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Merge pull request octo-models#122 from rail-berkeley/sim_eval_loop
Add sim rollout visualization option to train.py + finetune.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,115 @@ | ||
| from typing import List | ||
| import copy | ||
| import cv2 | ||
| from einops import rearrange | ||
| import gym | ||
| import jax | ||
| import jax.numpy as jnp | ||
| import numpy as np | ||
| import dlimp as dl | ||
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| from experiments.lucy.aloha_pro.aloha_scripts.utils import crop_resize | ||
| from experiments.lucy.aloha_pro.aloha_scripts.sim_env import BOX_POSE, sample_box_pose | ||
| from experiments.lucy.aloha_pro.aloha_scripts.sim_env import make_sim_env | ||
|
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| class AlohaGymEnv(gym.Env): | ||
| def __init__( | ||
| self, | ||
| env: gym.Env, | ||
| camera_names: List[str], | ||
| im_size: int = 256, | ||
| seed: int = 1234): | ||
| self._env = env | ||
| self.observation_space = gym.spaces.Dict( | ||
| { | ||
| **{ | ||
| f"image_{i}": gym.spaces.Box( | ||
| low=np.zeros((im_size, im_size, 3)), | ||
| high=255 * np.ones((im_size, im_size, 3)), | ||
| dtype=np.uint8, | ||
| ) for i in range(len(camera_names)) | ||
| }, | ||
| "proprio": gym.spaces.Box( | ||
| low=np.ones((14,)) * -1, high=np.ones((14,)), dtype=np.float32 | ||
| ), | ||
| } | ||
| ) | ||
| self.action_space = gym.spaces.Box( | ||
| low=np.ones((14,)) * -1, high=np.ones((14,)), dtype=np.float32 | ||
| ) | ||
| self.camera_names = camera_names | ||
| self._im_size = im_size | ||
| self._rng = np.random.default_rng(seed) | ||
|
|
||
| def step(self, action): | ||
| ts = self._env.step(action) | ||
| obs, images = self.get_obs(ts) | ||
| reward = ts.reward | ||
| info = {"images": images} | ||
|
|
||
| if reward == self._env.task.max_reward: | ||
| self._episode_is_success = 1 | ||
|
|
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| return obs, reward, False, False, info | ||
|
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| def reset(self, **kwargs): | ||
| x_range = [0.0, 0.2] | ||
| y_range = [0.4, 0.6] | ||
| z_range = [0.05, 0.05] | ||
| ranges = np.vstack([x_range, y_range, z_range]) | ||
| cube_position = self._rng.uniform(ranges[:, 0], ranges[:, 1]) | ||
| cube_quat = np.array([1, 0, 0, 0]) | ||
| BOX_POSE[0] = np.concatenate([cube_position, cube_quat]) | ||
|
|
||
| ts = self._env.reset(**kwargs) | ||
| obs, images = self.get_obs(ts) | ||
| info = {"images": images} | ||
| self._goal_obs = copy.deepcopy(obs) # HACK | ||
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| self._episode_is_success = 0 | ||
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| return obs, info | ||
|
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| def get_obs(self, ts): | ||
| curr_obs = {} | ||
| vis_images = [] | ||
|
|
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| for i, cam_name in enumerate(self.camera_names): | ||
| curr_image = ts.observation['images'][cam_name] | ||
|
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| # Check for 'cam_high' and crop | ||
| if cam_name == 'cam_high': | ||
| curr_image = crop_resize(curr_image) | ||
|
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| #curr_image = cv2.cvtColor(curr_image, cv2.COLOR_BGR2RGB) | ||
| vis_images.append(copy.deepcopy(curr_image)) | ||
| curr_image = jnp.array(curr_image) # XXX: / 255. ? | ||
| curr_obs[f"image_{i}"] = curr_image | ||
| curr_obs = dl.transforms.resize_images( | ||
| curr_obs, match=curr_obs.keys(), size=(self._im_size, self._im_size)) | ||
|
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| qpos_numpy = np.array(ts.observation['qpos']) | ||
| qpos = jnp.array(qpos_numpy) | ||
| curr_obs['proprio'] = qpos | ||
|
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| return curr_obs, np.concatenate(vis_images, axis=-2) | ||
|
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| def get_task(self): | ||
| assert self._goal_obs, "Need to run reset() before!" | ||
| return { | ||
| "language_instruction": ["pick up the cube and hand it over".encode()], | ||
| **jax.tree_map(lambda x: x[None] * 0, self._goal_obs), | ||
| } | ||
|
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| def get_episode_metrics(self): | ||
| return { | ||
| "success_rate": self._episode_is_success, | ||
| } | ||
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| # register gym environments | ||
| gym.register( | ||
| 'aloha-sim-cube-v0', | ||
| entry_point=lambda: AlohaGymEnv(make_sim_env("sim_transfer_cube"), camera_names=["top"]), | ||
| ) |
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