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