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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
This script tests the execution time of the DTLN model on a CPU.
Please use TF 2.2 for comparability.
Just run "python measure_execution_time.py"
Author: Nils L. Westhausen (nils.westhausen@uol.de)
Version: 13.05.2020
This code is licensed under the terms of the MIT-license.
"""
import time
import tensorflow as tf
import numpy as np
import os
# only use the cpu
os.environ["CUDA_VISIBLE_DEVICES"]=''
if __name__ == '__main__':
# loading model in saved model format
model = tf.saved_model.load('./pretrained_model/dtln_saved_model')
# mapping signature names to functions
infer = model.signatures["serving_default"]
exec_time = []
# create random input for testing
x = np.random.randn(1,512).astype('float32')
for idx in range(1010):
# run timer
start_time = time.time()
# infer one block
y = infer(tf.constant(x))['conv1d_1']
exec_time.append((time.time() - start_time))
# ignore the first ten iterations
print('Execution time per block: ' +
str( np.round(np.mean(np.stack(exec_time[10:]))*1000, 2)) + ' ms')
# Ubuntu 18.04 I5 6600k @ 3.5 GHz: 0.65 ms (4 cores)
# Macbook Air mid 2012 I7 3667U @ 2.0 GHz: 1.4 ms (2 cores)
# Raspberry Pi 3 B+ ARM Cortex A53 @ 1.4 GHz: 15.54 (4 cores)
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