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- eval_kit/core: 零框架依赖的指标内核（Trace/metrics/aggregation/terrain_spec/report/naming） - eval_kit/isaaclab: 适配层（env_cfg 改造、rollout 采集、checkpoint 加载） - eval_kit/profiles.py / protocols.py: 机器人形态描述与评估口径预设 - eval_kit/tests: 24 项内核自检 + 21 项命令行自检（都不需要仿真） - eval_suite.py: 编排入口，每个（阶段, 种子）一个子进程；与 eval.py 指标口径一致 - WebRTC 可视化：--livestream 透传、视口相机跟随/自由（--view-env / --free-camera）、 连接前停顿（--pause-s）、自动放开客户端改画幅、打印连接地址 - 诊断与替代路径：rollout 进度打印、--record-frames 抓帧
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
# Copyright (c) 2025 Deep Robotics
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# SPDX-License-Identifier: BSD 3-Clause
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"""checkpoint 解析与策略加载（IsaacLab + RSL-RL）。"""
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from __future__ import annotations
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import os
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from typing import Any
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import gymnasium as gym
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from isaaclab.envs import (
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DirectMARLEnv,
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DirectMARLEnvCfg,
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DirectRLEnvCfg,
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ManagerBasedRLEnvCfg,
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multi_agent_to_single_agent,
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)
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from isaaclab_tasks.utils import get_checkpoint_path
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from isaaclab_rl.rsl_rl import RslRlVecEnvWrapper
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def resolve_checkpoint(
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log_root_path: str,
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load_run: str | None = None,
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checkpoint: str | None = None,
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) -> str:
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"""解析出 checkpoint 的绝对路径。
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允许 ``load_run`` / ``checkpoint`` 传绝对路径或带目录的相对路径，从而跨实验目录
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取权重（例如从 Stage 1 的目录里取权重来评估），否则回退到 IsaacLab 的按名查找。
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"""
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if checkpoint is not None:
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checkpoint_path = os.path.expanduser(checkpoint)
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if os.path.isabs(checkpoint_path) or os.path.dirname(checkpoint_path):
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checkpoint_path = os.path.abspath(checkpoint_path)
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if not os.path.isfile(checkpoint_path):
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raise FileNotFoundError(f"找不到 checkpoint：{checkpoint_path}")
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return checkpoint_path
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if load_run is not None:
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run_path = os.path.expanduser(load_run)
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if os.path.isabs(run_path) or os.path.dirname(run_path):
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run_path = os.path.abspath(run_path)
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if not os.path.isdir(run_path):
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raise FileNotFoundError(f"找不到运行目录：{run_path}")
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return get_checkpoint_path(os.path.dirname(run_path), os.path.basename(run_path), checkpoint)
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return get_checkpoint_path(log_root_path, load_run, checkpoint)
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def make_env(
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task: str,
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env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg,
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clip_actions: float | None = None,
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):
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"""按已配置好的 ``env_cfg`` 建环境，并包成 RSL-RL 的向量环境。"""
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env = gym.make(task, cfg=env_cfg)
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if isinstance(env.unwrapped, DirectMARLEnv):
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env = multi_agent_to_single_agent(env)
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return RslRlVecEnvWrapper(env, clip_actions=clip_actions)
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def load_policy(env, agent_cfg: Any, resume_path: str, device: str | None = None):
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"""加载 checkpoint 并返回推理策略。"""
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from rsl_rl.runners import OnPolicyRunner
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runner = OnPolicyRunner(env, agent_cfg.to_dict(), log_dir=None, device=device or agent_cfg.device)
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runner.load(resume_path)
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return runner.get_inference_policy(device=device or env.unwrapped.device)
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