text: import numpy as np
  import scipy.misc
  import time
  import os
  from glob import glob
  
  def make_generator(path, batch_size, dataset):
      print("scan files", end=" ", flush=True)
      if dataset == "celeba":
        files = glob(os.path.join(path, "*.jpg"))
        dim = 64
      if dataset == "svhn" or dataset == "cifar10":
        files = glob(os.path.join(path, "*.png"))
        dim = 32
      if dataset == "lsun":
        # It's assumed the lsun images are splitted
        # into subdirectories named 0, 1, .., 304
        files = []
        for i in range(304):
          print("\rscan files %d" % i, end="", flush=True)
          files += glob(os.path.join(path, str(i), "*.jpg"))
        dim = 64
      n_files = len(files)
      print()
      print("%d images found" % n_files)
      def get_epoch():
          images = np.zeros((batch_size, 3, dim, dim), dtype='int32')
          files_idx = list(range(n_files))
          random_state = np.random.RandomState()
          random_state.shuffle(files_idx)
          for n, i in enumerate(files_idx):
              image = scipy.misc.imread(files[i])
              images[n % batch_size] = image.transpose(2,0,1)
              if n > 0 and n % batch_size == 0:
                  yield (images,)
      return get_epoch
  
  def load(batch_size, data_dir, dataset):
      return (
          make_generator(data_dir, batch_size, dataset),
          make_generator(data_dir, batch_size, dataset)
      )
  
  if __name__ == '__main__':
      train_gen, valid_gen = load(64)
      t0 = time.time()
      for i, batch in enumerate(train_gen(), start=1):
          print("s\t%d" % (str(time.time() - t0), batch[0][0,0,0,0]))
          if i == 1000:
              break
          t0 = time.time()
  