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from .base_options import BaseOptions
class TrainOptions(BaseOptions):
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser)
parser.add_argument('--earlystop_epoch', type=int, default=15)
parser.add_argument('--data_aug', action='store_true', help='if specified, perform additional data augmentation (photometric, blurring, jpegging)')
parser.add_argument('--optim', type=str, default='adam', help='optim to use [sgd, adam]')
parser.add_argument('--new_optim', action='store_true', help='new optimizer instead of loading the optim state')
parser.add_argument('--loss_freq', type=int, default=20, help='frequency of showing loss on tensorboard')
parser.add_argument('--save_latest_freq', type=int, default=2000, help='frequency of saving the latest results')
parser.add_argument('--save_epoch_freq', type=int, default=20, help='frequency of saving checkpoints at the end of epochs')
parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>, ...')
parser.add_argument('--last_epoch', type=int, default=-1, help='starting epoch count for scheduler intialization')
parser.add_argument('--niter', type=int, default=50, help='# of iter at starting learning rate')
# parser.add_argument('--niter', type=int, default=50, help='# of iter at starting learning rate')
parser.add_argument('--beta1', type=float, default=0.9, help='momentum term of adam')
parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam')
# parser.add_argument('--model_path')
# parser.add_argument('--no_resize', action='store_true')
# parser.add_argument('--no_crop', action='store_true')
parser.add_argument('--train_split', type=str, default='train', help='train, val, test, etc')
parser.add_argument('--val_split', type=str, default='val', help='train, val, test, etc')
self.isTrain = True
return parser
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