| #data | |
| dataset_name: deepjeb | |
| test_name: "baseline" | |
| gpu_id: 0 | |
| epochs: 300 | |
| data_dir: /raid/ansysai/udbhav/alpha_Xdata/xgm_data/data_prep_transformer/2_Structural_linear_static/deepjeb/1_VTK_surface/ | |
| json_file: ${data_dir}/../full_transform_params.json | |
| splits_file: ${data_dir}/../ | |
| data_folder: ${dataset_name} | |
| normalization: "std_norm" | |
| norm_vars: "von_mises_stress" | |
| physical_scale_for_test: True | |
| # num_points: 40000 | |
| num_points: 15000 | |
| num_workers: 1 | |
| #model | |
| indim: 3 | |
| outdim: 4 | |
| model: ansysLPFMs | |
| hidden_dim: 256 | |
| n_heads: 8 | |
| n_decoder: 8 | |
| mlp_ratio: 2 | |
| #training | |
| val_iter: 1 | |
| lr: 0.001 | |
| batch_size: 1 | |
| optimizer: | |
| type: AdamW | |
| scheduler: OneCycleLR #OneCycleLR | |
| loss_type: huber # options: mse, mae, huber | |
| # scheduler: LinearWarmupCosineAnnealingLR | |
| num_processes: 1 | |
| max_grad_norm: 1.0 | |
| mixed_precision: True #currently default fp16 is selected by torch.autocast(). Fp16 gave the best results for Transformer based models. | |
| eval: False | |
| chunked_eval: True # Default with True is evaluation of max chunks of size num_points that can fit in a data sample, to avoid small last chunks | |
| train_ckpt_load: False ## Will load best model if ckpt_load is false | |
| #logging | |
| # test_name: "Final_surface_only_OCLR_3p9M_float32_A100" | |
| pos_embed_sincos: True | |
| project_name: ${dataset_name} |