mirror of
https://github.com/sbrl/research-rainfallradar
synced 2024-11-14 21:53:03 +00:00
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
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import argparse
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import sys
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import re
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import importlib
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# import pysnooper
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# @pysnooper.snoop()
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def parse_args():
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"""Defines and parses the CLI arguments."""
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if len(sys.argv) < 2:
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sys.stderr.write("""
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This program trains, manipulates, visualises, and manages a contrastive learning based rainfall radar → water depth prediction model.
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It functions by first finding relationships between the rainfall radar data and the water depth + heightmap data (the 'pretrain' subcommand). After this, a decoder model to predict water depth (modelled as an image segmentation task), can then be trained.
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Available subcommands:
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pretrain Pretrain a contrastive learning model as an encoder.
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For more information, do src/index.py <subcommand> --help.
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""")
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exit(0)
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subcommand = re.sub(r'[^a-z0-9-]', '', sys.argv[1])
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subcommand_argparser = importlib.import_module(f"subcommands.{subcommand}").parse_args
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parser = subcommand_argparser()
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# sys.stderr.write(f"Error: Unknown subcommand '{subcommand} (try --help).\n")
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# exit(1)
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if parser == None:
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sys.stderr.write(f"Error: The subcommand '{subcommand}' did not return an argument parser. This is a bug.\n")
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exit(1)
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parser.add_argument("--only-gpu",
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help="If the GPU is not available, exit with an error (useful on shared HPC systems to avoid running out of memory & affecting other users)", action="store_true")
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return subcommand, parser.parse_args(args=sys.argv[2:])
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