lerobot-clone/lerobot/scripts/eval.py

import threading
from pathlib import Path

import hydra
import imageio
import numpy as np
import torch
from tensordict.nn import TensorDictModule
from termcolor import colored
from torchrl.envs import EnvBase

from lerobot.common.envs.factory import make_env
from lerobot.common.policies.factory import make_policy
from lerobot.common.utils import set_seed


def write_video(video_path, stacked_frames, fps):
    imageio.mimsave(video_path, stacked_frames, fps=fps)


def eval_policy(
    env: EnvBase,
    policy: TensorDictModule = None,
    num_episodes: int = 10,
    max_steps: int = 30,
    save_video: bool = False,
    video_dir: Path = None,
    fps: int = 15,
    env_step: int = None,
    wandb=None,
):
    if wandb is not None:
        assert env_step is not None
    sum_rewards = []
    max_rewards = []
    successes = []
    threads = []
    for i in range(num_episodes):
        ep_frames = []

        def rendering_callback(env, td=None):
            ep_frames.append(env.render())

        tensordict = env.reset()
        if save_video or wandb:
            # render first frame before rollout
            rendering_callback(env)

        with torch.inference_mode():
            rollout = env.rollout(
                max_steps=max_steps,
                policy=policy,
                callback=rendering_callback if save_video or wandb else None,
                auto_reset=False,
                tensordict=tensordict,
                auto_cast_to_device=True,
            )
        # print(", ".join([f"{x:.3f}" for x in rollout["next", "reward"][:,0].tolist()]))
        ep_sum_reward = rollout["next", "reward"].sum()
        ep_max_reward = rollout["next", "reward"].max()
        ep_success = rollout["next", "success"].any()
        sum_rewards.append(ep_sum_reward.item())
        max_rewards.append(ep_max_reward.item())
        successes.append(ep_success.item())

        if save_video or wandb:
            stacked_frames = np.stack(ep_frames)

            if save_video:
                video_dir.mkdir(parents=True, exist_ok=True)
                video_path = video_dir / f"eval_episode_{i}.mp4"
                thread = threading.Thread(
                    target=write_video,
                    args=(str(video_path), stacked_frames, fps),
                )
                thread.start()
                threads.append(thread)

            first_episode = i == 0
            if wandb and first_episode:
                eval_video = wandb.Video(
                    stacked_frames.transpose(0, 3, 1, 2), fps=fps, format="mp4"
                )
                wandb.log({"eval_video": eval_video}, step=env_step)

    for thread in threads:
        thread.join()

    metrics = {
        "avg_sum_reward": np.nanmean(sum_rewards),
        "avg_max_reward": np.nanmean(max_rewards),
        "pc_success": np.nanmean(successes) * 100,
    }
    return metrics


@hydra.main(version_base=None, config_name="default", config_path="../configs")
def eval_cli(cfg: dict):
    eval(cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().runtime.output_dir)


def eval(cfg: dict, out_dir=None):
    if out_dir is None:
        raise NotImplementedError()

    assert torch.cuda.is_available()
    torch.backends.cudnn.benchmark = True
    set_seed(cfg.seed)
    print(colored("Log dir:", "yellow", attrs=["bold"]), out_dir)

    env = make_env(cfg)

    if cfg.pretrained_model_path:
        policy = make_policy(cfg)
        policy = TensorDictModule(
            policy,
            in_keys=["observation", "step_count"],
            out_keys=["action"],
        )
    else:
        # when policy is None, rollout a random policy
        policy = None

    metrics = eval_policy(
        env,
        policy=policy,
        save_video=True,
        video_dir=Path(out_dir) / "eval",
        fps=cfg.fps,
        max_steps=cfg.episode_length,
        num_episodes=cfg.eval_episodes,
    )
    print(metrics)


if __name__ == "__main__":
    eval_cli()