# = 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
# ======================================
# = custom =
# ======================================
# setup the normalization function for discriminator
if args.gradient_penalty_mode == 'none':
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
data_loader = data.make_custom_dataset(img_paths,
args.batch_size,
pin_memory=use_gpu)
n_G_upsamplings = n_D_downsamplings = ... # 3 for 32x32 and 4 for 64x64
# ======================================
# = custom =
# ======================================
