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- # parser.py
- import argparse
-
- def get_parser():
- parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')
- parser.add_argument('--dataset_path', type=str, default="./data", help='Dataset path.')
- parser.add_argument('--data_url',
- help='path to training/inference dataset folder',
- default='./data')
-
- parser.add_argument('--train_url',
- help='model folder to save/load',
- default='./model')
-
- parser.add_argument('--result_url',
- help='folder to save inference results',
- default='./result')
- parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute.')
- parser.add_argument('--device_num', type=int, default=1, help='Device num.')
- parser.add_argument('--device_target', type=str, default="Ascend", help='Device choice Ascend or GPU')
- parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
- parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
- parser.add_argument('--epoch_size', type=int, default=1, help='Epoch size.')
- parser.add_argument('--batch_size', type=int, default=32, help='Batch size.')
- parser.add_argument('--num_classes', type=int, default=10, help='Num classes.')
- parser.add_argument('--checkpoint_path', type=str, default=None, help='CheckPoint file path.')
- args = parser.parse_args()
- return args
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