# = 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 = # ======================================