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e9b4f6f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | from __future__ import annotations
import logging
import os
import sys
import time
import importlib.util
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
os.chdir(ROOT)
from model import Transolver3D, Transolver3D_plus
import onescience
from onescience.distributed.manager import DistributedManager
from onescience.utils.YParams import YParams
from onescience.utils.transolver import cal_coefficient, save_prediction_to_vtk, visualize_prediction
def load_shapenet_car_datapipe():
module_path = Path(onescience.__file__).resolve().parent / "datapipes/cfd/ShapeNetCar.py"
spec = importlib.util.spec_from_file_location("_onescience_shapenetcar", module_path)
if spec is None or spec.loader is None:
raise ImportError(f"Unable to load ShapeNetCarDatapipe from {module_path}")
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module.ShapeNetCarDatapipe
def setup_logging(rank: int) -> logging.Logger:
level = logging.INFO if rank == 0 else logging.WARNING
logging.basicConfig(
level=level,
format="%(asctime)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
logging.getLogger().setLevel(level)
return logging.getLogger(__name__)
def build_model(model_name: str, model_params, device: torch.device) -> torch.nn.Module:
model_cls = {
"Transolver": Transolver3D,
"Transolver_plus": Transolver3D_plus,
}.get(model_name)
if model_cls is None:
raise NotImplementedError(f"Model {model_name} initialization not implemented.")
return model_cls(
n_hidden=model_params.n_hidden,
n_layers=model_params.n_layers,
space_dim=model_params.space_dim,
fun_dim=model_params.fun_dim,
n_head=model_params.n_head,
mlp_ratio=model_params.mlp_ratio,
out_dim=model_params.out_dim,
slice_num=model_params.slice_num,
unified_pos=model_params.unified_pos,
).to(device)
def resolve_device(gpuid: int) -> torch.device:
if torch.cuda.is_available() and int(gpuid) >= 0:
return torch.device(f"cuda:{gpuid}")
return torch.device("cpu")
def maybe_calculate_coefficient(data_dir: Path, pred_press: np.ndarray, pred_velo: np.ndarray, gt_press: np.ndarray, gt_velo: np.ndarray):
if not (data_dir / "quadpress_smpl.vtk").exists() or not (data_dir / "hexvelo_smpl.vtk").exists():
return None, None
pred_coef = cal_coefficient(str(data_dir), pred_press[:, None], pred_velo)
gt_coef = cal_coefficient(str(data_dir), gt_press[:, None], gt_velo)
return pred_coef, gt_coef
def main() -> None:
DistributedManager.initialize()
manager = DistributedManager()
logger = setup_logging(manager.rank)
if manager.rank != 0:
logger.warning("Inference should run on a single process; exiting non-zero rank.")
return
config_file_path = str(ROOT / "conf/config.yaml")
cfg = YParams(config_file_path, "model")
cfg_data = YParams(config_file_path, "datapipe")
cfg_train = YParams(config_file_path, "training")
cfg_test = YParams(config_file_path, "inference")
model_name = cfg.name
model_params = cfg.specific_params[model_name]
cfg_data.model_hparams = model_params
device = resolve_device(cfg_test.gpuid)
logger.info("Using device: %s", device)
ShapeNetCarDatapipe = load_shapenet_car_datapipe()
datapipe = ShapeNetCarDatapipe(params=cfg_data, distributed=False)
val_dataset = datapipe.val_dataset
coef_norm = datapipe.coef_norm
val_names = val_dataset.data_list_names
test_loader, _ = datapipe.val_dataloader()
logger.info("Loaded %d validation samples.", len(val_dataset))
model = build_model(model_name, model_params, device)
checkpoint_path = Path(cfg_train.checkpoint_dir) / f"{model_name}.pth"
if not checkpoint_path.exists():
raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
checkpoint = torch.load(checkpoint_path, map_location=device)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
result_root = Path(cfg_test.result_dir) / model_name
npy_dir = result_root / "npy"
vtk_dir = result_root / "vtk"
vis_dir = result_root / "vis"
npy_dir.mkdir(parents=True, exist_ok=True)
if cfg_test.save_vtk:
vtk_dir.mkdir(parents=True, exist_ok=True)
if cfg_test.visualize:
vis_dir.mkdir(parents=True, exist_ok=True)
criterion_func = nn.MSELoss(reduction="none")
l2errs_press, l2errs_velo, mses_press, mses_velo_var, times = [], [], [], [], []
gt_coef_list, pred_coef_list = [], []
mean = torch.tensor(coef_norm[2], dtype=torch.float32, device=device)
std = torch.tensor(coef_norm[3], dtype=torch.float32, device=device)
with torch.no_grad():
for index, data in enumerate(test_loader):
if index >= len(val_names):
break
sample_name = val_names[index]
data = data.to(device)
tic = time.time()
out = model(data)
times.append(time.time() - tic)
targets = data.y
pred_press = out[data.surf, -1] * std[-1] + mean[-1]
gt_press = targets[data.surf, -1] * std[-1] + mean[-1]
pred_velo = out[~data.surf, :-1] * std[:-1] + mean[:-1]
gt_velo = targets[~data.surf, :-1] * std[:-1] + mean[:-1]
out_denorm = out * std + mean
y_denorm = targets * std + mean
safe_name = sample_name.replace("/", "_")
np.save(npy_dir / f"{index}_{safe_name}_pred.npy", out_denorm.cpu().numpy())
np.save(npy_dir / f"{index}_{safe_name}_gt.npy", y_denorm.cpu().numpy())
data_dir = ROOT / cfg_data.source.data_dir / sample_name
pred_coef, gt_coef = maybe_calculate_coefficient(
data_dir,
pred_press.cpu().numpy(),
pred_velo.cpu().numpy(),
gt_press.cpu().numpy(),
gt_velo.cpu().numpy(),
)
if pred_coef is not None and gt_coef is not None:
pred_coef_list.append(pred_coef)
gt_coef_list.append(gt_coef)
l2errs_press.append((torch.norm(pred_press - gt_press) / (torch.norm(gt_press) + 1e-8)).cpu().numpy())
l2errs_velo.append((torch.norm(pred_velo - gt_velo) / (torch.norm(gt_velo) + 1e-8)).cpu().numpy())
mses_press.append(criterion_func(out[data.surf, -1], targets[data.surf, -1]).mean().cpu().numpy())
mses_velo_var.append(criterion_func(out[~data.surf, :-1], targets[~data.surf, :-1]).mean().cpu().numpy())
if cfg_test.save_vtk and (data_dir / "quadpress_smpl.vtk").exists():
save_prediction_to_vtk(
out_denorm=out_denorm,
targets=targets,
cfd_data=data,
sample_name=sample_name,
output_dir=str(vtk_dir),
index=index,
data_dir=str(ROOT / cfg_data.source.data_dir),
)
if cfg_test.visualize and cfg_test.save_vtk:
visualize_prediction(output_dir=str(vtk_dir), vis_dir=str(vis_dir), index=index)
logger.info("Results saved to: %s", result_root)
logger.info("Relative L2 pressure: %.6f", float(np.mean(l2errs_press)))
logger.info("Relative L2 velocity: %.6f", float(np.mean(l2errs_velo)))
logger.info("RMSE pressure: %.6f", float(np.sqrt(np.mean(mses_press)) * coef_norm[3][-1]))
rmse_velo = np.sqrt(np.mean(mses_velo_var, axis=0)) * coef_norm[3][:-1]
logger.info("Combined velocity RMSE: %.6f", float(np.sqrt(np.mean(np.square(rmse_velo)))))
logger.info("Mean inference time (s): %.6f", float(np.mean(times)))
if gt_coef_list:
coef_error = np.mean(np.abs(np.array(pred_coef_list) - np.array(gt_coef_list)) / (np.array(gt_coef_list) + 1e-8))
logger.info("Mean relative CD error: %.6f", float(coef_error))
else:
logger.info("Skipped drag coefficient metrics because VTK geometry files were not present.")
if __name__ == "__main__":
main()
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