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59 lines
2.6 KiB
Python
59 lines
2.6 KiB
Python
import sys
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sys.path.append('../code')
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import argparse
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import GPUtil
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import torch
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import random
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import numpy as np
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from training.train import IFTrainRunner
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def setup_seed(seed):
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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random.seed(seed)
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torch.backends.cudnn.deterministic = True
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
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parser.add_argument('--nepoch', type=int, default=400, help='number of epochs to train for')
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parser.add_argument('--nepoch_freeze', type=int, default=1000, help='number of epochs to train for')
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parser.add_argument('--conf', type=str, default='./confs/test.conf')
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parser.add_argument('--expname', type=str, default='')
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parser.add_argument('--gpu', type=str, default='auto', help='GPU to use [default: GPU auto]')
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parser.add_argument('--is_continue', default=False, action="store_true", help='If set, indicates continuing from a previous run.')
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parser.add_argument('--timestamp', default='latest', type=str, help='The timestamp of the run to be used in case of continuing from a previous run.')
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parser.add_argument('--checkpoint', default='latest',type=str,help='The checkpoint epoch number of the run to be used in case of continuing from a previous run.')
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parser.add_argument('--train_cameras', default=False, action="store_true", help='If set, optimizing also camera location.')
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parser.add_argument('--scan_id', type=int, default=-1, help='If set, taken to be the scan id.')
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opt = parser.parse_args()
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if opt.gpu == "auto":
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deviceIDs = GPUtil.getAvailable(order='memory', limit=1, maxLoad=0.5, maxMemory=0.5, includeNan=False, excludeID=[], excludeUUID=[])
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gpu = deviceIDs[0]
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else:
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gpu = opt.gpu
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setup_seed(0)
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trainrunner = IFTrainRunner(conf=opt.conf,
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batch_size=opt.batch_size,
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nepochs=opt.nepoch,
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nepoch_freeze=opt.nepoch_freeze,
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expname=opt.expname,
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gpu_index=gpu,
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exps_folder_name='exps',
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is_continue=opt.is_continue,
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timestamp=opt.timestamp,
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checkpoint=opt.checkpoint,
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scan_id=opt.scan_id,
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train_cameras=opt.train_cameras
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)
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trainrunner.run()
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