Integrate pusht env from diffusion
This commit is contained in:
Simon Alibert
239
lerobot/common/envs/pusht/env.py
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239
lerobot/common/envs/pusht/env.py
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import importlib
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from collections import deque
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from typing import Optional
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import einops
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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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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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num_prev_obs=0,
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num_prev_action=0,
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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.num_prev_obs = num_prev_obs
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self.num_prev_action = num_prev_action
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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_gym:
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raise ImportError("Cannot import gym.")
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# TODO(rcadene) (PushTEnv is similar to PushTImageEnv, but without the image rendering, it's faster to iterate on)
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# from lerobot.common.envs.pusht.pusht_env import PushTEnv
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if not from_pixels:
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raise NotImplementedError("Use PushTEnv, instead of PushTImageEnv")
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from lerobot.common.envs.pusht.pusht_image_env import PushTImageEnv
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self._env = PushTImageEnv(render_size=self.image_size)
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self._make_spec()
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self._current_seed = self.set_seed(seed)
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if self.num_prev_obs > 0:
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self._prev_obs_image_queue = deque(maxlen=self.num_prev_obs)
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self._prev_obs_state_queue = deque(maxlen=self.num_prev_obs)
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if self.num_prev_action > 0:
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raise NotImplementedError()
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# self._prev_action_queue = deque(maxlen=self.num_prev_action)
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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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image = torch.from_numpy(raw_obs["image"])
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obs = {"image": image}
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if not self.pixels_only:
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obs["state"] = torch.from_numpy(raw_obs["agent_pos"]).type(torch.float32)
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else:
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# TODO:
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obs = {"state": torch.from_numpy(raw_obs["observation"]).type(torch.float32)}
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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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# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
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self._current_seed += 1
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self.set_seed(self._current_seed)
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raw_obs = self._env.reset()
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assert self._current_seed == self._env._seed
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obs = self._format_raw_obs(raw_obs)
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if self.num_prev_obs > 0:
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stacked_obs = {}
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if "image" in obs:
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self._prev_obs_image_queue = deque(
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[obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
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)
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stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
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if "state" in obs:
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self._prev_obs_state_queue = deque(
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[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
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)
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stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
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obs = stacked_obs
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td = TensorDict(
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{
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"observation": TensorDict(obs, batch_size=[]),
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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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if action.ndim == 1:
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action = einops.repeat(action, "c -> t c", t=self.frame_skip)
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else:
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if self.frame_skip > 1:
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raise NotImplementedError()
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num_action_steps = action.shape[0]
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for i in range(num_action_steps):
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raw_obs, reward, done, info = self._env.step(action[i])
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sum_reward += reward
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obs = self._format_raw_obs(raw_obs)
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if self.num_prev_obs > 0:
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stacked_obs = {}
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if "image" in obs:
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self._prev_obs_image_queue.append(obs["image"])
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stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
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if "state" in obs:
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self._prev_obs_state_queue.append(obs["state"])
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stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
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obs = stacked_obs
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td = TensorDict(
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{
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"observation": TensorDict(obs, batch_size=[]),
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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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image_shape = (3, self.image_size, self.image_size)
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if self.num_prev_obs > 0:
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image_shape = (self.num_prev_obs + 1, *image_shape)
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obs["image"] = BoundedTensorSpec(
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low=0,
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high=1,
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shape=image_shape,
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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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state_shape = self._env.observation_space["agent_pos"].shape
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if self.num_prev_obs > 0:
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state_shape = (self.num_prev_obs + 1, *state_shape)
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obs["state"] = BoundedTensorSpec(
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low=0,
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high=512,
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shape=state_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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state_shape = self._env.observation_space["observation"].shape
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if self.num_prev_obs > 0:
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state_shape = (self.num_prev_obs + 1, *state_shape)
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obs["state"] = UnboundedContinuousTensorSpec(
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# TODO:
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shape=state_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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