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21e6c23 | 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 | from __future__ import annotations
import json
import logging
import random
import sys
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))
from onescience.distributed.manager import DistributedManager
from onescience.utils.YParams import YParams
from train import build_datapipe, build_model, resolve_path
def setup_logging() -> logging.Logger:
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
return logging.getLogger("transolver-inference")
def load_checkpoint(path: Path, device: torch.device):
if not path.exists():
raise FileNotFoundError(f"Checkpoint not found: {path}. Run scripts/train.py first or provide a compatible weight.")
try:
return torch.load(path, map_location=device, weights_only=False)
except TypeError:
return torch.load(path, map_location=device)
def run_fake_inference(cfg_model, cfg_data, cfg_train, logger: logging.Logger, device: torch.device) -> int:
model_params = cfg_model.specific_params[cfg_model.name]
datapipe = build_datapipe(cfg_data, model_params, distributed=False)
test_loader = datapipe._loader(datapipe.test_dataset, False)[0]
model = build_model(cfg_model).to(device)
checkpoint_path = resolve_path(cfg_train.checkpoint_dir) / f"{cfg_model.name}.pth"
checkpoint = load_checkpoint(checkpoint_path, device)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
result_dir = resolve_path(cfg_train.result_dir)
result_dir.mkdir(parents=True, exist_ok=True)
mean_out, std_out = datapipe.coef_norm[2], datapipe.coef_norm[3]
preds, targets = [], []
criterion = nn.MSELoss()
with torch.no_grad():
for data in test_loader:
data = data.to(device)
out = model(data)
pred = out.cpu().numpy() * (std_out + 1e-8) + mean_out
target = data.y.cpu().numpy() * (std_out + 1e-8) + mean_out
preds.append(pred)
targets.append(target)
pred_arr = np.concatenate(preds, axis=0)
target_arr = np.concatenate(targets, axis=0)
mse = float(criterion(torch.from_numpy(pred_arr), torch.from_numpy(target_arr)))
np.savez(result_dir / "predictions.npz", pred=pred_arr, target=target_arr, mse=np.array(mse, dtype=np.float32))
(result_dir / "score.json").write_text(json.dumps({"model_name": cfg_model.name, "mse": mse}, indent=2), encoding="utf-8")
logger.info("Saved fake inference outputs to %s", result_dir)
logger.info("MSE: %.6f", mse)
return 0
def run_airfrans_inference(cfg_model, cfg_data, cfg_train, logger: logging.Logger, device: torch.device) -> int:
import pyvista as pv
import scipy.stats as sc
from tqdm import tqdm
from onescience.utils.transolver.metrics import (
Airfoil_test,
Compute_coefficients,
Infer_test,
NumpyEncoder,
rel_err,
)
model_params = cfg_model.specific_params[cfg_model.name]
hparams = model_params
hparams["subsampling"] = cfg_data.data.subsampling
datapipe = build_datapipe(cfg_data, model_params, distributed=False)
coef_norm = datapipe.coef_norm
test_loader = datapipe.test_dataloader()
test_names = datapipe.test_dataset.data_list_names
model = build_model(cfg_model).to(device)
checkpoint_path = resolve_path(cfg_train.checkpoint_dir) / f"{cfg_model.name}.pth"
checkpoint = load_checkpoint(checkpoint_path, device)
model.load_state_dict(checkpoint["model_state_dict"])
model.eval()
path_in = resolve_path(cfg_data.source.data_dir)
path_out = resolve_path(cfg_train.result_dir) / cfg_data.data.splits.task
path_out.mkdir(parents=True, exist_ok=True)
sample_count = min(int(cfg_train.n_test), len(test_names))
chosen = sorted(random.sample(range(len(test_names)), k=sample_count)) if sample_count else []
criterion = nn.MSELoss(reduction="none")
scores_vol, scores_surf, scores_force, scores_p, scores_wss = [], [], [], [], []
times, true_coefs, pred_coefs = [], [], []
for j, data in enumerate(tqdm(test_loader, desc="Testing")):
sim_name = test_names[j]
u_inf, angle = float(sim_name.split("_")[2]), float(sim_name.split("_")[3])
outs, elapsed = Infer_test(device, [model], [hparams], data, coef_norm=coef_norm)
times.append(elapsed)
intern = pv.read(path_in / sim_name / f"{sim_name}_internal.vtu")
aerofoil = pv.read(path_in / sim_name / f"{sim_name}_aerofoil.vtp")
tc, true_intern, true_airfoil = Compute_coefficients([intern], [aerofoil], data.surf.cpu(), u_inf, angle, keep_vtk=True)
tc, true_airfoil = tc[0], true_airfoil[0]
intern_pred, aerofoil_pred = Airfoil_test(intern, aerofoil, outs, coef_norm, data.surf.cpu())
pc, intern_pred_vtk, aerofoil_pred_vtk = Compute_coefficients(intern_pred, aerofoil_pred, data.surf.cpu(), u_inf, angle, keep_vtk=True)
true_coefs.append(tc)
pred_coefs.append(pc)
out = outs[0]
scores_vol.append(criterion(out[~data.surf], data.y[~data.surf]).mean(dim=0).cpu().numpy())
scores_surf.append(criterion(out[data.surf], data.y[data.surf]).mean(dim=0).cpu().numpy())
scores_force.append(rel_err(tc, pc[0]))
scores_wss.append(rel_err(true_airfoil.point_data["wallShearStress"], aerofoil_pred_vtk[0].point_data["wallShearStress"]).mean(axis=0))
scores_p.append(rel_err(true_airfoil.point_data["p"], aerofoil_pred_vtk[0].point_data["p"]).mean(axis=0))
if j in chosen:
intern_pred_vtk[0].save(path_out / f"{sim_name}_pred_internal.vtu")
aerofoil_pred_vtk[0].save(path_out / f"{sim_name}_pred_aerofoil.vtp")
true_coefs_arr = np.array(true_coefs)
pred_coefs_arr = np.array(pred_coefs)
spear = [
sc.spearmanr(true_coefs_arr[:, 0], pred_coefs_arr[:, 0, 0])[0],
sc.spearmanr(true_coefs_arr[:, 1], pred_coefs_arr[:, 0, 1])[0],
]
score = {
"model_name": cfg_model.name,
"mean_time": np.array(times).mean(axis=0),
"std_time": np.array(times).std(axis=0),
"mean_score_vol": np.array(scores_vol).mean(axis=0),
"mean_score_surf": np.array(scores_surf).mean(axis=0),
"mean_score_force": np.array(scores_force).mean(axis=0),
"mean_rel_p": np.array(scores_p).mean(axis=0),
"mean_rel_wss": np.array(scores_wss).mean(axis=0),
"spearman_coef": np.array(spear),
}
(path_out / f"score_{cfg_model.name}.json").write_text(json.dumps(score, indent=2, cls=NumpyEncoder), encoding="utf-8")
logger.info("Saved AirfRANS inference outputs to %s", path_out)
return 0
def main() -> int:
config_path = ROOT / "conf" / "config.yaml"
cfg_model = YParams(str(config_path), "model")
cfg_data = YParams(str(config_path), "datapipe")
cfg_train = YParams(str(config_path), "training")
logger = setup_logging()
DistributedManager.initialize()
device_name = getattr(cfg_train, "device", "auto")
if device_name == "cpu":
device = torch.device("cpu")
elif device_name.startswith("cuda"):
device = torch.device(device_name if torch.cuda.is_available() else "cpu")
else:
device = torch.device(f"cuda:{cfg_train.gpuid}" if torch.cuda.is_available() else "cpu")
if cfg_data.backend == "fake_airfrans":
return run_fake_inference(cfg_model, cfg_data, cfg_train, logger, device)
if cfg_data.backend == "airfrans":
return run_airfrans_inference(cfg_model, cfg_data, cfg_train, logger, device)
raise ValueError(f"Unsupported datapipe.backend: {cfg_data.backend}")
if __name__ == "__main__":
raise SystemExit(main())
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