| """Hidden-family refinement with known-family replay for conditional DENIM v2.""" |
|
|
| from __future__ import annotations |
|
|
| import copy |
| import json |
| from collections import defaultdict |
|
|
| import torch |
|
|
| from src.t2_denim_conditional import ConditionalDENIM |
| from src.train_t2_denim_conditional_v2 import ( |
| ARTIFACT_DIR, |
| MODEL_DIR, |
| _train_stage, |
| evaluate, |
| load_all_trajectories, |
| transitions, |
| ) |
|
|
|
|
| def run(*, steps: int = 1200) -> dict[str, object]: |
| trajectories = load_all_trajectories() |
| hidden_name = "hidden_three_memory_tabulated_hardening" |
| known_train = [x for x in trajectories if x.material != hidden_name and x.role == "train"] |
| known_validation = [x for x in trajectories if x.material != hidden_name and x.role == "validation"] |
| hidden_train = [x for x in trajectories if x.material == hidden_name and x.role == "train"] |
| hidden_validation = [x for x in trajectories if x.material == hidden_name and x.role == "validation"] |
| data = { |
| "known_train": transitions(known_train), |
| "known_validation": transitions(known_validation), |
| "hidden_train": transitions(hidden_train), |
| "hidden_validation": transitions(hidden_validation), |
| } |
| checkpoint = torch.load(MODEL_DIR / "denim_conditional_v2.pt", map_location="cpu", weights_only=False) |
| model = ConditionalDENIM() |
| model.load_state_dict(checkpoint["state_dict"]) |
| refined = copy.deepcopy(model) |
| training = _train_stage( |
| refined, |
| [data["known_train"], data["hidden_train"], data["hidden_train"], data["hidden_train"]], |
| [data["known_validation"], data["hidden_validation"], data["hidden_validation"], data["hidden_validation"]], |
| steps=steps, |
| seed=20260930, |
| learning_rate=5.0e-4, |
| ) |
| groups: dict[str, list] = defaultdict(list) |
| for trajectory in trajectories: |
| groups[trajectory.evaluation].append(trajectory) |
| selected = ( |
| "hidden_frozen_test", |
| "test_path_ood", |
| "test_amplitude_ood", |
| "test_long_horizon", |
| "test_discretization", |
| ) |
| metrics: dict[str, object] = { |
| "training": training, |
| "evaluation": {name: evaluate(refined, groups[name]) for name in selected}, |
| } |
| known_test = [x for x in trajectories if x.material != hidden_name and x.role == "test"] |
| metrics["evaluation"]["known_strict_test_all"] = evaluate(refined, known_test) |
| torch.save( |
| { |
| "state_dict": refined.state_dict(), |
| "architecture": "ConditionalDENIM(channels=2, embedding=32, hidden=48)", |
| "stage": "hidden-focused refinement with known-family replay", |
| }, |
| MODEL_DIR / "denim_conditional_v2_refined.pt", |
| ) |
| ARTIFACT_DIR.mkdir(parents=True, exist_ok=True) |
| (ARTIFACT_DIR / "refined_metrics.json").write_text( |
| json.dumps(metrics, indent=2) + "\n", encoding="utf-8" |
| ) |
| print(json.dumps(metrics["evaluation"], indent=2)) |
| return metrics |
|
|
|
|
| if __name__ == "__main__": |
| run() |
|
|
