text: #!/usr/bin/env python3
  
  import os
  import glob
  #os.environ['CUDA_VISIBLE_DEVICES'] = '2'
  import numpy as np
  import fid
  # from scipy.misc import imread
  from imageio import imread
  import tensorflow as tf
  
  ########
  # PATHS
  ########
  # data_path = '/media/data1/vox/Resized/test' # set path to training set images
  data_path = '/home/yuthon/Workspace/pix2pixHD/checkpoints/vox_embedded_ssim_4gpus_new_eval/eval/vox_embedded_eval/generated' # set path to training set images
  output_path = '/home/yuthon/Workspace/pix2pixHD/checkpoints/vox_embedded_ssim_4gpus_new_eval/eval/vox_embedded_eval/generated/fid_stats.npz' # path for where to store the statistics
  # if you have downloaded and extracted
  #   http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
  # set this path to the directory where the extracted files are, otherwise
  # just set it to None and the script will later download the files for you
  inception_path = None
  print("check for inception model..", end=" ", flush=True)
  inception_path = fid.check_or_download_inception(inception_path) # download inception if necessary
  print("ok")
  
  # loads all images into memory (this might require a lot of RAM!)
  # print("load images..", end=" " , flush=True)
  # image_list = glob.glob(os.path.join(data_path, '*.jpg'))
  # images = np.array([imread(str(fn)).astype(np.float32) for fn in image_list])
  # print("%d images found and loaded" % len(images))
  
  print("create inception graph..", end=" ", flush=True)
  fid.create_inception_graph(inception_path)  # load the graph into the current TF graph
  print("ok")
  
  print("calculte FID stats..", end=" ", flush=True)
  config = tf.ConfigProto()
  config.gpu_options.allow_growth=True
  with tf.Session(config=config) as sess:
      sess.run(tf.global_variables_initializer())
      mu, sigma = fid._handle_path(data_path, sess, low_profile=True)
      # mu, sigma = fid.calculate_activation_statistics(images, sess, batch_size=100)
      np.savez_compressed(output_path, mu=mu, sigma=sigma)
  print("finished")
  