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| import os | |
| import argparse | |
| import importlib.util | |
| import torch | |
| from isegm.utils.exp import init_experiment | |
| def main(): | |
| args = parse_args() | |
| if args.temp_model_path: | |
| model_script = load_module(args.temp_model_path) | |
| else: | |
| model_script = load_module(args.model_path) | |
| model_base_name = getattr(model_script, 'MODEL_NAME', None) | |
| args.distributed = 'WORLD_SIZE' in os.environ | |
| cfg = init_experiment(args, model_base_name) | |
| torch.backends.cudnn.benchmark = True | |
| torch.multiprocessing.set_sharing_strategy('file_system') | |
| model_script.main(cfg) | |
| def parse_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('model_path', type=str, | |
| help='Path to the model script.') | |
| parser.add_argument('--exp-name', type=str, default='', | |
| help='Here you can specify the name of the experiment. ' | |
| 'It will be added as a suffix to the experiment folder.') | |
| parser.add_argument('--workers', type=int, default=4, | |
| metavar='N', help='Dataloader threads.') | |
| parser.add_argument('--batch-size', type=int, default=-1, | |
| help='You can override model batch size by specify positive number.') | |
| parser.add_argument('--ngpus', type=int, default=1, | |
| help='Number of GPUs. ' | |
| 'If you only specify "--gpus" argument, the ngpus value will be calculated automatically. ' | |
| 'You should use either this argument or "--gpus".') | |
| parser.add_argument('--gpus', type=str, default='', required=False, | |
| help='Ids of used GPUs. You should use either this argument or "--ngpus".') | |
| parser.add_argument('--resume-exp', type=str, default=None, | |
| help='The prefix of the name of the experiment to be continued. ' | |
| 'If you use this field, you must specify the "--resume-prefix" argument.') | |
| parser.add_argument('--resume-prefix', type=str, default='latest', | |
| help='The prefix of the name of the checkpoint to be loaded.') | |
| parser.add_argument('--start-epoch', type=int, default=0, | |
| help='The number of the starting epoch from which training will continue. ' | |
| '(it is important for correct logging and learning rate)') | |
| parser.add_argument('--weights', type=str, default=None, | |
| help='Model weights will be loaded from the specified path if you use this argument.') | |
| parser.add_argument('--temp-model-path', type=str, default='', | |
| help='Do not use this argument (for internal purposes).') | |
| parser.add_argument("--local_rank", type=int, default=0) | |
| # parameters for experimenting | |
| parser.add_argument('--layerwise-decay', action='store_true', | |
| help='layer wise decay for transformer blocks.') | |
| parser.add_argument('--upsample', type=str, default='x1', | |
| help='upsample the output.') | |
| parser.add_argument('--random-split', action='store_true', | |
| help='random split the patch instead of window split.') | |
| return parser.parse_args() | |
| def load_module(script_path): | |
| spec = importlib.util.spec_from_file_location("model_script", script_path) | |
| model_script = importlib.util.module_from_spec(spec) | |
| spec.loader.exec_module(model_script) | |
| return model_script | |
| if __name__ == '__main__': | |
| main() |