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from .base_options import BaseOptions
class TrainOptions(BaseOptions):
def initialize(self):
BaseOptions.initialize(self)
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=5000, help='frequency of saving the latest results')
self.parser.add_argument('--save_epoch_freq', type=int, default=5, help='frequency of saving checkpoints at the end of epochs')
self.parser.add_argument('--save_display_freq', type=int, default=2500, help='save the current display of results every save_display_freq_iterations')
self.parser.add_argument('--continue_train', action='store_true', help='if continue training, load the latest model')
self.parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
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('--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('--ntrain', type=int, default=float("inf"), help='# of examples per epoch.')
self.parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
self.parser.add_argument('--no_lsgan', action='store_true', help='if true, do *not* use least square GAN, if false, use vanilla GAN')
self.parser.add_argument('--lambda_A', type=float, default=10.0, help='weight for cycle loss (A -> B -> A)')
self.parser.add_argument('--lambda_B', type=float, default=10.0, help='weight for cycle loss (B -> A -> B)')
self.parser.add_argument('--pool_size', type=int, default=0, help='the size of image buffer that stores previously generated images')
self.parser.add_argument('--preprocessing', type=str, default='resize_and_crop', help='resizing/cropping strategy')
self.isTrain = True
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