"""Rollout evaluation for the Task-E ACT policy checkpoint.""" import argparse import os import sys import time from datetime import datetime from isaaclab.app import AppLauncher parser = argparse.ArgumentParser(description="Evaluate ACT checkpoint on ATEC Task E.") parser.add_argument( "--checkpoint", type=str, default="runs/act-task-e-rgb-100demos-seed1/checkpoints/best_loss.pt", help="ACT checkpoint path.", ) parser.add_argument("--task", type=str, default="ATEC-TaskE-Piper") parser.add_argument("--episodes", type=int, default=3) parser.add_argument("--max_steps", type=int, default=1500) parser.add_argument("--video_path", type=str, default=None, help="Optional MP4 output path for episode 1.") parser.add_argument("--video_interval", type=int, default=2, help="Record every N env steps.") parser.add_argument("--video_fps", type=int, default=25) parser.add_argument("--seed", type=int, default=None) parser.add_argument("--disable_fabric", action="store_true", default=False) parser.add_argument("--debug", action="store_true", default=False) parser.add_argument( "--solution_module", type=str, default="solution_act", help="Module under demo/ that provides AlgSolution, e.g. solution_act or solution_pca.", ) AppLauncher.add_app_launcher_args(parser) args_cli = parser.parse_args() args_cli.enable_cameras = True repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")) checkpoint = os.path.abspath(args_cli.checkpoint) os.environ["ATEC_ACT_POLICY_PATH"] = checkpoint app_launcher = AppLauncher(args_cli) simulation_app = app_launcher.app import gymnasium as gym # noqa: E402 import torch # noqa: E402 from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent # noqa: E402 from isaaclab_tasks.utils import parse_env_cfg # noqa: E402 import atec_rl_lab.tasks # noqa: F401, E402 demo_dir = os.path.join(repo_root, "demo") if repo_root not in sys.path: sys.path.insert(0, repo_root) if demo_dir not in sys.path: sys.path.insert(0, demo_dir) from scripts.act.task_e.collector import basket_status_lines # noqa: E402 import importlib # noqa: E402 AlgSolution = importlib.import_module(args_cli.solution_module).AlgSolution def _frame_from_obs(obs) -> object: rgb = obs["image"]["video_rgb"] if isinstance(rgb, torch.Tensor): frame = rgb[0].detach().cpu() if frame.ndim == 3 and frame.shape[0] in (3, 4): frame = frame.permute(1, 2, 0) if frame.shape[-1] == 4: frame = frame[..., :3] if frame.dtype != torch.uint8: frame = (frame.float() * 255.0).clamp(0, 255).to(torch.uint8) return frame.numpy() return rgb[0] def _resolve_video_path() -> str | None: if args_cli.video_path is None: return None if args_cli.video_path: return os.path.abspath(args_cli.video_path) stamp = datetime.now().strftime("%Y%m%d_%H%M%S") return os.path.join(repo_root, "logs", "videos", "task_e_act_eval", f"eval_{stamp}.mp4") def evaluate() -> list[dict[str, float]]: if not os.path.exists(checkpoint): raise FileNotFoundError(f"Checkpoint not found: {checkpoint}") env_cfg = parse_env_cfg( args_cli.task, device=args_cli.device, num_envs=1, use_fabric=not args_cli.disable_fabric, ) if args_cli.seed is not None: env_cfg.seed = args_cli.seed env = gym.make(args_cli.task, cfg=env_cfg) if isinstance(env.unwrapped, DirectMARLEnv): env = multi_agent_to_single_agent(env) policy = AlgSolution() video_path = _resolve_video_path() writer = None if video_path is not None: import imageio.v2 as imageio os.makedirs(os.path.dirname(video_path), exist_ok=True) writer = imageio.get_writer(video_path, fps=args_cli.video_fps, quality=7) print(f"[INFO] Recording episode 1 video to: {video_path}") results = [] try: for episode in range(args_cli.episodes): reset_kwargs = {"seed": args_cli.seed + episode} if args_cli.seed is not None else {} obs, _ = env.reset(**reset_kwargs) policy.reset_episode() total_reward = 0.0 elapsed_time = 0.0 steps = 0 done = False start_wall = time.time() if writer is not None and episode == 0: writer.append_data(_frame_from_obs(obs)) while simulation_app.is_running() and steps < args_cli.max_steps: with torch.inference_mode(): resp = policy.predicts(obs, total_reward) if resp["giveup"]: break action = torch.as_tensor(resp["action"], dtype=torch.float32, device=args_cli.device).view(1, -1) obs, reward, terminated, truncated, info = env.step(action) sim_dt = info["Step_dt"] total_reward += reward.mean().item() / sim_dt if isinstance(reward, torch.Tensor) else float(reward) / sim_dt if isinstance(info, dict) and "Elapsed_Time" in info: elapsed = info["Elapsed_Time"] elapsed_time = elapsed.item() if hasattr(elapsed, "item") else float(elapsed) else: elapsed_time += env.unwrapped.step_dt done = bool(terminated.item() or truncated.item()) steps += 1 if writer is not None and episode == 0 and steps % max(1, args_cli.video_interval) == 0: writer.append_data(_frame_from_obs(obs)) if args_cli.debug and steps % 100 == 0: print(f"[DEBUG] episode={episode + 1} step={steps} score={total_reward:.2f}") if done: break result = { "episode": episode + 1, "score": float(total_reward), "elapsed_time": float(elapsed_time), "steps": float(steps), "done": float(done), "wall_time": time.time() - start_wall, } results.append(result) try: for line in basket_status_lines(env, [1, 2, 3]): print(f"[BASKET] episode={episode + 1} {line}") except Exception as exc: if args_cli.debug: print(f"[DEBUG] basket status unavailable: {exc}") print( "[RESULT] " f"episode={result['episode']:.0f} " f"score={result['score']:.2f} " f"elapsed_time={result['elapsed_time']:.2f} " f"steps={result['steps']:.0f} " f"done={bool(result['done'])} " f"wall_time={result['wall_time']:.1f}s" ) finally: if writer is not None: writer.close() env.close() return results if __name__ == "__main__": try: results = evaluate() if results: scores = torch.tensor([r["score"] for r in results], dtype=torch.float32) print(f"[SUMMARY] episodes={len(results)} mean_score={scores.mean().item():.2f} best_score={scores.max().item():.2f}") finally: simulation_app.close()