| import train |
| import os |
| import torch |
| import argparse |
|
|
| from dataset.load_dataset import load_train_val_fold |
| from dataset.dataset import GraphDataset |
| from models.Transolver import Model |
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument('--data_dir', default='/data/PDE_data/mlcfd_data/training_data') |
| parser.add_argument('--save_dir', default='/data/PDE_data/mlcfd_data/preprocessed_data') |
| parser.add_argument('--fold_id', default=0, type=int) |
| parser.add_argument('--gpu', default=0, type=int) |
| parser.add_argument('--val_iter', default=10, type=int) |
| parser.add_argument('--cfd_config_dir', default='cfd/cfd_params.yaml') |
| parser.add_argument('--cfd_model') |
| parser.add_argument('--cfd_mesh', action='store_true') |
| parser.add_argument('--r', default=0.2, type=float) |
| parser.add_argument('--weight', default=0.5, type=float) |
| parser.add_argument('--lr', default=0.001, type=float) |
| parser.add_argument('--batch_size', default=1, type=int) |
| parser.add_argument('--nb_epochs', default=200, type=int) |
| parser.add_argument('--preprocessed', default=1, type=int) |
| args = parser.parse_args() |
| print(args) |
|
|
| hparams = {'lr': args.lr, 'batch_size': args.batch_size, 'nb_epochs': args.nb_epochs} |
|
|
| n_gpu = torch.cuda.device_count() |
| use_cuda = 0 <= args.gpu < n_gpu and torch.cuda.is_available() |
| device = torch.device(f'cuda:{args.gpu}' if use_cuda else 'cpu') |
|
|
| train_data, val_data, coef_norm = load_train_val_fold(args, preprocessed=args.preprocessed) |
| train_ds = GraphDataset(train_data, use_cfd_mesh=args.cfd_mesh, r=args.r) |
| val_ds = GraphDataset(val_data, use_cfd_mesh=args.cfd_mesh, r=args.r) |
|
|
| if args.cfd_model == 'Transolver': |
| model = Model(n_hidden=256, n_layers=8, space_dim=7, |
| fun_dim=0, |
| n_head=8, |
| mlp_ratio=2, out_dim=4, |
| slice_num=32, |
| unified_pos=0).cuda() |
|
|
| path = f'metrics/{args.cfd_model}/{args.fold_id}/{args.nb_epochs}_{args.weight}' |
| if not os.path.exists(path): |
| os.makedirs(path) |
|
|
| model = train.main(device, train_ds, val_ds, model, hparams, path, val_iter=args.val_iter, reg=args.weight, |
| coef_norm=coef_norm) |
|
|
