9d002032d1
WIP Aloha env tests pass Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1]) Update README Copy past from act repo Remove download.py add a WIP for Simxarm Remove download.py add a WIP for Simxarm Add act yaml (TODO: try train.py) Training can runs (TODO: eval) Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model) Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm) poetry lock fix bug in compute_stats for action normalization fix more bugs in normalization fix training fix import PushtEnv inheriates AbstractEnv, Improve factory Normalization Add _make_env to EnvAbstract Add call_rendering_hooks to pusht env SimxarmEnv inherites from AbstractEnv (NOT TESTED) Add aloha tests artifacts + update pusht stats fix image normalization: before env was in [0,1] but dataset in [0,255], and now both in [0,255] Small fix on simxarm Add next to obs Add top camera to Aloha env (TODO: make it compatible with set of cameras) Add top camera to Aloha env (TODO: make it compatible with set of cameras)
151 lines
4.2 KiB
Python
151 lines
4.2 KiB
Python
import logging
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import threading
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import time
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from pathlib import Path
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import hydra
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import imageio
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import numpy as np
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import torch
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import tqdm
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from tensordict.nn import TensorDictModule
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from torchrl.envs import EnvBase
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from lerobot.common.datasets.factory import make_offline_buffer
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from lerobot.common.envs.factory import make_env
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from lerobot.common.logger import log_output_dir
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from lerobot.common.policies.factory import make_policy
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from lerobot.common.utils import init_logging, set_seed
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def write_video(video_path, stacked_frames, fps):
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imageio.mimsave(video_path, stacked_frames, fps=fps)
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def eval_policy(
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env: EnvBase,
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policy: TensorDictModule = None,
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num_episodes: int = 10,
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max_steps: int = 30,
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save_video: bool = False,
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video_dir: Path = None,
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fps: int = 15,
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return_first_video: bool = False,
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):
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start = time.time()
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sum_rewards = []
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max_rewards = []
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successes = []
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threads = []
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for i in tqdm.tqdm(range(num_episodes)):
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ep_frames = []
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if save_video or (return_first_video and i == 0):
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def render_frame(env):
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ep_frames.append(env.render()) # noqa: B023
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env.register_rendering_hook(render_frame)
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with torch.inference_mode():
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rollout = env.rollout(
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max_steps=max_steps,
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policy=policy,
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auto_cast_to_device=True,
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)
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# print(", ".join([f"{x:.3f}" for x in rollout["next", "reward"][:,0].tolist()]))
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ep_sum_reward = rollout["next", "reward"].sum()
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ep_max_reward = rollout["next", "reward"].max()
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ep_success = rollout["next", "success"].any()
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sum_rewards.append(ep_sum_reward.item())
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max_rewards.append(ep_max_reward.item())
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successes.append(ep_success.item())
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if save_video or (return_first_video and i == 0):
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stacked_frames = np.stack(ep_frames)
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if save_video:
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video_dir.mkdir(parents=True, exist_ok=True)
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video_path = video_dir / f"eval_episode_{i}.mp4"
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thread = threading.Thread(
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target=write_video,
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args=(str(video_path), stacked_frames, fps),
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)
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thread.start()
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threads.append(thread)
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if return_first_video and i == 0:
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first_video = stacked_frames.transpose(0, 3, 1, 2)
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env.reset_rendering_hooks()
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for thread in threads:
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thread.join()
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info = {
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"avg_sum_reward": np.nanmean(sum_rewards),
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"avg_max_reward": np.nanmean(max_rewards),
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"pc_success": np.nanmean(successes) * 100,
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"eval_s": time.time() - start,
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"eval_ep_s": (time.time() - start) / num_episodes,
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}
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if return_first_video:
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return info, first_video
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return info
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@hydra.main(version_base=None, config_name="default", config_path="../configs")
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def eval_cli(cfg: dict):
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eval(cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().runtime.output_dir)
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def eval(cfg: dict, out_dir=None):
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if out_dir is None:
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raise NotImplementedError()
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init_logging()
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if cfg.device == "cuda":
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assert torch.cuda.is_available()
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else:
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logging.warning("Using CPU, this will be slow.")
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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set_seed(cfg.seed)
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log_output_dir(out_dir)
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logging.info("make_offline_buffer")
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offline_buffer = make_offline_buffer(cfg)
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logging.info("make_env")
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env = make_env(cfg, transform=offline_buffer.transform)
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if cfg.policy.pretrained_model_path:
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policy = make_policy(cfg)
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policy = TensorDictModule(
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policy,
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in_keys=["observation", "step_count"],
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out_keys=["action"],
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)
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else:
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# when policy is None, rollout a random policy
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policy = None
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metrics = eval_policy(
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env,
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policy=policy,
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save_video=True,
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video_dir=Path(out_dir) / "eval",
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fps=cfg.env.fps,
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max_steps=cfg.env.episode_length // cfg.n_action_steps,
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num_episodes=cfg.eval_episodes,
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)
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print(metrics)
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logging.info("End of eval")
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if __name__ == "__main__":
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eval_cli()
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