forked from tangger/lerobot
Add common, refactor eval with eval_policy
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Cadene
0
lerobot/common/envs/__init__.py
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lerobot/common/envs/__init__.py
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lerobot/common/envs/factory.py
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lerobot/common/envs/factory.py
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from torchrl.envs.transforms import StepCounter, TransformedEnv
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from lerobot.common.envs.simxarm import SimxarmEnv
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def make_env(cfg):
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assert cfg.env == "simxarm"
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env = SimxarmEnv(
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task=cfg.task,
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from_pixels=cfg.from_pixels,
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pixels_only=cfg.pixels_only,
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image_size=cfg.image_size,
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)
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# limit rollout to max_steps
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env = TransformedEnv(env, StepCounter(max_steps=cfg.episode_length))
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return env
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# def make_env(env_name, frame_skip, device, is_test=False):
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# env = GymEnv(
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# env_name,
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# frame_skip=frame_skip,
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# from_pixels=True,
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# pixels_only=False,
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# device=device,
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# )
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# env = TransformedEnv(env)
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# env.append_transform(NoopResetEnv(noops=30, random=True))
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# if not is_test:
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# env.append_transform(EndOfLifeTransform())
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# env.append_transform(RewardClipping(-1, 1))
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# env.append_transform(ToTensorImage())
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# env.append_transform(GrayScale())
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# env.append_transform(Resize(84, 84))
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# env.append_transform(CatFrames(N=4, dim=-3))
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# env.append_transform(RewardSum())
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# env.append_transform(StepCounter(max_steps=4500))
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# env.append_transform(DoubleToFloat())
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# env.append_transform(VecNorm(in_keys=["pixels"]))
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# return env
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183
lerobot/common/envs/simxarm.py
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lerobot/common/envs/simxarm.py
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import importlib
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from typing import Optional
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import numpy as np
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import torch
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from tensordict import TensorDict
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from torchrl.data.tensor_specs import (
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BoundedTensorSpec,
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CompositeSpec,
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DiscreteTensorSpec,
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UnboundedContinuousTensorSpec,
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)
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from torchrl.envs import EnvBase
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from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
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from lerobot.common.utils import set_seed
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_has_gym = importlib.util.find_spec("gym") is not None
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_has_simxarm = importlib.util.find_spec("simxarm") is not None and _has_gym
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class SimxarmEnv(EnvBase):
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def __init__(
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self,
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task,
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from_pixels: bool = False,
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pixels_only: bool = False,
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image_size=None,
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seed=1337,
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device="cpu",
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):
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super().__init__(device=device, batch_size=[])
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self.task = task
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self.from_pixels = from_pixels
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self.pixels_only = pixels_only
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self.image_size = image_size
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if pixels_only:
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assert from_pixels
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if from_pixels:
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assert image_size
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if not _has_simxarm:
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raise ImportError("Cannot import simxarm.")
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if not _has_gym:
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raise ImportError("Cannot import gym.")
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import gym
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from gym.wrappers import TimeLimit
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from simxarm import TASKS
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if self.task not in TASKS:
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raise ValueError(
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f"Unknown task {self.task}. Must be one of {list(TASKS.keys())}"
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)
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self._env = TASKS[self.task]["env"]()
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self._env = TimeLimit(self._env, TASKS[self.task]["episode_length"])
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MAX_NUM_ACTIONS = 4
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num_actions = len(TASKS[self.task]["action_space"])
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self._action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
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self._action_padding = np.zeros(
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(MAX_NUM_ACTIONS - num_actions), dtype=np.float32
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)
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if "w" not in TASKS[self.task]["action_space"]:
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self._action_padding[-1] = 1.0
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self._make_spec()
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self.set_seed(seed)
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def render(self, mode="rgb_array", width=384, height=384):
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return self._env.render(mode, width=width, height=height)
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def _format_raw_obs(self, raw_obs):
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if self.from_pixels:
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camera = self.render(
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mode="rgb_array", width=self.image_size, height=self.image_size
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)
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camera = camera.transpose(2, 0, 1) # (H, W, C) -> (C, H, W)
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camera = torch.tensor(camera.copy(), dtype=torch.uint8)
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obs = {"camera": camera}
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if not self.pixels_only:
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obs["robot_state"] = torch.tensor(
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self._env.robot_state, dtype=torch.float32
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)
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else:
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obs = {"state": torch.tensor(raw_obs["observation"], dtype=torch.float32)}
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obs = TensorDict(obs, batch_size=[])
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return obs
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def _reset(self, tensordict: Optional[TensorDict] = None):
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td = tensordict
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if td is None or td.is_empty():
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raw_obs = self._env.reset()
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td = TensorDict(
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{
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"observation": self._format_raw_obs(raw_obs),
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"done": torch.tensor([False], dtype=torch.bool),
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},
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batch_size=[],
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)
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else:
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raise NotImplementedError()
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return td
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def _step(self, tensordict: TensorDict):
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td = tensordict
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action = td["action"].numpy()
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# step expects shape=(4,) so we pad if necessary
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action = np.concatenate([action, self._action_padding])
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# TODO(rcadene): add info["is_success"] and info["success"] ?
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raw_obs, reward, done, info = self._env.step(action)
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td = TensorDict(
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{
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"observation": self._format_raw_obs(raw_obs),
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"reward": torch.tensor([reward], dtype=torch.float32),
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"done": torch.tensor([done], dtype=torch.bool),
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"success": torch.tensor([info["success"]], dtype=torch.bool),
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},
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batch_size=[],
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)
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return td
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def _make_spec(self):
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obs = {}
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if self.from_pixels:
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obs["camera"] = BoundedTensorSpec(
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low=0,
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high=255,
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shape=(3, self.image_size, self.image_size),
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dtype=torch.uint8,
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device=self.device,
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)
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if not self.pixels_only:
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obs["robot_state"] = UnboundedContinuousTensorSpec(
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shape=(len(self._env.robot_state),),
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dtype=torch.float32,
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device=self.device,
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)
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else:
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# TODO(rcadene): add observation_space achieved_goal and desired_goal?
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obs["state"] = UnboundedContinuousTensorSpec(
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shape=self._env.observation_space["observation"].shape,
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dtype=torch.float32,
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device=self.device,
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)
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self.observation_spec = CompositeSpec({"observation": obs})
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self.action_spec = _gym_to_torchrl_spec_transform(
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self._action_space,
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device=self.device,
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)
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self.reward_spec = UnboundedContinuousTensorSpec(
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shape=(1,),
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dtype=torch.float32,
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device=self.device,
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)
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self.done_spec = DiscreteTensorSpec(
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2,
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shape=(1,),
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dtype=torch.bool,
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device=self.device,
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)
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self.success_spec = DiscreteTensorSpec(
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2,
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shape=(1,),
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dtype=torch.bool,
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device=self.device,
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)
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def _set_seed(self, seed: Optional[int]):
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set_seed(seed)
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self._env.seed(seed)
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