Fix unit tests, Refactor, Add pusht env, (TODO pusht replay buffer, image preprocessing)
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Cadene
193
lerobot/common/envs/pusht.py
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193
lerobot/common/envs/pusht.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_diffpolicy = importlib.util.find_spec("diffusion_policy") is not None and _has_gym
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class PushtEnv(EnvBase):
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def __init__(
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self,
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frame_skip: int = 1,
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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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max_episode_length=25, # TODO: verify
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):
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super().__init__(device=device, batch_size=[])
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self.frame_skip = frame_skip
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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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self.max_episode_length = max_episode_length
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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_diffpolicy:
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raise ImportError("Cannot import diffusion_policy.")
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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 diffusion_policy.env.pusht.pusht_env import PushTEnv
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from diffusion_policy.env.pusht.pusht_image_env import PushTImageEnv
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from gym.wrappers import TimeLimit
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self._env = PushTImageEnv(render_size=self.image_size)
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self._env = TimeLimit(self._env, self.max_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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if width != height:
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raise NotImplementedError()
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tmp = self._env.render_size
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self._env.render_size = width
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out = self._env.render(mode)
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self._env.render_size = tmp
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return out
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def _format_raw_obs(self, raw_obs):
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if self.from_pixels:
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obs = {"image": torch.from_numpy(raw_obs["image"])}
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if not self.pixels_only:
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obs["state"] = torch.from_numpy(raw_obs["agent_pos"]).type(
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torch.float32
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)
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else:
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# TODO:
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obs = {
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"state": torch.from_numpy(raw_obs["observation"]).type(torch.float32)
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}
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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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# TODO(rcadene): add info["is_success"] and info["success"] ?
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sum_reward = 0
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for t in range(self.frame_skip):
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raw_obs, reward, done, info = self._env.step(action)
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sum_reward += reward
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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([sum_reward], dtype=torch.float32),
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# succes and done are true when coverage > self.success_threshold in env
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"done": torch.tensor([done], dtype=torch.bool),
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"success": torch.tensor([done], 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["image"] = BoundedTensorSpec(
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low=0,
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high=1,
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shape=(3, self.image_size, self.image_size),
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dtype=torch.float32,
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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["state"] = BoundedTensorSpec(
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low=0,
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high=512,
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shape=self._env.observation_space["agent_pos"].shape,
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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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# TODO:
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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._env.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 = CompositeSpec(
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{
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"done": 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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"success": 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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}
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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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