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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=99, 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=10000, help='frequency of saving the latest results') |
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self.parser.add_argument('--save_epoch_freq', type=int, default=10000, help='frequency of saving checkpoints at the end of epochs') |
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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('--debug', action='store_true', help='only do one epoch and displays at each iteration') |
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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('--load_pretrain', type=str, default='', help='load the pretrained model from the specified location') |
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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('--phase', type=str, default='train', help='train, val, test, etc') |
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self.parser.add_argument('--niter', type=int, default=10000, help='# of iter at starting learning rate') |
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self.parser.add_argument('--niter_decay', type=int, default=10000, 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=0.0002, help='initial learning rate for adam') |
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self.parser.add_argument('--num_D', type=int, default=2, help='number of discriminators to use') |
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self.parser.add_argument('--n_layers_D', type=int, default=4, help='only used if which_model_netD==n_layers') |
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self.parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') |
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self.parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss') |
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self.parser.add_argument('--lambda_id', type=float, default=20.0, help='weight for id loss') |
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self.parser.add_argument('--lambda_rec', type=float, default=10.0, help='weight for reconstruction loss') |
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self.parser.add_argument('--lambda_GP', type=float, default=10.0, help='weight for gradient penalty loss') |
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self.parser.add_argument('--no_ganFeat_loss', action='store_true', help='if specified, do *not* use discriminator feature matching loss') |
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self.parser.add_argument('--no_vgg_loss', action='store_true', help='if specified, do *not* use VGG feature matching loss') |
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self.parser.add_argument('--gan_mode', type=str, default='hinge', help='(ls|original|hinge)') |
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self.parser.add_argument('--pool_size', type=int, default=0, help='the size of image buffer that stores previously generated images') |
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self.parser.add_argument('--times_G', type=int, default=1, |
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help='time of training generator before traning discriminator') |
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self.isTrain = True |
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