code: # SPDX-FileCopyrightText: 2020 SAP SE
  #
  # SPDX-License-Identifier: Apache-2.0
  
  # From https://github.com/openai/improved-gan/blob/master/inception_score/model.py
  # Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os.path
  import sys
  import tarfile
  
  import numpy as np
  from six.moves import urllib
  import tensorflow as tf
  import glob
  import scipy.misc
  import math
  import sys
  
  MODEL_DIR = '/tmp/imagenet'
  DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
  softmax = None
  
  # Call this function with list of images. Each of elements should be a 
  # numpy array with values ranging from 0 to 255.
  def get_inception_score(images, splits=10):
    assert(type(images) == list)
    assert(type(images[0]) == np.ndarray)
    assert(len(images[0].shape) == 3)
    assert(np.max(images[0]) > 10)
    assert(np.min(images[0]) >= 0.0)
    inps = []
    for img in images:
      img = img.astype(np.float32)
      inps.append(np.expand_dims(img, 0))
    bs = 100
    with tf.Session() as sess:
      preds = []
      n_batches = int(math.ceil(float(len(inps)) / float(bs)))
      for i in range(n_batches):
          # sys.stdout.write(".")
          # sys.stdout.flush()
          inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
          inp = np.concatenate(inp, 0)
          pred = sess.run(softmax, {'ExpandDims:0': inp})
          preds.append(pred)
      preds = np.concatenate(preds, 0)
      scores = []
      for i in range(splits):
        part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
        kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
        kl = np.mean(np.sum(kl, 1))
        scores.append(np.exp(kl))
      return np.mean(scores), np.std(scores)
  
  # This function is called automatically.
  def _init_inception():
    global softmax
    if not os.path.exists(MODEL_DIR):
      os.makedirs(MODEL_DIR)
    filename = DATA_URL.split('/')[-1]
    filepath = os.path.join(MODEL_DIR, filename)
    if not os.path.exists(filepath):
      def _progress(count, block_size, total_size):
        sys.stdout.write('\r>> Downloading %s %.1f%%' % (
            filename, float(count * block_size) / float(total_size) * 100.0))
        sys.stdout.flush()
      filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
      print()
      statinfo = os.stat(filepath)
      print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')
    tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)
    with tf.gfile.FastGFile(os.path.join(
        MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      _ = tf.import_graph_def(graph_def, name='')
    # Works with an arbitrary minibatch size.
    with tf.Session() as sess:
      pool3 = sess.graph.get_tensor_by_name('pool_3:0')
      ops = pool3.graph.get_operations()
      for op_idx, op in enumerate(ops):
          for o in op.outputs:
              shape = o.get_shape()
              shape = [s.value for s in shape]
              new_shape = []
              for j, s in enumerate(shape):
                  if s == 1 and j == 0:
                      new_shape.append(None)
                  else:
                      new_shape.append(s)
              o._shape = tf.TensorShape(new_shape)
      w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1]
      logits = tf.matmul(tf.squeeze(pool3), w)
      softmax = tf.nn.softmax(logits)
  
  if softmax is None:
      r = 5
   # _init_inception()
  
apis: numpy.sum numpy.exp tensorflow.squeeze numpy.max numpy.expand_dims numpy.min tensorflow.Session tensorflow.import_graph_def tensorflow.TensorShape numpy.std numpy.concatenate tensorflow.nn.softmax numpy.log numpy.mean tensorflow.GraphDef