TF lite bath model test

tflite_bath_model.py

import tensorflow as tf
import numpy as np
from keras.models import load_model
import time

event_list = ['bathhub', 'shower', 'sink', 'toiletflush', 'others']
input_shape = (1, 1, 110250)

def postprocessing(response):
    response = np.reshape(response, (len(event_list)))
    res_list = [round(r,3) for r in response.tolist()]
    result = dict(zip(event_list, res_list))
    return result

def predict_keras(data, fs=22050):
    data = np.reshape(data, input_shape)

    # Load model
    model = load_model('m-2.h5')
    model.load_weights('weights_e570.h5')

    # Predict    
    pred = model.predict(data)
     
    res = postprocessing(pred)

    return res

def predict_tflite(data, fs=22050):
    data = np.reshape(data, input_shape)

    # Load TFLite model and allocate tensors.
    interpreter = tf.lite.Interpreter(model_path="model2-7.tflite")
    interpreter.allocate_tensors()

    # Get input and output tensors.
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    # Predict
    interpreter.set_tensor(input_details[0]['index'], data)
    interpreter.invoke()
    pred = interpreter.get_tensor(output_details[0]['index'])
    
    res = postprocessing(pred)

    return res

if __name__ == "__main__":
    task = 'event'
    
    input_data = np.load('input.npy')
    start_time = time.time()
    pred = predict_keras(input_data)
    end_time = time.time()
    print("< Keras Prediction >\n{}\nExecution Time: {:.3} second".format(pred, end_time - start_time))

    start_time = time.time()
    pred = predict_tflite(input_data)
    end_time = time.time()
    print("< TF lite Prediction >\n{}\nExecution Time: {:.3} second".format(pred, end_time - start_time))