client_utils.py

import numpy as np

def print_statistics(processing_times, batch_size):
    print('\nprocessing time for all iterations')
    print('average time: {:.2f} ms; average speed: {:.2f} fps'
          .format(round(np.average(processing_times), 2),
                  round(1000 * batch_size / np.average(processing_times), 2)))

    print('median time: {:.2f} ms; median speed: {:.2f} fps'
          .format(round(np.median(processing_times), 2),
                 round(1000 * batch_size / np.median(processing_times), 2)))

    print('max time: {:.2f} ms; min speed: {:.2f} fps'.format(round(np.max(processing_times), 2),round(1000 * batch_size / np.max(processing_times), 2)))

    print('min time: {:.2f} ms; max speed: {:.2f} fps'.format(round(np.min(processing_times), 2),
                                                          round(1000 * batch_size / np.min(processing_times), 2)))
                       
    print('time percentile 90: {:.2f} ms; speed percentile 90: {:.2f} fps'.format(
    round(np.percentile(processing_times, 90), 2),
    round(1000 * batch_size / np.percentile(processing_times, 90), 2)
))
    print('time percentile 50: {:.2f} ms; speed percentile 50: {:.2f} fps'.format(
    round(np.percentile(processing_times, 50), 2),
    round(1000 * batch_size / np.percentile(processing_times, 50), 2)))
    print('time standard deviation: {:.2f}'.format(round(np.std(processing_times), 2)))
    print('time variance: {:.2f}'.format(round(np.var(processing_times), 2)))