profile_tf.py

# -*- coding: utf-8 -*-
#
# Copyright (C) 2020 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
# acting on behalf of its Max Planck Institute for Intelligent Systems and the
# Max Planck Institute for Biological Cybernetics. All rights reserved.
#
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is holder of all proprietary rights
# on this computer program. You can only use this computer program if you have closed a license agreement
# with MPG or you get the right to use the computer program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and liable to prosecution.
# Contact: ps-license@tuebingen.mpg.de
#
#
# If you use this code in a research publication please consider citing the following:
#
# STAR: Sparse Trained  Articulated Human Body Regressor <https://arxiv.org/pdf/2008.08535.pdf>
#
#
# Code Developed by:
# Ahmed A. A. Osman

from star.tf.star import STAR 
import tensorflow as tf 
import time 
import numpy as np 

list_batch_size = [2,4,8,16,32,64,128,256,512]
for batch_size in list_batch_size:
    star = STAR()
    pose  = tf.random.normal((batch_size,72),dtype=tf.float32) 
    betas = tf.random.normal((batch_size,10),dtype=tf.float32)
    trans = tf.random.normal((batch_size,3),dtype=tf.float32)
    list_time = []
    for i in range(0,50):
        xstart = time.time()
        verts = star(pose,betas,trans)
        list_time.append(time.time()-xstart)
    print('Batch Size %d, Duration %f'%(batch_size,np.mean(list_time[10:])))