Source code for stable_baselines3.common.vec_env.vec_transpose

from copy import deepcopy
from typing import Dict, Union

import numpy as np
from gymnasium import spaces

from stable_baselines3.common.preprocessing import is_image_space, is_image_space_channels_first
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvStepReturn, VecEnvWrapper

[docs]class VecTransposeImage(VecEnvWrapper): """ Re-order channels, from HxWxC to CxHxW. It is required for PyTorch convolution layers. :param venv: :param skip: Skip this wrapper if needed as we rely on heuristic to apply it or not, which may result in unwanted behavior, see GH issue #671. """ def __init__(self, venv: VecEnv, skip: bool = False): assert is_image_space(venv.observation_space) or isinstance( venv.observation_space, spaces.dict.Dict ), "The observation space must be an image or dictionary observation space" self.skip = skip # Do nothing if skip: super().__init__(venv) return if isinstance(venv.observation_space, spaces.dict.Dict): self.image_space_keys = [] observation_space = deepcopy(venv.observation_space) for key, space in observation_space.spaces.items(): if is_image_space(space): # Keep track of which keys should be transposed later self.image_space_keys.append(key) observation_space.spaces[key] = self.transpose_space(space, key) else: observation_space = self.transpose_space(venv.observation_space) super().__init__(venv, observation_space=observation_space)
[docs] @staticmethod def transpose_space(observation_space: spaces.Box, key: str = "") -> spaces.Box: """ Transpose an observation space (re-order channels). :param observation_space: :param key: In case of dictionary space, the key of the observation space. :return: """ # Sanity checks assert is_image_space(observation_space), "The observation space must be an image" assert not is_image_space_channels_first( observation_space ), f"The observation space {key} must follow the channel last convention" height, width, channels = observation_space.shape new_shape = (channels, height, width) return spaces.Box(low=0, high=255, shape=new_shape, dtype=observation_space.dtype)
[docs] @staticmethod def transpose_image(image: np.ndarray) -> np.ndarray: """ Transpose an image or batch of images (re-order channels). :param image: :return: """ if len(image.shape) == 3: return np.transpose(image, (2, 0, 1)) return np.transpose(image, (0, 3, 1, 2))
[docs] def transpose_observations(self, observations: Union[np.ndarray, Dict]) -> Union[np.ndarray, Dict]: """ Transpose (if needed) and return new observations. :param observations: :return: Transposed observations """ # Do nothing if self.skip: return observations if isinstance(observations, dict): # Avoid modifying the original object in place observations = deepcopy(observations) for k in self.image_space_keys: observations[k] = self.transpose_image(observations[k]) else: observations = self.transpose_image(observations) return observations
[docs] def step_wait(self) -> VecEnvStepReturn: observations, rewards, dones, infos = self.venv.step_wait() # Transpose the terminal observations for idx, done in enumerate(dones): if not done: continue if "terminal_observation" in infos[idx]: infos[idx]["terminal_observation"] = self.transpose_observations(infos[idx]["terminal_observation"]) return self.transpose_observations(observations), rewards, dones, infos
[docs] def reset(self) -> Union[np.ndarray, Dict]: """ Reset all environments """ return self.transpose_observations(self.venv.reset())
[docs] def close(self) -> None: self.venv.close()