244 lines
8.1 KiB
Python
244 lines
8.1 KiB
Python
import importlib
|
|
import logging
|
|
from collections import deque
|
|
from typing import Optional
|
|
|
|
import cv2
|
|
import numpy as np
|
|
import torch
|
|
from tensordict import TensorDict
|
|
from torchrl.data.tensor_specs import (
|
|
BoundedTensorSpec,
|
|
CompositeSpec,
|
|
DiscreteTensorSpec,
|
|
UnboundedContinuousTensorSpec,
|
|
)
|
|
from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
|
|
|
|
from lerobot.common.envs.abstract import AbstractEnv
|
|
from lerobot.common.utils import set_seed
|
|
|
|
_has_gym = importlib.util.find_spec("gym") is not None
|
|
|
|
|
|
class PushtEnv(AbstractEnv):
|
|
_reset_warning_issued = False
|
|
|
|
def __init__(
|
|
self,
|
|
task="pusht",
|
|
frame_skip: int = 1,
|
|
from_pixels: bool = False,
|
|
pixels_only: bool = False,
|
|
image_size=None,
|
|
seed=1337,
|
|
device="cpu",
|
|
num_prev_obs=1,
|
|
num_prev_action=0,
|
|
):
|
|
super().__init__(
|
|
task=task,
|
|
frame_skip=frame_skip,
|
|
from_pixels=from_pixels,
|
|
pixels_only=pixels_only,
|
|
image_size=image_size,
|
|
seed=seed,
|
|
device=device,
|
|
num_prev_obs=num_prev_obs,
|
|
num_prev_action=num_prev_action,
|
|
)
|
|
|
|
def _make_env(self):
|
|
if not _has_gym:
|
|
raise ImportError("Cannot import gym.")
|
|
|
|
# TODO(rcadene) (PushTEnv is similar to PushTImageEnv, but without the image rendering, it's faster to iterate on)
|
|
# from lerobot.common.envs.pusht.pusht_env import PushTEnv
|
|
|
|
if not self.from_pixels:
|
|
raise NotImplementedError("Use PushTEnv, instead of PushTImageEnv")
|
|
from lerobot.common.envs.pusht.pusht_image_env import PushTImageEnv
|
|
|
|
self._env = PushTImageEnv(render_size=self.image_size)
|
|
|
|
def render(self, mode="rgb_array", width=96, height=96, with_marker=True):
|
|
"""
|
|
with_marker adds a cursor showing the targeted action for the controller.
|
|
"""
|
|
if width != height:
|
|
raise NotImplementedError()
|
|
tmp = self._env.render_size
|
|
if width != self._env.render_size:
|
|
self._env.render_cache = None
|
|
self._env.render_size = width
|
|
out = self._env.render(mode).copy()
|
|
if with_marker and self._env.latest_action is not None:
|
|
action = np.array(self._env.latest_action)
|
|
coord = (action / 512 * self._env.render_size).astype(np.int32)
|
|
marker_size = int(8 / 96 * self._env.render_size)
|
|
thickness = int(1 / 96 * self._env.render_size)
|
|
cv2.drawMarker(
|
|
out,
|
|
coord,
|
|
color=(255, 0, 0),
|
|
markerType=cv2.MARKER_CROSS,
|
|
markerSize=marker_size,
|
|
thickness=thickness,
|
|
)
|
|
self._env.render_size = tmp
|
|
return out
|
|
|
|
def _format_raw_obs(self, raw_obs):
|
|
if self.from_pixels:
|
|
image = torch.from_numpy(raw_obs["image"])
|
|
obs = {"image": image}
|
|
|
|
if not self.pixels_only:
|
|
obs["state"] = torch.from_numpy(raw_obs["agent_pos"]).type(torch.float32)
|
|
else:
|
|
# TODO:
|
|
obs = {"state": torch.from_numpy(raw_obs["observation"]).type(torch.float32)}
|
|
|
|
return obs
|
|
|
|
def _reset(self, tensordict: Optional[TensorDict] = None):
|
|
if tensordict is not None and not PushtEnv._reset_warning_issued:
|
|
logging.warning(f"{self.__class__.__name__}._reset ignores the provided tensordict.")
|
|
PushtEnv._reset_warning_issued = True
|
|
|
|
# we need to handle seed iteration, since self._env.reset() rely an internal _seed.
|
|
self._current_seed += 1
|
|
self.set_seed(self._current_seed)
|
|
raw_obs = self._env.reset()
|
|
assert self._current_seed == self._env._seed
|
|
|
|
obs = self._format_raw_obs(raw_obs)
|
|
|
|
if self.num_prev_obs > 0:
|
|
stacked_obs = {}
|
|
if "image" in obs:
|
|
self._prev_obs_image_queue = deque(
|
|
[obs["image"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
|
|
)
|
|
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
|
|
if "state" in obs:
|
|
self._prev_obs_state_queue = deque(
|
|
[obs["state"]] * (self.num_prev_obs + 1), maxlen=(self.num_prev_obs + 1)
|
|
)
|
|
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
|
|
obs = stacked_obs
|
|
|
|
td = TensorDict(
|
|
{
|
|
"observation": TensorDict(obs, batch_size=[]),
|
|
"done": torch.tensor([False], dtype=torch.bool),
|
|
},
|
|
batch_size=[],
|
|
)
|
|
|
|
return td
|
|
|
|
def _step(self, tensordict: TensorDict):
|
|
td = tensordict
|
|
action = td["action"].numpy()
|
|
assert action.ndim == 1
|
|
# TODO(rcadene): add info["is_success"] and info["success"] ?
|
|
|
|
raw_obs, reward, done, info = self._env.step(action)
|
|
|
|
obs = self._format_raw_obs(raw_obs)
|
|
|
|
if self.num_prev_obs > 0:
|
|
stacked_obs = {}
|
|
if "image" in obs:
|
|
self._prev_obs_image_queue.append(obs["image"])
|
|
stacked_obs["image"] = torch.stack(list(self._prev_obs_image_queue))
|
|
if "state" in obs:
|
|
self._prev_obs_state_queue.append(obs["state"])
|
|
stacked_obs["state"] = torch.stack(list(self._prev_obs_state_queue))
|
|
obs = stacked_obs
|
|
|
|
td = TensorDict(
|
|
{
|
|
"observation": TensorDict(obs, batch_size=[]),
|
|
"reward": torch.tensor([reward], dtype=torch.float32),
|
|
# success and done are true when coverage > self.success_threshold in env
|
|
"done": torch.tensor([done], dtype=torch.bool),
|
|
"success": torch.tensor([done], dtype=torch.bool),
|
|
},
|
|
batch_size=[],
|
|
)
|
|
return td
|
|
|
|
def _make_spec(self):
|
|
obs = {}
|
|
if self.from_pixels:
|
|
image_shape = (3, self.image_size, self.image_size)
|
|
if self.num_prev_obs > 0:
|
|
image_shape = (self.num_prev_obs + 1, *image_shape)
|
|
|
|
obs["image"] = BoundedTensorSpec(
|
|
low=0,
|
|
high=255,
|
|
shape=image_shape,
|
|
dtype=torch.uint8,
|
|
device=self.device,
|
|
)
|
|
if not self.pixels_only:
|
|
state_shape = self._env.observation_space["agent_pos"].shape
|
|
if self.num_prev_obs > 0:
|
|
state_shape = (self.num_prev_obs + 1, *state_shape)
|
|
|
|
obs["state"] = BoundedTensorSpec(
|
|
low=0,
|
|
high=512,
|
|
shape=state_shape,
|
|
dtype=torch.float32,
|
|
device=self.device,
|
|
)
|
|
else:
|
|
# TODO(rcadene): add observation_space achieved_goal and desired_goal?
|
|
state_shape = self._env.observation_space["observation"].shape
|
|
if self.num_prev_obs > 0:
|
|
state_shape = (self.num_prev_obs + 1, *state_shape)
|
|
|
|
obs["state"] = UnboundedContinuousTensorSpec(
|
|
# TODO:
|
|
shape=state_shape,
|
|
dtype=torch.float32,
|
|
device=self.device,
|
|
)
|
|
self.observation_spec = CompositeSpec({"observation": obs})
|
|
|
|
self.action_spec = _gym_to_torchrl_spec_transform(
|
|
self._env.action_space,
|
|
device=self.device,
|
|
)
|
|
|
|
self.reward_spec = UnboundedContinuousTensorSpec(
|
|
shape=(1,),
|
|
dtype=torch.float32,
|
|
device=self.device,
|
|
)
|
|
|
|
self.done_spec = CompositeSpec(
|
|
{
|
|
"done": DiscreteTensorSpec(
|
|
2,
|
|
shape=(1,),
|
|
dtype=torch.bool,
|
|
device=self.device,
|
|
),
|
|
"success": DiscreteTensorSpec(
|
|
2,
|
|
shape=(1,),
|
|
dtype=torch.bool,
|
|
device=self.device,
|
|
),
|
|
}
|
|
)
|
|
|
|
def _set_seed(self, seed: Optional[int]):
|
|
set_seed(seed)
|
|
self._env.seed(seed)
|