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| from .base_options import BaseOptions | |
| class TrainOptions(BaseOptions): | |
| def initialize(self): | |
| BaseOptions.initialize(self) | |
| # for displays | |
| self.parser.add_argument('--display_freq', type=int, default=100, | |
| help='frequency of showing training results on screen') | |
| self.parser.add_argument('--print_freq', type=int, default=100, | |
| help='frequency of showing training results on console') | |
| self.parser.add_argument('--save_latest_freq', type=int, | |
| default=1000, help='frequency of saving the latest results') | |
| self.parser.add_argument('--save_epoch_freq', type=int, default=10, | |
| help='frequency of saving checkpoints at the end of epochs') | |
| self.parser.add_argument('--no_html', action='store_true', | |
| help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') | |
| self.parser.add_argument('--debug', action='store_true', | |
| help='only do one epoch and displays at each iteration') | |
| # for training | |
| self.parser.add_argument('--load_pretrain', type=str, default='./checkpoints/label2city', | |
| help='load the pretrained model from the specified location') | |
| self.parser.add_argument('--which_epoch', type=str, default='latest', | |
| help='which epoch to load? set to latest to use latest cached model') | |
| self.parser.add_argument( | |
| '--phase', type=str, default='test', help='train, val, test, etc') | |
| self.parser.add_argument('--serial_batches', action='store_true', | |
| help='if true, takes images in order to make batches, otherwise takes them randomly') | |
| self.parser.add_argument( | |
| '--niter', type=int, default=100, help='# of iter at starting learning rate') | |
| self.parser.add_argument('--niter_decay', type=int, default=100, | |
| help='# of iter to linearly decay learning rate to zero') | |
| self.parser.add_argument( | |
| '--beta1', type=float, default=0.5, help='momentum term of adam') | |
| self.parser.add_argument( | |
| '--lr', type=float, default=0.0002, help='initial learning rate for adam') | |
| # for discriminators | |
| self.parser.add_argument( | |
| '--num_D', type=int, default=2, help='number of discriminators to use') | |
| self.parser.add_argument( | |
| '--n_layers_D', type=int, default=3, help='only used if which_model_netD==n_layers') | |
| self.parser.add_argument( | |
| '--ndf', type=int, default=64, help='# of discrim filters in first conv layer') | |
| self.parser.add_argument( | |
| '--lambda_feat', type=float, default=10.0, help='weight for feature matching loss') | |
| self.parser.add_argument('--no_ganFeat_loss', action='store_true', | |
| help='if specified, do *not* use discriminator feature matching loss') | |
| self.parser.add_argument('--no_vgg_loss', action='store_true', | |
| help='if specified, do *not* use VGG feature matching loss') | |
| self.parser.add_argument('--no_lsgan', action='store_true', | |
| help='do *not* use least square GAN, if false, use vanilla GAN') | |
| self.parser.add_argument('--pool_size', type=int, default=0, | |
| help='the size of image buffer that stores previously generated images') | |
| self.isTrain = True | |