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- """
- Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
- Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
- """
-
- from .base_options import BaseOptions
-
-
- class TrainOptions(BaseOptions):
- def initialize(self, parser):
- BaseOptions.initialize(self, parser)
- # for displays
- parser.add_argument('--display_freq', type=int, default=100,
- help='frequency of showing training results on screen')
- parser.add_argument('--print_freq', type=int, default=100,
- help='frequency of showing training results on console')
- parser.add_argument('--save_latest_freq', type=int, default=5000,
- help='frequency of saving the latest results')
- parser.add_argument('--save_epoch_freq', type=int, default=10,
- 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 to [opt.checkpoints_dir]/[opt.name]/web/')
- parser.add_argument('--debug', action='store_true',
- help='only do one epoch and displays at each iteration')
- parser.add_argument('--tf_log', action='store_true',
- help='if specified, use tensorboard logging. Requires tensorflow installed')
-
- # for training
- parser.add_argument('--continue_train', action='store_true',
- help='continue training: load the latest model')
- parser.add_argument('--which_epoch', type=str, default='latest',
- help='which epoch to load? set to latest to use latest cached model')
- parser.add_argument('--niter', type=int, default=100,
- help='# of iter at starting learning rate. This is NOT the total #epochs. Totla #epochs is niter + niter_decay')
- parser.add_argument('--niter_decay', type=int, default=100,
- help='# of iter to linearly decay learning rate to zero')
- parser.add_argument('--optimizer', type=str, default='adam')
- parser.add_argument('--beta1', type=float,
- default=0.0, help='momentum term of adam')
- parser.add_argument('--beta2', type=float,
- default=0.9, help='momentum term of adam')
- parser.add_argument('--no_TTUR', action='store_true',
- help='Use TTUR training scheme')
-
- # the default values for beta1 and beta2 differ by TTUR option
- opt, _ = parser.parse_known_args()
- if opt.no_TTUR:
- parser.set_defaults(beta1=0.5, beta2=0.999)
-
- parser.add_argument('--lr', type=float, default=0.0002,
- help='initial learning rate for adam')
- parser.add_argument('--D_steps_per_G', type=int, default=1,
- help='number of discriminator iterations per generator iterations.')
-
- # for discriminators
- parser.add_argument('--ndf', type=int, default=64,
- help='# of discrim filters in first conv layer')
- parser.add_argument('--lambda_feat', type=float,
- default=10.0, help='weight for feature matching loss')
- parser.add_argument('--lambda_vgg', type=float,
- default=10.0, help='weight for vgg loss')
- parser.add_argument('--no_ganFeat_loss', action='store_true',
- help='if specified, do *not* use discriminator feature matching loss')
- parser.add_argument('--no_vgg_loss', action='store_true',
- help='if specified, do *not* use VGG feature matching loss')
- parser.add_argument('--gan_mode', type=str,
- default='hinge', help='(ls|original|hinge)')
- parser.add_argument(
- '--netD', type=str, default='multiscale', help='(n_layers|multiscale|image)')
- parser.add_argument('--lambda_kld', type=float, default=0.05)
- self.isTrain = True
- return parser
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