| from .base_options import BaseOptions | |
| class TrainOptions(BaseOptions): | |
| def initialize(self, parser): | |
| parser = BaseOptions.initialize(self, parser) | |
| parser.add_argument('--optim', type=str, default='adam', help='optim to use [sgd, adam]') | |
| parser.add_argument('--loss_freq', type=int, default=100, help='frequency of showing loss on tensorboard') | |
| parser.add_argument('--save_epoch_freq', type=int, default=1, | |
| help='frequency of saving checkpoints at the end of epochs') | |
| 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') | |
| parser.add_argument('--epoch', type=int, default=100, help='total epoches') | |
| parser.add_argument('--beta1', type=float, default=0.9, help='momentum term of adam') | |
| parser.add_argument('--lr', type=float, default=2e-9, help='initial learning rate for adam') | |
| parser.add_argument('--pretrained_model', type=str, default='./checkpoints/experiment_name/model_epoch_29.pth', help='model will fine tune on it if fine-tune is True') | |
| parser.add_argument('--fine-tune', type=bool, default=True) | |
| self.isTrain = True | |
| return parser | |