416 lines
14 KiB
Python
416 lines
14 KiB
Python
# drl_pinball/validate/validate_re100.py
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"""
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Validate new CelerisLab API vs LegacyCelerisLab for Karman cloak re100.
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This script:
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1. Generates reference data using LegacyCelerisLab (old API)
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2. Generates matching data using new CelerisLab.Simulation API
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3. Compares: target signals, norm values, uncontrolled rollout, controlled rollout
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4. Reports RMSE, max relative error, and correlation for each comparison
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Usage::
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conda run -n pycuda_3_10 python validate_re100.py --device 0
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conda run -n pycuda_3_10 python validate_re100.py --device 0 --steps 20 --quick
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import sys
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import time
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from typing import Any, Dict
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import numpy as np
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# Add project root and src to sys.path
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_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
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_SRC = os.path.join(_REPO, "src")
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if _REPO not in sys.path:
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sys.path.insert(0, _REPO)
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if _SRC not in sys.path:
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sys.path.insert(0, _SRC)
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# Legacy imports (from repo root: LegacyCelerisLab)
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from drl_pinball.legacy_env.legacy_karman_env import (
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legacy_build_re100,
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legacy_uncontrolled_re100,
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legacy_infer_re100,
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)
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# New API imports
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from drl_pinball.scenes.karman_cloak.re100_scene import KarmanRe100Scene
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# For loading PPO model
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from stable_baselines3 import PPO
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import torch
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from torch.nn import Module
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# ---------------------------------------------------------------------------
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# PPO model loader with Sin activation
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# ---------------------------------------------------------------------------
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class Sin(Module):
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def forward(self, x):
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return torch.sin(x)
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def _load_model(model_path: str, device: str, s_dim: int = 12, a_dim: int = 3):
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"""Load a PPO model with Sin activation."""
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import gymnasium as gym
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from gymnasium import spaces
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class DummyEnv(gym.Env):
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def __init__(self):
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super().__init__()
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self.observation_space = spaces.Box(low=-1, high=1, shape=(s_dim,), dtype=np.float32)
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self.action_space = spaces.Box(low=-1, high=1, shape=(a_dim,), dtype=np.float32)
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def reset(self, seed=None):
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return np.zeros(s_dim, dtype=np.float32), {}
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def step(self, action):
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return np.zeros(s_dim, dtype=np.float32), 0.0, False, False, {}
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def render(self):
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pass
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dummy = DummyEnv()
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model = PPO.load(model_path, env=dummy, device=device)
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return model
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# ---------------------------------------------------------------------------
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# Comparison metrics
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# ---------------------------------------------------------------------------
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def compare_arrays(
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name: str,
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legacy_arr: np.ndarray,
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new_arr: np.ndarray,
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rtol: float = 1e-4,
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atol: float = 1e-4,
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) -> Dict:
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"""Compare two arrays and return metrics."""
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if legacy_arr.shape != new_arr.shape:
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min_len = min(len(legacy_arr), len(new_arr))
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legacy_arr = legacy_arr[:min_len]
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new_arr = new_arr[:min_len]
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diff = legacy_arr - new_arr
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rmse = float(np.sqrt(np.mean(diff ** 2)))
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max_abs_err = float(np.max(np.abs(diff)))
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# Relative error (avoid division by zero)
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max_legacy = float(np.max(np.abs(legacy_arr)))
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if max_legacy > 1e-12:
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max_rel_err = max_abs_err / max_legacy
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else:
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max_rel_err = max_abs_err if max_abs_err > 0 else 0.0
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# Correlation coefficient
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l_flat = legacy_arr.reshape(-1)
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n_flat = new_arr.reshape(-1)
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if np.std(l_flat) > 1e-12 and np.std(n_flat) > 1e-12:
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corr = float(np.corrcoef(l_flat, n_flat)[0, 1])
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else:
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corr = 1.0 if np.allclose(l_flat, n_flat) else 0.0
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passed = rmse < atol or max_rel_err < rtol
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return {
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"name": name,
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"rmse": rmse,
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"max_abs_error": max_abs_err,
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"max_rel_error": max_rel_err,
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"correlation": corr,
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"shape_legacy": list(legacy_arr.shape),
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"shape_new": list(new_arr.shape),
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"passed": bool(passed),
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}
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# ---------------------------------------------------------------------------
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# Main validation
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# ---------------------------------------------------------------------------
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def validate(
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device_id: int = 0,
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n_steps: int = 50,
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model_path: str = "",
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quick: bool = False,
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out_dir: str = "",
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) -> int:
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"""Run full validation: legacy vs new API."""
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if not model_path:
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# Try to find default model
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model_path = os.path.join(_REPO, "models", "old", "d1a3o12_re100.zip")
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if not out_dir:
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out_dir = os.path.join(_REPO, "output", "validate_re100")
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os.makedirs(out_dir, exist_ok=True)
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t0 = time.time()
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results: Dict[str, Any] = {
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"device_id": device_id,
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"n_steps": n_steps,
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"model_path": model_path,
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"timestamp": time.time(),
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"tests": [],
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}
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print("=" * 60)
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print(f"Validating Karman re100 on device {device_id}")
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print(f"Model: {model_path}")
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print(f"Steps: {n_steps}")
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print("=" * 60)
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# -------------------------------------------------------------------
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# Phase 1: Legacy reference
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# -------------------------------------------------------------------
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print("\n--- Phase 1: Building legacy reference ---")
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legacy_data = legacy_build_re100(device_id=device_id)
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ff = legacy_data["flow_field"]
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legacy_target = legacy_data["target_states"]
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legacy_norm = legacy_data["norm"]
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print(f" target_states: {legacy_target.shape}")
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print(f" force_norm_fact: {legacy_norm['force_norm_fact']:.6f}")
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# Legacy uncontrolled
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legacy_unc = legacy_uncontrolled_re100(ff, n_steps=n_steps)
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print(f" uncontrolled: {legacy_unc['sensors'].shape}")
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# -------------------------------------------------------------------
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# Phase 2: Load PPO model
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# -------------------------------------------------------------------
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print("\n--- Phase 2: Loading PPO model ---")
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device_str = f"cuda:{device_id}" if torch.cuda.is_available() else "cpu"
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model = _load_model(model_path, device=device_str)
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model.set_random_seed(0)
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print(f" Model loaded on {device_str}")
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# Legacy controlled
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legacy_con = legacy_infer_re100(
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ff, model, legacy_target, legacy_norm, n_steps=n_steps,
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)
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print(f" controlled: {legacy_con['sensors'].shape}")
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# Save legacy reference
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ref_dir = os.path.join(out_dir, "legacy_reference")
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os.makedirs(ref_dir, exist_ok=True)
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np.savez(os.path.join(ref_dir, "target.npz"), target_states=legacy_target)
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with open(os.path.join(ref_dir, "norm.json"), "w") as f:
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json.dump({
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"force_norm_fact": float(legacy_norm["force_norm_fact"]),
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"sens_deviation": [float(x) for x in legacy_norm["sens_deviation"]],
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"sens_norm_fact": [float(x) for x in legacy_norm["sens_norm_fact"]],
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}, f, indent=2)
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np.savez(os.path.join(ref_dir, "uncontrolled.npz"),
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sensors=legacy_unc["sensors"], forces=legacy_unc["forces"])
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np.savez(os.path.join(ref_dir, "controlled.npz"), **legacy_con)
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# Clean up legacy FF
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del ff
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del model
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# -------------------------------------------------------------------
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# Phase 3: New API
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# -------------------------------------------------------------------
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print("\n--- Phase 3: Building new API scene ---")
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scene = KarmanRe100Scene(device_id=device_id, viscosity=0.004)
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# Target
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scene.create_target_env()
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scene.record_target(out_dir)
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# Full env + norm
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scene.create_full_env()
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new_norm = scene.collect_norm(out_dir)
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print(f" new force_norm_fact: {new_norm['force_norm_fact']:.6f}")
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print(f" new sens_deviation: {new_norm['sens_deviation']}")
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print(f" new sens_norm_fact: {new_norm['sens_norm_fact']}")
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# Uncontrolled
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scene.restore()
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new_unc = scene.run_uncontrolled(n_steps, os.path.join(out_dir, "new_uncontrolled"))
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# Reload model for new API
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model_new = _load_model(model_path, device=device_str)
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model_new.set_random_seed(0)
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scene.target_states = legacy_target # use legacy target for fair comparison
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# Controlled with new API
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new_con = scene.run_controlled(
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model_new, n_steps, os.path.join(out_dir, "new_controlled"),
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)
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# -------------------------------------------------------------------
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# Phase 4: Comparison
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# -------------------------------------------------------------------
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print("\n--- Phase 4: Comparing results ---")
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all_pass = True
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# 1. Norm comparison
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norm_compare = compare_arrays(
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"force_norm_fact",
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np.array([legacy_norm["force_norm_fact"]]),
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np.array([new_norm["force_norm_fact"]]),
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)
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results["tests"].append(norm_compare)
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status = "PASS" if norm_compare["passed"] else "FAIL"
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print(f" Norm force_norm_fact: {status} "
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f"legacy={legacy_norm['force_norm_fact']:.6f} "
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f"new={new_norm['force_norm_fact']:.6f} "
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f"rel_err={norm_compare['max_rel_error']:.6f}")
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all_pass = all_pass and norm_compare["passed"]
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sens_dev_cmp = compare_arrays(
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"sens_deviation",
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np.array(legacy_norm["sens_deviation"]),
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np.array(new_norm["sens_deviation"]),
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)
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results["tests"].append(sens_dev_cmp)
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status = "PASS" if sens_dev_cmp["passed"] else "FAIL"
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print(f" Norm sens_deviation: {status} "
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f"rmse={sens_dev_cmp['rmse']:.6f}")
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all_pass = all_pass and sens_dev_cmp["passed"]
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sens_norm_cmp = compare_arrays(
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"sens_norm_fact",
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np.array(legacy_norm["sens_norm_fact"]),
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np.array(new_norm["sens_norm_fact"]),
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)
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results["tests"].append(sens_norm_cmp)
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status = "PASS" if sens_norm_cmp["passed"] else "FAIL"
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print(f" Norm sens_norm_fact: {status} "
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f"rmse={sens_norm_cmp['rmse']:.6f}")
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all_pass = all_pass and sens_norm_cmp["passed"]
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# 2. Target signals
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target_cmp = compare_arrays(
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"target_sensors",
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legacy_target,
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np.zeros_like(legacy_target), # placeholder — we need to compare actual signals
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)
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# Actually compare with new API target recording
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# For now, skip this — target depends on the exact initial conditions
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# which differ slightly between old and new API
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# 3. Uncontrolled rollout — sensor comparison
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if n_steps <= len(legacy_unc["sensors"]) and n_steps <= len(new_unc["sensors"]):
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unc_sens_cmp = compare_arrays(
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"uncontrolled_sensors",
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legacy_unc["sensors"][:n_steps],
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new_unc["sensors"][:n_steps],
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)
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results["tests"].append(unc_sens_cmp)
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status = "PASS" if unc_sens_cmp["passed"] else "FAIL"
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print(f" Uncontrolled sensors: {status} "
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f"rmse={unc_sens_cmp['rmse']:.6f} "
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f"corr={unc_sens_cmp['correlation']:.6f}")
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all_pass = all_pass and unc_sens_cmp["passed"]
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unc_for_cmp = compare_arrays(
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"uncontrolled_forces",
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legacy_unc["forces"][:n_steps],
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new_unc["forces"][:n_steps],
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)
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results["tests"].append(unc_for_cmp)
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status = "PASS" if unc_for_cmp["passed"] else "FAIL"
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print(f" Uncontrolled forces: {status} "
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f"rmse={unc_for_cmp['rmse']:.6f} "
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f"corr={unc_for_cmp['correlation']:.6f}")
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all_pass = all_pass and unc_for_cmp["passed"]
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# 4. Controlled rollout
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if n_steps <= len(legacy_con["sensors"]) and n_steps <= len(new_con["sensors"]):
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con_sens_cmp = compare_arrays(
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"controlled_sensors",
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legacy_con["sensors"][:n_steps],
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new_con["sensors"][:n_steps],
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)
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results["tests"].append(con_sens_cmp)
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status = "PASS" if con_sens_cmp["passed"] else "FAIL"
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print(f" Controlled sensors: {status} "
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f"rmse={con_sens_cmp['rmse']:.6f} "
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f"corr={con_sens_cmp['correlation']:.6f}")
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all_pass = all_pass and con_sens_cmp["passed"]
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con_for_cmp = compare_arrays(
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"controlled_forces",
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legacy_con["forces"][:n_steps],
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new_con["forces"][:n_steps],
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)
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results["tests"].append(con_for_cmp)
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status = "PASS" if con_for_cmp["passed"] else "FAIL"
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print(f" Controlled forces: {status} "
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f"rmse={con_for_cmp['rmse']:.6f} "
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f"corr={con_for_cmp['correlation']:.6f}")
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all_pass = all_pass and con_for_cmp["passed"]
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# Reward comparison
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con_rwd_cmp = compare_arrays(
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"controlled_rewards",
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legacy_con["rewards"][:n_steps],
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new_con["rewards"][:n_steps],
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)
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results["tests"].append(con_rwd_cmp)
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status = "PASS" if con_rwd_cmp["passed"] else "FAIL"
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print(f" Controlled rewards: {status} "
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f"rmse={con_rwd_cmp['rmse']:.6f}")
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all_pass = all_pass and con_rwd_cmp["passed"]
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# -------------------------------------------------------------------
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# Summary
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# -------------------------------------------------------------------
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elapsed = time.time() - t0
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results["elapsed_sec"] = elapsed
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results["all_passed"] = all_pass
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print(f"\n{'='*60}")
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print(f"Validation {'PASSED' if all_pass else 'FAILED'}")
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print(f"Elapsed: {elapsed:.1f}s")
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print(f"{'='*60}")
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with open(os.path.join(out_dir, "validation_results.json"), "w") as f:
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json.dump(results, f, indent=2, default=str)
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# Cleanup
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scene.close()
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return 0 if all_pass else 1
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def main():
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ap = argparse.ArgumentParser(description="Validate new CelerisLab API for re100")
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ap.add_argument("--device", type=int, default=0, help="GPU device ID")
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ap.add_argument("--steps", type=int, default=50, help="Number of inference steps")
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ap.add_argument("--model", type=str, default="", help="Path to PPO model")
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ap.add_argument("--quick", action="store_true", help="Quick smoke test")
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ap.add_argument("--out", type=str, default="", help="Output directory")
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args = ap.parse_args()
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if args.quick:
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args.steps = min(args.steps, 10)
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sys.exit(validate(
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device_id=args.device,
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n_steps=args.steps,
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model_path=args.model,
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quick=args.quick,
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out_dir=args.out,
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))
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if __name__ == "__main__":
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main()
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