def get_inception_score()

in inception.py [0:0]

• 25 lines of code
• 5 McCabe index (conditional complexity)

def get_inception_score(images, splits=10):
  # For convenience
  if len(images[0].shape) != 3:
    return 0, 0

  # Bypassing all the assertions so that we don't end prematuraly'
  # 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 = 1
  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)