70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
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import timing
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import argparse
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import sys
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import numpy as np
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import torch.nn as nn
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from torch.autograd import Variable
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from torch.optim import SGD
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from torch.utils.data import DataLoader
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from util.util import enumerateWithEstimate
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from .dsets import PrepcacheLunaDataset, getCtSampleSize
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from util.logconf import logging
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# from .model import LunaModel
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log = logging.getLogger(__name__)
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# log.setLevel(logging.WARN)
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log.setLevel(logging.INFO)
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# log.setLevel(logging.DEBUG)
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class LunaPrepCacheApp:
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@classmethod
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def __init__(self, sys_argv=None):
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if sys_argv is None:
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sys_argv = sys.argv[1:]
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parser = argparse.ArgumentParser()
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parser.add_argument('--batch-size',
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help='Batch size to use for training',
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default=1024,
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type=int,
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)
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parser.add_argument('--num-workers',
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help='Number of worker processes for background data loading',
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default=8,
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type=int,
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)
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# parser.add_argument('--scaled',
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# help="Scale the CT chunks to square voxels.",
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# default=False,
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# action='store_true',
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# )
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self.cli_args = parser.parse_args(sys_argv)
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def main(self):
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log.info("Starting {}, {}".format(type(self).__name__, self.cli_args))
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self.prep_dl = DataLoader(
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PrepcacheLunaDataset(
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# sortby_str='series_uid',
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),
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batch_size=self.cli_args.batch_size,
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num_workers=self.cli_args.num_workers,
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)
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batch_iter = enumerateWithEstimate(
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self.prep_dl,
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"Stuffing cache",
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start_ndx=self.prep_dl.num_workers,
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)
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for batch_ndx, batch_tup in batch_iter:
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pass
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if __name__ == '__main__':
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LunaPrepCacheApp().main()
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