from __future__ import annotations from dataclasses import dataclass from pathlib import Path from typing import Any, Dict, Optional import torch @dataclass class Episode: obs: torch.FloatTensor act: torch.LongTensor rew: torch.FloatTensor end: torch.ByteTensor trunc: torch.ByteTensor info: Dict[str, Any] states: torch.FloatTensor ego_state: torch.FloatTensor def __len__(self) -> int: return self.obs.size(0) def __add__(self, other: Episode) -> Episode: assert self.dead.sum() == 0 d = {k: torch.cat((v, other.__dict__[k]), dim=0) for k, v in self.__dict__.items() if k != "info"} return Episode(**d, info=merge_info(self.info, other.info)) def to(self, device) -> Episode: return Episode(**{k: v.to(device) if k != "info" else v for k, v in self.__dict__.items()}) @property def dead(self) -> torch.ByteTensor: return (self.end + self.trunc).clip(max=1) def compute_metrics(self) -> Dict[str, Any]: return {"length": len(self), "return": self.rew.sum().item()} @classmethod def load(cls, path: Path, map_location: Optional[torch.device] = None) -> Episode: return cls( **{ k: v.div(255).mul(2).sub(1) if k == "obs" else v for k, v in torch.load(Path(path), map_location=map_location).items() } ) def save(self, path: Path) -> None: path = Path(path) path.parent.mkdir(parents=True, exist_ok=True) d = {k: v.add(1).div(2).mul(255).byte() if k == "obs" else v for k, v in self.__dict__.items()} torch.save(d, path.with_suffix(".tmp")) path.with_suffix(".tmp").rename(path) def merge_info(info_a, info_b): keys_a = set(info_a) keys_b = set(info_b) intersection = keys_a & keys_b info = { **{k: info_a[k] for k in keys_a if k not in intersection}, **{k: info_b[k] for k in keys_b if k not in intersection}, **{k: torch.cat((info_a[k], info_b[k]), dim=0) for k in intersection}, } return info