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- from .base_options import BaseOptions
-
-
- class TrainOptions(BaseOptions):
- def initialize(self, parser):
- parser = BaseOptions.initialize(self, parser)
-
- # training epoch
- parser.add_argument('--iter_count', type=int, default=1, help='the starting epoch count')
- parser.add_argument('--niter', type=int, default=5000000, help='# of iter with initial learning rate')
- parser.add_argument('--niter_decay', type=int, default=0, help='# of iter to decay learning rate to zero')
-
- # learning rate and loss weight
- parser.add_argument('--lr_policy', type=str, default='lambda', help='learning rate policy[lambda|step|plateau]')
- parser.add_argument('--lr', type=float, default=1e-4, help='initial learning rate for adam')
- parser.add_argument('--gan_mode', type=str, default='lsgan', choices=['wgan-gp', 'hinge', 'lsgan'])
-
- # display the results
- parser.add_argument('--display_freq', type=int, default=1000, help='frequency of showing training results on screen')
- parser.add_argument('--eval_iters_freq', type=int, default=15000, help='frequency of showing training results on screen')
- parser.add_argument('--print_freq', type=int, default=1000, help='frequency of showing training results on console')
- parser.add_argument('--save_latest_freq', type=int, default=1000, help='frequency of saving the latest results')
- parser.add_argument('--save_iters_freq', type=int, default=10000, help='frequency of saving checkpoints at the end of epochs')
- parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results')
-
- self.isTrain = True
-
- return parser
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