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| from typing import Any, Union, List | |
| import copy | |
| import numpy as np | |
| import gym | |
| import matplotlib.pyplot as plt | |
| import einops | |
| import imageio | |
| from easydict import EasyDict | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.envs.common.env_element import EnvElement, EnvElementInfo | |
| from ding.envs.common.common_function import affine_transform | |
| from ding.torch_utils import to_ndarray, to_list | |
| from .d4rl_wrappers import wrap_d4rl | |
| from ding.utils import ENV_REGISTRY | |
| MAZE_BOUNDS = { | |
| 'maze2d-umaze-v1': (0, 5, 0, 5), | |
| 'maze2d-medium-v1': (0, 8, 0, 8), | |
| 'maze2d-large-v1': (0, 9, 0, 12) | |
| } | |
| def plot2img(fig, remove_margins=True): | |
| # https://stackoverflow.com/a/35362787/2912349 | |
| # https://stackoverflow.com/a/54334430/2912349 | |
| from matplotlib.backends.backend_agg import FigureCanvasAgg | |
| if remove_margins: | |
| fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) | |
| canvas = FigureCanvasAgg(fig) | |
| canvas.draw() | |
| img_as_string, (width, height) = canvas.print_to_buffer() | |
| return np.fromstring(img_as_string, dtype='uint8').reshape((height, width, 4)) | |
| def zipsafe(*args): | |
| length = len(args[0]) | |
| assert all([len(a) == length for a in args]) | |
| return zip(*args) | |
| def zipkw(*args, **kwargs): | |
| nargs = len(args) | |
| keys = kwargs.keys() | |
| vals = [kwargs[k] for k in keys] | |
| zipped = zipsafe(*args, *vals) | |
| for items in zipped: | |
| zipped_args = items[:nargs] | |
| zipped_kwargs = {k: v for k, v in zipsafe(keys, items[nargs:])} | |
| yield zipped_args, zipped_kwargs | |
| class D4RLEnv(BaseEnv): | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._use_act_scale = cfg.use_act_scale | |
| self._init_flag = False | |
| if 'maze' in self._cfg.env_id: | |
| self.observations = [] | |
| self._extent = (0, 1, 1, 0) | |
| def reset(self) -> np.ndarray: | |
| if not self._init_flag: | |
| self._env = self._make_env(only_info=False) | |
| self._env.observation_space.dtype = np.float32 # To unify the format of envs in DI-engine | |
| self._observation_space = self._env.observation_space | |
| if 'maze' in self._cfg.env_id: | |
| new_low = np.tile(self._observation_space.low, 2) | |
| new_high = np.tile(self._observation_space.high, 2) | |
| self._observation_space = gym.spaces.Box(low=new_low, high=new_high) | |
| self._action_space = self._env.action_space | |
| self._reward_space = gym.spaces.Box( | |
| low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32 | |
| ) | |
| self._init_flag = True | |
| if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed: | |
| np_seed = 100 * np.random.randint(1, 1000) | |
| self._env.seed(self._seed + np_seed) | |
| elif hasattr(self, '_seed'): | |
| self._env.seed(self._seed) | |
| if 'maze' in self._cfg.env_id: | |
| target = self._env.get_target() | |
| self.target_obs = np.array([*target, 0, 0]) | |
| obs = self._env.reset() | |
| if 'maze' in self._cfg.env_id: | |
| self.observations.append(obs) | |
| obs = np.hstack((obs, self.target_obs)) | |
| obs = to_ndarray(obs).astype('float32') | |
| self._eval_episode_return = 0. | |
| return obs | |
| def close(self) -> None: | |
| if self._init_flag: | |
| self._env.close() | |
| self._init_flag = False | |
| def seed(self, seed: int, dynamic_seed: bool = True) -> None: | |
| self._seed = seed | |
| self._dynamic_seed = dynamic_seed | |
| np.random.seed(self._seed) | |
| def step(self, action: Union[np.ndarray, list]) -> BaseEnvTimestep: | |
| action = to_ndarray(action) | |
| if self._use_act_scale: | |
| action_range = {'min': self.action_space.low[0], 'max': self.action_space.high[0], 'dtype': np.float32} | |
| action = affine_transform(action, min_val=action_range['min'], max_val=action_range['max']) | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| if 'maze' in self._cfg.env_id: | |
| self.observations.append(obs) | |
| obs = np.hstack([obs, self.target_obs]) | |
| obs = to_ndarray(obs).astype('float32') | |
| rew = to_ndarray([rew]) # wrapped to be transfered to a array with shape (1,) | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| # self.composite('/mnt/PD/render/rollout.png',self.observations,ncol=1) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def renders(self, observations, conditions=None, title=None): | |
| bounds = MAZE_BOUNDS[self._cfg.env_id] | |
| observations = observations + .5 | |
| if len(bounds) == 2: | |
| _, scale = bounds | |
| observations /= scale | |
| elif len(bounds) == 4: | |
| _, iscale, _, jscale = bounds | |
| observations[:, 0] /= iscale | |
| observations[:, 1] /= jscale | |
| else: | |
| raise RuntimeError(f'Unrecognized bounds for {self._cfg.env_id}: {bounds}') | |
| if conditions is not None: | |
| conditions /= scale | |
| plt.clf() | |
| fig = plt.gcf() | |
| fig.set_size_inches(5, 5) | |
| plt.imshow(self._background * .5, | |
| extent=self._extent, cmap=plt.cm.binary, vmin=0, vmax=1) | |
| path_length = len(observations) | |
| colors = plt.cm.jet(np.linspace(0,1,path_length)) | |
| plt.plot(observations[:,1], observations[:,0], c='black', zorder=10) | |
| plt.scatter(observations[:,1], observations[:,0], c=colors, zorder=20) | |
| plt.axis('off') | |
| plt.title(title) | |
| img = plot2img(fig, remove_margins=self._remove_margins) | |
| return img | |
| def composite(self, savepath, paths, ncol=5, **kwargs): | |
| assert len(paths) % ncol == 0, 'Number of paths must be divisible by number of columns' | |
| images = [] | |
| for path, kw in zipkw(paths, **kwargs): | |
| img = self.renders(*path, **kw) | |
| images.append(img) | |
| images = np.stack(images, axis=0) | |
| nrow = len(images) // ncol | |
| images = einops.rearrange(images, | |
| '(nrow ncol) H W C -> (nrow H) (ncol W) C', nrow=nrow, ncol=ncol) | |
| imageio.imsave(savepath, images) | |
| print(f'Saved {len(paths)} samples to: {savepath}') | |
| def _make_env(self, only_info=False): | |
| return wrap_d4rl( | |
| self._cfg.env_id, | |
| norm_obs=self._cfg.get( | |
| 'norm_obs', | |
| EasyDict(use_norm=False, offline_stats=dict(use_offline_stats=False, )), | |
| ), | |
| norm_reward=self._cfg.get('norm_reward', EasyDict(use_norm=False, )), | |
| only_info=only_info | |
| ) | |
| def __repr__(self) -> str: | |
| return "DI-engine D4RL Env({})".format(self._cfg.env_id) | |
| def create_collector_env_cfg(cfg: dict) -> List[dict]: | |
| collector_cfg = copy.deepcopy(cfg) | |
| collector_env_num = collector_cfg.pop('collector_env_num', 1) | |
| return [collector_cfg for _ in range(collector_env_num)] | |
| def create_evaluator_env_cfg(cfg: dict) -> List[dict]: | |
| evaluator_cfg = copy.deepcopy(cfg) | |
| evaluator_env_num = evaluator_cfg.pop('evaluator_env_num', 1) | |
| evaluator_cfg.get('norm_reward', EasyDict(use_norm=False, )).use_norm = False | |
| return [evaluator_cfg for _ in range(evaluator_env_num)] | |
| def observation_space(self) -> gym.spaces.Space: | |
| return self._observation_space | |
| def action_space(self) -> gym.spaces.Space: | |
| return self._action_space | |
| def reward_space(self) -> gym.spaces.Space: | |
| return self._reward_space | |