old_contents: #!/usr/bin/env python3
  from __future__ import absolute_import, division, print_function
  import os
  import glob
  #os.environ['CUDA_VISIBLE_DEVICES'] = '0'
  import numpy as np
  import fid
  from scipy.misc import imread
  import tensorflow as tf
  
  # Paths
  image_path = '/local00/bioinf/tmp/' # set path to some generated images
  stats_path = '/local00/bioinf/fid_stats_cifar10.npz' # training set statistics
  inception_path = fid.check_or_download_inception(None) # download inception network
  
  # loads all images into memory (this might require a lot of RAM!)
  image_list = glob.glob(os.path.join(datapath, '*.jpg'))
  images = np.array([imread(str(fn)).astype(np.float32) for fn in files])
  
  # load precalculated training set statistics
  f = np.load(path)
  mu_real, sigma_real = f['mu'][:], f['sigma'][:]
  f.close()
  
  fid.create_inception_graph(inception_path)  # load the graph into the current TF graph
  with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
      mu_gen, sigma_gen = fid.calculate_activation_statistics(images, sess, batch_size=100)
  
  fid_value = fid.calculate_frechet_distance(mu_gen, sigma_gen, mu_real, sigma_real)
  print("FID: %s" % fid_value)
  
new_contents: #!/usr/bin/env python3
  from __future__ import absolute_import, division, print_function
  import os
  import glob
  #os.environ['CUDA_VISIBLE_DEVICES'] = '0'
  import numpy as np
  import fid
  from scipy.misc import imread
  import tensorflow as tf
  
  # Paths
  image_path = '/local00/bioinf/tmp/' # set path to some generated images
  stats_path = '/local00/bioinf/fid_stats_cifar10.npz' # training set statistics
  inception_path = fid.check_or_download_inception(None) # download inception network
  
  # loads all images into memory (this might require a lot of RAM!)
  image_list = glob.glob(os.path.join(datapath, '*.jpg'))
  images = np.array([imread(str(fn)).astype(np.float32) for fn in files])
  
  # load precalculated training set statistics
  f = np.load(stats_path)
  mu_real, sigma_real = f['mu'][:], f['sigma'][:]
  f.close()
  
  fid.create_inception_graph(inception_path)  # load the graph into the current TF graph
  with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
      mu_gen, sigma_gen = fid.calculate_activation_statistics(images, sess, batch_size=100)
  
  fid_value = fid.calculate_frechet_distance(mu_gen, sigma_gen, mu_real, sigma_real)
  print("FID: %s" % fid_value)
  
subject: fix path typo