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from .base_options import BaseOptions |
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class TrainOptions(BaseOptions): |
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def initialize(self): |
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BaseOptions.initialize(self) |
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self.parser.add_argument('--display_freq', type=int, default=30, help='frequency of showing training results on screen') |
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self.parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console') |
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self.parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') |
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self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs') |
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self.parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') |
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self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') |
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self.parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') |
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self.parser.add_argument('--niter', type=int, default=100, help='# of iter at starting learning rate') |
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self.parser.add_argument('--niter_decay', type=int, default=100, help='# of iter to linearly decay learning rate to zero') |
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self.parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') |
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self.parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate for adam') |
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self.parser.add_argument('--no_lsgan', action='store_true', help='do *not* use least square GAN, if false, use vanilla GAN') |
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self.parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images') |
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self.parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') |
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self.parser.add_argument('--config', type=str, default='configs/unit_gta2city_folder.yaml', help='Path to the config file.') |
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self.isTrain = True |
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