forked from tangger/lerobot
Fix unit tests, Refactor, Add pusht env, (TODO pusht replay buffer, image preprocessing)
This commit is contained in:
Cadene
@@ -1,17 +1,26 @@
|
||||
from torchrl.envs.transforms import StepCounter, TransformedEnv
|
||||
|
||||
from lerobot.common.envs.pusht import PushtEnv
|
||||
from lerobot.common.envs.simxarm import SimxarmEnv
|
||||
|
||||
|
||||
def make_env(cfg):
|
||||
assert cfg.env == "simxarm"
|
||||
env = SimxarmEnv(
|
||||
task=cfg.task,
|
||||
frame_skip=cfg.action_repeat,
|
||||
from_pixels=cfg.from_pixels,
|
||||
pixels_only=cfg.pixels_only,
|
||||
image_size=cfg.image_size,
|
||||
)
|
||||
kwargs = {
|
||||
"frame_skip": cfg.action_repeat,
|
||||
"from_pixels": cfg.from_pixels,
|
||||
"pixels_only": cfg.pixels_only,
|
||||
"image_size": cfg.image_size,
|
||||
}
|
||||
|
||||
if cfg.env == "simxarm":
|
||||
kwargs["task"] = cfg.task
|
||||
clsfunc = SimxarmEnv
|
||||
elif cfg.env == "pusht":
|
||||
clsfunc = PushtEnv
|
||||
else:
|
||||
raise ValueError(cfg.env)
|
||||
|
||||
env = clsfunc(**kwargs)
|
||||
|
||||
# limit rollout to max_steps
|
||||
env = TransformedEnv(env, StepCounter(max_steps=cfg.episode_length))
|
||||
|
||||
193
lerobot/common/envs/pusht.py
Normal file
193
lerobot/common/envs/pusht.py
Normal file
@@ -0,0 +1,193 @@
|
||||
import importlib
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from tensordict import TensorDict
|
||||
from torchrl.data.tensor_specs import (
|
||||
BoundedTensorSpec,
|
||||
CompositeSpec,
|
||||
DiscreteTensorSpec,
|
||||
UnboundedContinuousTensorSpec,
|
||||
)
|
||||
from torchrl.envs import EnvBase
|
||||
from torchrl.envs.libs.gym import _gym_to_torchrl_spec_transform
|
||||
|
||||
from lerobot.common.utils import set_seed
|
||||
|
||||
_has_gym = importlib.util.find_spec("gym") is not None
|
||||
_has_diffpolicy = importlib.util.find_spec("diffusion_policy") is not None and _has_gym
|
||||
|
||||
|
||||
class PushtEnv(EnvBase):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
frame_skip: int = 1,
|
||||
from_pixels: bool = False,
|
||||
pixels_only: bool = False,
|
||||
image_size=None,
|
||||
seed=1337,
|
||||
device="cpu",
|
||||
max_episode_length=25, # TODO: verify
|
||||
):
|
||||
super().__init__(device=device, batch_size=[])
|
||||
self.frame_skip = frame_skip
|
||||
self.from_pixels = from_pixels
|
||||
self.pixels_only = pixels_only
|
||||
self.image_size = image_size
|
||||
self.max_episode_length = max_episode_length
|
||||
|
||||
if pixels_only:
|
||||
assert from_pixels
|
||||
if from_pixels:
|
||||
assert image_size
|
||||
|
||||
if not _has_diffpolicy:
|
||||
raise ImportError("Cannot import diffusion_policy.")
|
||||
if not _has_gym:
|
||||
raise ImportError("Cannot import gym.")
|
||||
|
||||
import gym
|
||||
from diffusion_policy.env.pusht.pusht_env import PushTEnv
|
||||
from diffusion_policy.env.pusht.pusht_image_env import PushTImageEnv
|
||||
from gym.wrappers import TimeLimit
|
||||
|
||||
self._env = PushTImageEnv(render_size=self.image_size)
|
||||
self._env = TimeLimit(self._env, self.max_episode_length)
|
||||
|
||||
# MAX_NUM_ACTIONS = 4
|
||||
# num_actions = len(TASKS[self.task]["action_space"])
|
||||
# self._action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(num_actions,))
|
||||
# self._action_padding = np.zeros(
|
||||
# (MAX_NUM_ACTIONS - num_actions), dtype=np.float32
|
||||
# )
|
||||
# if "w" not in TASKS[self.task]["action_space"]:
|
||||
# self._action_padding[-1] = 1.0
|
||||
|
||||
self._make_spec()
|
||||
self.set_seed(seed)
|
||||
|
||||
def render(self, mode="rgb_array", width=384, height=384):
|
||||
if width != height:
|
||||
raise NotImplementedError()
|
||||
tmp = self._env.render_size
|
||||
self._env.render_size = width
|
||||
out = self._env.render(mode)
|
||||
self._env.render_size = tmp
|
||||
return out
|
||||
|
||||
def _format_raw_obs(self, raw_obs):
|
||||
if self.from_pixels:
|
||||
obs = {"image": torch.from_numpy(raw_obs["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)
|
||||
}
|
||||
|
||||
obs = TensorDict(obs, batch_size=[])
|
||||
return obs
|
||||
|
||||
def _reset(self, tensordict: Optional[TensorDict] = None):
|
||||
td = tensordict
|
||||
if td is None or td.is_empty():
|
||||
raw_obs = self._env.reset()
|
||||
|
||||
td = TensorDict(
|
||||
{
|
||||
"observation": self._format_raw_obs(raw_obs),
|
||||
"done": torch.tensor([False], dtype=torch.bool),
|
||||
},
|
||||
batch_size=[],
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
return td
|
||||
|
||||
def _step(self, tensordict: TensorDict):
|
||||
td = tensordict
|
||||
action = td["action"].numpy()
|
||||
# step expects shape=(4,) so we pad if necessary
|
||||
# TODO(rcadene): add info["is_success"] and info["success"] ?
|
||||
sum_reward = 0
|
||||
for t in range(self.frame_skip):
|
||||
raw_obs, reward, done, info = self._env.step(action)
|
||||
sum_reward += reward
|
||||
|
||||
td = TensorDict(
|
||||
{
|
||||
"observation": self._format_raw_obs(raw_obs),
|
||||
"reward": torch.tensor([sum_reward], dtype=torch.float32),
|
||||
# succes 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:
|
||||
obs["image"] = BoundedTensorSpec(
|
||||
low=0,
|
||||
high=1,
|
||||
shape=(3, self.image_size, self.image_size),
|
||||
dtype=torch.float32,
|
||||
device=self.device,
|
||||
)
|
||||
if not self.pixels_only:
|
||||
obs["state"] = BoundedTensorSpec(
|
||||
low=0,
|
||||
high=512,
|
||||
shape=self._env.observation_space["agent_pos"].shape,
|
||||
dtype=torch.float32,
|
||||
device=self.device,
|
||||
)
|
||||
else:
|
||||
# TODO(rcadene): add observation_space achieved_goal and desired_goal?
|
||||
obs["state"] = UnboundedContinuousTensorSpec(
|
||||
# TODO:
|
||||
shape=self._env.observation_space["observation"].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)
|
||||
@@ -167,18 +167,21 @@ class SimxarmEnv(EnvBase):
|
||||
device=self.device,
|
||||
)
|
||||
|
||||
self.done_spec = DiscreteTensorSpec(
|
||||
2,
|
||||
shape=(1,),
|
||||
dtype=torch.bool,
|
||||
device=self.device,
|
||||
)
|
||||
|
||||
self.success_spec = DiscreteTensorSpec(
|
||||
2,
|
||||
shape=(1,),
|
||||
dtype=torch.bool,
|
||||
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]):
|
||||
|
||||
Reference in New Issue
Block a user