tensorflow の実行が途中から進まないです

python初心者です。Taehoon Kim氏のtensorflow(1.13.1)画像自動生成デモ(タイトル名)を実行したところ以下のエラーが出てしまいました。
(macOS)ご教授頂けましたら幸いです。

python
1### 発生している問題・エラーメッセージ
2Traceback (most recent call last):
3  File "main.py", line 103, in <module>
4    tf.app.run()
5  File "/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 125, in run
6    _sys.exit(main(argv))
7  File "main.py", line 81, in main
8    data_dir=FLAGS.data_dir)
9  File "/Users/ina/model.py", line 81, in __init__
10    raise Exception("[!] No data found in '" + data_path + "'")
11Exception: [!] No data found in './data/celebA/*.jpg'

python
1
2
3### 該当のソースコード
4
5import os
6import scipy.misc
7import numpy as np
8
9from model import DCGAN
10from utils import pp, visualize, to_json, show_all_variables
11
12import tensorflow as tf
13
14flags = tf.app.flags
15flags.DEFINE_integer("epoch", 25, "Epoch to train [25]")
16flags.DEFINE_float("learning_rate", 0.0002, "Learning rate of for adam [0.0002]")
17flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]")
18flags.DEFINE_float("train_size", np.inf, "The size of train images [np.inf]")
19flags.DEFINE_integer("batch_size", 64, "The size of batch images [64]")```
20flags.DEFINE_integer("input_height", 108, "The size of image to use (will be center cropped). [108]")
21flags.DEFINE_integer("input_width", None, "The size of image to use (will be center cropped). If None, same value as input_height [None]")
22flags.DEFINE_integer("output_height", 64, "The size of the output images to produce [64]")
23flags.DEFINE_integer("output_width", None, "The size of the output images to produce. If None, same value as output_height [None]")
24flags.DEFINE_string("dataset", "celebA", "The name of dataset [celebA, mnist, lsun]")
25flags.DEFINE_string("input_fname_pattern", "*.jpg", "Glob pattern of filename of input images [*]")
26flags.DEFINE_string("checkpoint_dir", "checkpoint", "Directory name to save the checkpoints [checkpoint]")
27flags.DEFINE_string("data_dir", "./data", "Root directory of dataset [data]")
28flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]")
29flags.DEFINE_boolean("train", False, "True for training, False for testing [False]")
30flags.DEFINE_boolean("crop", False, "True for training, False for testing [False]")
31flags.DEFINE_boolean("visualize", False, "True for visualizing, False for nothing [False]")
32flags.DEFINE_integer("generate_test_images", 100, "Number of images to generate during test. [100]")
33FLAGS = flags.FLAGS
34
35
36def main(_):
37  pp.pprint(flags.FLAGS.__flags)
38
39  if FLAGS.input_width is None:
40    FLAGS.input_width = FLAGS.input_height
41  if FLAGS.output_width is None:
42    FLAGS.output_width = FLAGS.output_height
43
44  if not os.path.exists(FLAGS.checkpoint_dir):
45    os.makedirs(FLAGS.checkpoint_dir)
46  if not os.path.exists(FLAGS.sample_dir):
47    os.makedirs(FLAGS.sample_dir)
48
49  gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
50  run_config = tf.ConfigProto()
51  run_config.gpu_options.allow_growth=True
52
53  with tf.Session(config=run_config) as sess:
54    if FLAGS.dataset == 'mnist':
55      dcgan = DCGAN(
56          sess,
57          input_width=FLAGS.input_width,
58          input_height=FLAGS.input_height,
59          output_width=FLAGS.output_width,
60          output_height=FLAGS.output_height,
61          batch_size=FLAGS.batch_size,
62          sample_num=FLAGS.batch_size,
63          y_dim=10,
64          z_dim=FLAGS.generate_test_images,
65          dataset_name=FLAGS.dataset,
66          input_fname_pattern=FLAGS.input_fname_pattern,
67          crop=FLAGS.crop,
68          checkpoint_dir=FLAGS.checkpoint_dir,
69          sample_dir=FLAGS.sample_dir,
70          data_dir=FLAGS.data_dir )
71    else:
72      dcgan = DCGAN(
73          sess,
74          input_width=FLAGS.input_width,
75          input_height=FLAGS.input_height,
76          output_width=FLAGS.output_width,
77          output_height=FLAGS.output_height,
78          batch_size=FLAGS.batch_size,
79          sample_num=FLAGS.batch_size,
80          z_dim=FLAGS.generate_test_images,
81          dataset_name=FLAGS.dataset,
82          input_fname_pattern=FLAGS.input_fname_pattern,
83          crop=FLAGS.crop,
84          checkpoint_dir=FLAGS.checkpoint_dir,
85          sample_dir=FLAGS.sample_dir,
86          data_dir=FLAGS.data_dir)
87
88    show_all_variables()
89
90    if FLAGS.train:
91      dcgan.train(FLAGS)
92    else:
93      if not dcgan.load(FLAGS.checkpoint_dir)[0]:
94        raise Exception("[!] Train a model first, then run test mode")
95      
96
97    to_json("./web/js/layers.js", [dcgan.h0_w, dcgan.h0_b, dcgan.g_bn0],
98                    [dcgan.h1_w, dcgan.h1_b, dcgan.g_bn1],
99                    [dcgan.h2_w, dcgan.h2_b, dcgan.g_bn2],
100                     [dcgan.h3_w, dcgan.h3_b, dcgan.g_bn3],
101                     [dcgan.h4_w, dcgan.h4_b, None])
102
103     Below is codes for visualization
104    OPTION = 1
105    visualize(sess, dcgan, FLAGS, OPTION)
106
107if __name__ == '__main__':
108  tf.app.run()