args.test_ints = args.test_ints * len(args.test_att_names)
sess = tl.session()
sess.__enter__() # make default
# ==============================================================================
# = data and model =
# ==============================================================================
# data
test_dataset, len_test_dataset = data.make_celeba_dataset(
args.img_dir,
args.test_label_path,
args.att_names,
args.n_samples,
load_size=args.load_size,
crop_size=args.crop_size,
training=False,
drop_remainder=False,
shuffle=False,
repeat=None)
test_iter = test_dataset.make_one_shot_iterator()
# ==============================================================================
# = graph =
# ==============================================================================
def sample_graph():
# ======================================
# = graph =
py.arg('--experiment_name', default='default')
args = py.args()
# output_dir
output_dir = py.join('output', args.experiment_name)
py.mkdir(output_dir)
# save settings
py.args_to_yaml(py.join(output_dir, 'settings.yml'), args)
# others
n_atts = len(args.att_names)
train_dataset, len_train_dataset = data.make_celeba_dataset(args.img_dir, args.train_label_path, args.att_names, args.batch_size,
load_size=args.load_size, crop_size=args.crop_size,
training=True, shuffle=False, repeat=None)
print(len_train_dataset)
print(train_dataset)
train_iter = train_dataset.make_one_shot_iterator()
sess = tl.session()
sess.__enter__() # make default
# get the next item
with tf.Session() as sess:
xa, a = train_iter.get_next()
b = tf.random_shuffle(a)
b_ = b * 2 - 1
# save settings
py.args_to_yaml(py.join(output_dir, 'settings.yml'), args)
# ==============================================================================
# = data and model =
# ==============================================================================
# setup dataset
if args.dataset in ['cifar10', 'fashion_mnist', 'mnist']: # 32x32
dataset, shape, len_dataset = data.make_32x32_dataset(args.dataset, args.batch_size)
n_G_upsamplings = n_D_downsamplings = 3
elif args.dataset == 'celeba': # 64x64
img_paths = py.glob('data/img_align_celeba', '*.jpg')
dataset, shape, len_dataset = data.make_celeba_dataset(img_paths, args.batch_size)
n_G_upsamplings = n_D_downsamplings = 4
elif args.dataset == 'anime': # 64x64
img_paths = py.glob('data/faces', '*.jpg')
dataset, shape, len_dataset = data.make_anime_dataset(img_paths, args.batch_size)
n_G_upsamplings = n_D_downsamplings = 4
elif args.dataset == 'custom':
# ======================================
# = custom =
# ======================================
img_paths = ... # image paths of custom dataset
dataset, shape, len_dataset = data.make_custom_dataset(img_paths, args.batch_size)
n_G_upsamplings = n_D_downsamplings = ... # 3 for 32x32 and 4 for 64x64
# ======================================
# ==============================================================================
# = data =
# ==============================================================================
# setup dataset
if args.dataset in ['cifar10', 'fashion_mnist', 'mnist']: # 32x32
data_loader, shape = data.make_32x32_dataset(args.dataset,
args.batch_size,
pin_memory=use_gpu)
n_G_upsamplings = n_D_downsamplings = 3
elif args.dataset == 'celeba': # 64x64
img_paths = py.glob('data/img_align_celeba', '*.jpg')
data_loader, shape = data.make_celeba_dataset(img_paths,
args.batch_size,
pin_memory=use_gpu)
n_G_upsamplings = n_D_downsamplings = 4
elif args.dataset == 'anime': # 64x64
img_paths = py.glob('data/faces', '*.jpg')
data_loader, shape = data.make_anime_dataset(img_paths,
args.batch_size,
pin_memory=use_gpu)
n_G_upsamplings = n_D_downsamplings = 4
elif args.dataset == 'custom':
# ======================================
# = custom =
# ======================================
img_paths = ... # image paths of custom dataset
