| 7 | import pandas as pd |
| 8 | |
| 9 | def parse_args(): |
| 10 | parser = argparse.ArgumentParser() |
| 11 | |
| 12 | # Add your arguments here |
| 13 | parser.add_argument('--dataset', default="landoceandataset", type=str, choices=["landoceandataset", "inat2018", "checkerboard"]) |
| 14 | parser.add_argument('--pe', default=["sphericalharmonics"], type=str, nargs='+', help='positional encoder(s)', choices=["sphericalharmonics", "theory", "grid", "spherec", "spherecplus", "spherem", "spheremplus", "direct", "cartesian3d", "wrap"]) |
| 15 | parser.add_argument('--nn', default=["siren"], type=str, nargs='+', help='neural network(s)', choices=["linear", "siren", "fcnet", "mlp"]) |
| 16 | parser.add_argument('--num-seeds', default=5, type=int, help='number of random seeds') |
| 17 | parser.add_argument('--gpus', default='-1', type=int, nargs='+', help='which gpus to use; if unset uses -1 which we map to auto') |
| 18 | parser.add_argument('--accelerator', default='auto', type=str, |
| 19 | help='lightning accelerator') |
| 20 | |
| 21 | parser.add_argument('-r', '--resume-ckpt-from-results-dir', action="store_true", |
| 22 | help="searches through provided results dir and resumes from suitable checkpoint " |
| 23 | "that matches pe and nn") |
| 24 | parser.add_argument('--no-fit', action="store_true", help='skip the fitting of the model and only compute results tables') |
| 25 | |
| 26 | args = parser.parse_args() |
| 27 | return args |
| 28 | |
| 29 | |
| 30 | def main(): |