import os from data.pix2pix_dataset import Pix2pixDataset class Sunny2DiffWeathersDataset(Pix2pixDataset): @staticmethod def modify_commandline_options(parser, is_train): parser = Pix2pixDataset.modify_commandline_options(parser, is_train) parser.add_argument('--test_mode', type=str, default='all', help='specify style mode to control multi-modal image synthesis (MMIS) during test phase:' 'night | cloudy | rainy | snowy | all') parser.set_defaults(preprocess_mode='fixed') parser.set_defaults(load_size=512) parser.set_defaults(crop_size=512) parser.set_defaults(display_winsize=512) parser.set_defaults(aspect_ratio=2.0) opt, _ = parser.parse_known_args() if hasattr(opt, 'num_upsampling_layers'): parser.set_defaults(num_upsampling_layers='more') return parser def get_paths(self, opt): croot = opt.croot sroot = opt.sroot with open(os.path.join(croot, 'bdd100k_lists/sunny2diffweathers/sunny_%s.txt' % opt.phase)) as c_list: c_image_paths_read = c_list.read().splitlines() c_image_paths = [os.path.join(croot, p) for p in c_image_paths_read if p != ''] if opt.phase == 'train' or opt.test_mode == 'all': mode_list = ['night', 'cloudy', 'rainy', 'snowy'] else: mode_list = [opt.test_mode] s_image_paths = [] for mode in mode_list: with open(os.path.join(sroot, 'bdd100k_lists/sunny2diffweathers/%s_%s.txt' % (mode, opt.phase))) as s_list: s_image_paths_read = s_list.read().splitlines() s_image_paths_mode = [os.path.join(sroot, p) for p in s_image_paths_read if p != ''] s_image_paths.extend(s_image_paths_mode) while len(s_image_paths) < len(c_image_paths): s_image_paths = s_image_paths + s_image_paths instance_paths = [] length = min(len(c_image_paths), len(s_image_paths)) c_image_paths = c_image_paths[:length] s_image_paths = s_image_paths[:length] return c_image_paths, s_image_paths, instance_paths def paths_match(self, path1, path2): return True