| """Physical and runtime audit for the refined conditional DENIM v2.""" |
|
|
| from __future__ import annotations |
|
|
| import json |
| import time |
| from collections import defaultdict |
|
|
| import torch |
|
|
| from src.t2_denim_conditional import ConditionalDENIM, rollout |
| from src.train_t2_denim_conditional_v2 import ARTIFACT_DIR, MODEL_DIR, load_all_trajectories |
|
|
|
|
| def run() -> dict[str, object]: |
| checkpoint = torch.load( |
| MODEL_DIR / "denim_conditional_v2_refined.pt", |
| map_location="cpu", |
| weights_only=False, |
| ) |
| model = ConditionalDENIM() |
| model.load_state_dict(checkpoint["state_dict"]) |
| model.eval() |
| trajectories = load_all_trajectories() |
| groups: dict[str, list] = defaultdict(list) |
| for trajectory in trajectories: |
| groups[trajectory.evaluation].append(trajectory) |
| selected = ( |
| groups["hidden_frozen_test"][:8] |
| + groups["test_path_ood"][:8] |
| + groups["test_long_horizon"][:8] |
| ) |
| strain = torch.stack([item.strain for item in selected]) |
| started = time.perf_counter() |
| with torch.no_grad(): |
| result = rollout( |
| strain, |
| torch.tensor([item.young for item in selected]), |
| torch.tensor([item.poisson for item in selected]), |
| torch.tensor([item.yield_stress for item in selected]), |
| torch.stack([item.descriptor for item in selected]), |
| model, |
| bisection_iterations=24, |
| ) |
| seconds = time.perf_counter() - started |
| reference = torch.stack([item.stress for item in selected]) |
| error = result["stress"] - reference |
| active = result["plastic_increment"] > 1.0e-11 |
| report = { |
| "model": "material-conditioned DENIM v2 refined", |
| "parameter_count": sum(parameter.numel() for parameter in model.parameters()), |
| "audit_trajectory_count": len(selected), |
| "audit_state_point_count": int(strain.shape[0] * strain.shape[1]), |
| "stress_rmse_mpa": float(torch.sqrt(error.square().mean()) / 1.0e6), |
| "maximum_yield_residual_pa": float(result["yield_residual"][active].abs().max()), |
| "minimum_peeq_increment": float(torch.diff(result["peeq"], dim=1).min()), |
| "maximum_plastic_strain_trace": float( |
| result["plastic_strain"][..., :3].sum(dim=-1).abs().max() |
| ), |
| "maximum_memory_trace_pa": float( |
| result["memories"][..., :3].sum(dim=-1).abs().max() |
| ), |
| "all_finite": all(torch.isfinite(value).all() for value in result.values()), |
| "inference_seconds": seconds, |
| "state_points_per_second": float(strain.shape[0] * strain.shape[1] / seconds), |
| "descriptor_hidden_hardening_entries_zero": bool( |
| torch.count_nonzero(torch.stack([x.descriptor[3:10] for x in selected])) == 0 |
| ), |
| "scope": "material-point audit; conditional v2 is not yet an AgentFEM global provider bundle", |
| } |
| ARTIFACT_DIR.mkdir(parents=True, exist_ok=True) |
| (ARTIFACT_DIR / "physical_runtime_validation.json").write_text( |
| json.dumps(report, indent=2) + "\n", encoding="utf-8" |
| ) |
| print(json.dumps(report, indent=2)) |
| return report |
|
|
|
|
| if __name__ == "__main__": |
| run() |
|
|
