driftwm/utils.py

from __future__ import annotations

import json
import math
import os
import random
from pathlib import Path
from typing import Any

import numpy as np
import torch
import yaml


def ensure_dir(path: str | os.PathLike[str]) -> Path:
    p = Path(path)
    p.mkdir(parents=True, exist_ok=True)
    return p


def load_yaml(path: str | os.PathLike[str]) -> dict[str, Any]:
    with open(path, "r", encoding="utf-8") as f:
        return yaml.safe_load(f)


def save_json(obj: Any, path: str | os.PathLike[str]) -> None:
    path = Path(path)
    ensure_dir(path.parent)
    with open(path, "w", encoding="utf-8") as f:
        json.dump(obj, f, indent=2, sort_keys=True)


def set_seed(seed: int) -> None:
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)


def wrap_angle(theta: float | np.ndarray) -> float | np.ndarray:
    return (theta + math.pi) % (2.0 * math.pi) - math.pi


def angle_error(pred: np.ndarray, target: np.ndarray) -> np.ndarray:
    return np.arctan2(np.sin(pred - target), np.cos(pred - target))


def obs_from_state(state: np.ndarray) -> np.ndarray:
    theta = state[..., 2]
    return np.stack(
        [state[..., 0], state[..., 1], np.cos(theta), np.sin(theta)],
        axis=-1,
    ).astype(np.float32)


def state_dim_for_action_dim(action_dim: int) -> int:
    return 6 + action_dim


def pad_action(action: np.ndarray, max_dim: int = 3) -> np.ndarray:
    out = np.zeros(max_dim, dtype=np.float32)
    out[: action.shape[-1]] = action
    return out


def unpad_action(action: np.ndarray, action_dim: int) -> np.ndarray:
    return np.asarray(action[..., :action_dim], dtype=np.float32)


def device_from_arg(device: str | None = None) -> torch.device:
    if device:
        return torch.device(device)
    return torch.device("cuda" if torch.cuda.is_available() else "cpu")


def configure_torch_runtime() -> None:
    """Prefer stable kernels on local ROCm builds.

    The local rocm712 environment can expose RDNA targets where MIOpen's GRU
    kernel compilation fails. Disabling the cuDNN/MIOpen RNN backend keeps the
    same ROCm device while using PyTorch's native kernels.
    """
    if torch.cuda.is_available() and getattr(torch.version, "hip", None):
        torch.backends.cudnn.enabled = False