File size: 7,290 Bytes
21e1acb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | """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()
|