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import os |
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import argparse |
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import torch |
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class Options(): |
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def __init__(self): |
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parser = argparse.ArgumentParser(description='PyTorch Classification') |
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parser.add_argument('--data_path', type=str, help='path to dataset where images store') |
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parser.add_argument('--train_set', type=str, help='train') |
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parser.add_argument('--val_set', type=str, help='validation') |
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parser.add_argument('--model_path', type=str, help='path to trained model') |
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parser.add_argument('--log_path', type=str, help='path to log files') |
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parser.add_argument('--task_name', type=str, help='task name for naming saved model files and log files') |
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parser.add_argument('--train', action='store_true', default=False, help='train only') |
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parser.add_argument('--test', action='store_true', default=False, help='test only') |
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parser.add_argument('--batch_size', type=int, default=6, help='batch size for origin global image (without downsampling)') |
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parser.add_argument('--log_interval_local', type=int, default=10, help='classification classes') |
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parser.add_argument('--resume', type=str, default="", help='path for model') |
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parser.add_argument('--graphcam', action='store_true', default=False, help='GraphCAM') |
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parser.add_argument('--dataset_metadata_path', type=str, help='Location of the metadata associated with the created dataset: label mapping, splits and so on') |
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self.parser = parser |
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def parse(self): |
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args = self.parser.parse_args() |
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args.num_epochs = 120 |
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args.lr = 1e-3 |
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if args.test: |
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args.num_epochs = 1 |
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return args |
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