To convert a TensorFlow Lite model to a SavedModel, you can follow these steps:

Step 1: Install TensorFlow Make sure you have TensorFlow installed on your system. You can use the following command to install TensorFlow:

pip install tensorflow

Step 2: Load the TensorFlow Lite model Load the TensorFlow Lite model using the tf.lite.Interpreter() class. You can specify the path to the TensorFlow Lite model file (.tflite).

import tensorflow as tf

# Load the TensorFlow Lite model
interpreter = tf.lite.Interpreter(model_path='path/to/tflite/model.tflite')
interpreter.allocate_tensors()

Step 3: Convert to a SavedModel To convert the TensorFlow Lite model to a SavedModel, you need to create a tf.function and use the tf.saved_model.save() function. Here's an example:

# Create a tf.function from the interpreter
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

@tf.function(input_signature=[{input_details[0]['name']: tf.TensorSpec(shape=input_details[0]['shape'], dtype=input_details[0]['dtype'])}])
def lite_to_saved_model(inputs):
    interpreter.set_tensor(input_details[0]['index'], inputs[input_details[0]['name']])
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])
    return {output_details[0]['name']: output_data}

# Convert to a SavedModel
tf.saved_model.save(lite_to_saved_model, 'path/to/saved_model')

Step 4: Verify the SavedModel You can load the SavedModel and verify it using the saved_model_cli tool:

saved_model_cli show --dir path/to/saved_model --tag_set serve --signature_def serving_default

That's it! You have now converted your TensorFlow Lite model to a SavedModel