9d002032d1
WIP Aloha env tests pass Rendering works (fps look fast tho? TODO action bounding is too wide [-1,1]) Update README Copy past from act repo Remove download.py add a WIP for Simxarm Remove download.py add a WIP for Simxarm Add act yaml (TODO: try train.py) Training can runs (TODO: eval) Add tasks without end_effector that are compatible with dataset, Eval can run (TODO: training and pretrained model) Add AbstractEnv, Refactor AlohaEnv, Add rendering_hook in env, Minor modifications, (TODO: Refactor Pusht and Simxarm) poetry lock fix bug in compute_stats for action normalization fix more bugs in normalization fix training fix import PushtEnv inheriates AbstractEnv, Improve factory Normalization Add _make_env to EnvAbstract Add call_rendering_hooks to pusht env SimxarmEnv inherites from AbstractEnv (NOT TESTED) Add aloha tests artifacts + update pusht stats fix image normalization: before env was in [0,1] but dataset in [0,255], and now both in [0,255] Small fix on simxarm Add next to obs Add top camera to Aloha env (TODO: make it compatible with set of cameras) Add top camera to Aloha env (TODO: make it compatible with set of cameras)
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
import abc
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from collections import deque
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from typing import Optional
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from tensordict import TensorDict
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from torchrl.envs import EnvBase
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class AbstractEnv(EnvBase):
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def __init__(
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self,
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task,
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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=1,
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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.task = task
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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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self._rendering_hooks = []
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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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self._make_env()
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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 register_rendering_hook(self, func):
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self._rendering_hooks.append(func)
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def call_rendering_hooks(self):
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for func in self._rendering_hooks:
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func(self)
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def reset_rendering_hooks(self):
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self._rendering_hooks = []
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@abc.abstractmethod
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def render(self, mode="rgb_array", width=640, height=480):
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raise NotImplementedError()
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@abc.abstractmethod
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def _reset(self, tensordict: Optional[TensorDict] = None):
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raise NotImplementedError()
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@abc.abstractmethod
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def _step(self, tensordict: TensorDict):
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raise NotImplementedError()
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@abc.abstractmethod
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def _make_env(self):
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raise NotImplementedError()
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@abc.abstractmethod
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def _make_spec(self):
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raise NotImplementedError()
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@abc.abstractmethod
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def _set_seed(self, seed: Optional[int]):
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raise NotImplementedError()
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