| 20 | |
| 21 | |
| 22 | def fit_models(): |
| 23 | for pe in ["grid", "spherecplus", "sphericalharmonics"]: |
| 24 | for nn in ["fcnet", "siren"]: |
| 25 | for seed in np.arange(5): |
| 26 | args = Namespace(dataset='checkerboard', |
| 27 | pe=pe, |
| 28 | nn=nn, |
| 29 | save_model=False, |
| 30 | log_wandb=False, |
| 31 | hparams='hparams.yaml', |
| 32 | results_dir='results/train', |
| 33 | expname=f'exp_longitudinal/{seed}', |
| 34 | legendre_polys=None, # take from hparams file |
| 35 | seed=seed, |
| 36 | harmonics_calculation="analytic", |
| 37 | resume_ckpt_from_results_dir=False, |
| 38 | matplotlib=True, |
| 39 | matplotlib_show=False, |
| 40 | checkerboard_scale=1, |
| 41 | use_expnamehps=False, |
| 42 | max_epochs=None, |
| 43 | accelerator="cpu", |
| 44 | gpus=-1, |
| 45 | min_radius=None) |
| 46 | |
| 47 | fit(args) |
| 48 | return args |
| 49 | |
| 50 | def extract_df_to_csv(resultsdir): |
| 51 | seeds = [s for s in os.listdir(resultsdir) if os.path.isdir(os.path.join(resultsdir, s))] |