Delete eval_hyperbolic.py

eval_hyperbolic.py

@@ -1,990 +0,0 @@
-"""
-cube:
-MUJOCO_GL=egl python eval_hyperbolic.py --config-name=hyperbolic_cube policy=./ogbench/Ablation/No_SigReg/lewm_hyperbolic_no_sigreg_epoch_100 solver.device=auto planning.hyperbolic.terminal_weight=0.0 planning.hyperbolic.best_weight=1.0 planning.hyperbolic.mean_weight=0.0 planning.hyperbolic.progress_weight=0.0 planning.hyperbolic.cone_weight=0.0
-
-antmaze:
-MUJOCO_GL=egl python eval_hyperbolic.py --config-name=hyperbolic_antmaze policy=./ogbench/Experiment/hyperbolic_exp_antmaze/lewm_hyperbolic_epoch_100 solver.device=auto planning.hyperbolic.terminal_weight=0.0 planning.hyperbolic.best_weight=1.0 planning.hyperbolic.mean_weight=0.0 planning.hyperbolic.progress_weight=0.0 planning.hyperbolic.cone_weight=0.0
-
-"""
-
-
-import os
-
-
-def _configure_mujoco_backend() -> str:
-    backend = os.environ.get("MUJOCO_GL", "egl").strip().lower() or "egl"
-    os.environ["MUJOCO_GL"] = backend
-    if backend in {"egl", "osmesa"}:
-        os.environ.setdefault("PYOPENGL_PLATFORM", backend)
-    return backend
-
-
-MUJOCO_GL_BACKEND = _configure_mujoco_backend()
-
-import time
-from collections import defaultdict
-from copy import deepcopy
-from pathlib import Path
-from typing import Any, Sequence
-
-import gymnasium as gym
-import hydra
-import numpy as np
-import stable_pretraining as spt
-import torch
-from omegaconf import DictConfig, OmegaConf, open_dict
-from sklearn import preprocessing
-from torchvision.transforms import v2 as transforms
-from tqdm.auto import tqdm
-import stable_worldmodel as swm
-
-from module import ARPredictor, Embedder, MLP, SIGReg
-from train_hyperbolic import (
-    AdaptiveEntailmentConeLoss,
-    HyperbolicJEPA,
-    LorentzContrastiveLoss,
-    LorentzManifold,
-    build_hyperbolic_world_model,
-    ensure_hyperbolic_defaults,
-)
-from utils import resolve_runtime_device
-
-
-def _maybe_register_ogbench_envs() -> None:
-    try:
-        import ogbench  # noqa: F401
-        print("[eval] imported ogbench to register Gym environments", flush=True)
-    except Exception as exc:
-        print(
-            f"[eval] ogbench import skipped: {type(exc).__name__}: {exc}",
-            flush=True,
-        )
-
-
-def _build_env_candidates(env_name: str) -> list[str]:
-    raw_candidates = [env_name]
-
-    if env_name.startswith("visual-"):
-        raw_candidates.append(env_name.removeprefix("visual-"))
-
-    for suffix in ("-navigate-v0", "-oraclerep-v0"):
-        expanded = []
-        for candidate in raw_candidates:
-            if candidate.endswith(suffix):
-                expanded.append(candidate[: -len(suffix)] + "-v0")
-        raw_candidates.extend(expanded)
-
-    expanded = []
-    for candidate in raw_candidates:
-        if candidate.startswith("visual-"):
-            expanded.append(candidate.removeprefix("visual-"))
-    raw_candidates.extend(expanded)
-
-    candidates = []
-    seen = set()
-    for candidate in raw_candidates:
-        if candidate in seen:
-            continue
-        seen.add(candidate)
-        candidates.append(candidate)
-    return candidates
-
-
-def _resolve_world_env_name(env_name: str) -> str:
-    _maybe_register_ogbench_envs()
-
-    registry_keys = set(gym.registry.keys())
-    if env_name in registry_keys:
-        return env_name
-
-    candidates = _build_env_candidates(env_name)
-    for candidate in candidates:
-        if candidate in registry_keys:
-            print(
-                f"[eval] resolved env_name '{env_name}' -> '{candidate}'",
-                flush=True,
-            )
-            return candidate
-
-    print(
-        f"[eval] could not resolve env_name '{env_name}'. "
-        f"Tried: {candidates}",
-        flush=True,
-    )
-    return env_name
-
-def img_transform(cfg):
-    transform = transforms.Compose(
-        [
-            transforms.ToImage(),
-            transforms.ToDtype(torch.float32, scale=True),
-            transforms.Normalize(**spt.data.dataset_stats.ImageNet),
-            transforms.Resize(size=cfg.eval.img_size),
-        ]
-    )
-    return transform
-
-
-def get_episodes_length(dataset, episodes):
-    col_name = "episode_idx" if "episode_idx" in dataset.column_names else "ep_idx"
-
-    episode_idx = dataset.get_col_data(col_name)
-    step_idx = dataset.get_col_data("step_idx")
-    lengths = []
-    for ep_id in episodes:
-        lengths.append(np.max(step_idx[episode_idx == ep_id]) + 1)
-    return np.array(lengths)
-
-
-def get_dataset(cfg, dataset_name):
-    dataset_path = Path(cfg.cache_dir or swm.data.utils.get_cache_dir())
-    dataset = swm.data.HDF5Dataset(
-        dataset_name,
-        keys_to_cache=cfg.dataset.keys_to_cache,
-        cache_dir=dataset_path,
-    )
-    return dataset
-
-
-def _get_column_row_count(dataset, col_name: str) -> int | None:
-    h5_file = getattr(dataset, "h5_file", None)
-    try:
-        if h5_file is not None and col_name in h5_file:
-            shape = getattr(h5_file[col_name], "shape", None)
-            if shape:
-                return int(shape[0])
-    except Exception:
-        pass
-
-    try:
-        return int(len(dataset.get_col_data(col_name)))
-    except Exception:
-        return None
-
-
-def _resolve_safe_row_limit(dataset, extra_columns: Sequence[str]) -> tuple[int, dict[str, int]]:
-    counts: dict[str, int] = {"__len__": int(len(dataset))}
-    episode_meta_columns = {"ep_len", "ep_offset", "episode_ends"}
-    columns = [
-        col for col in list(getattr(dataset, "_keys", [])) + list(extra_columns)
-        if col not in episode_meta_columns
-    ]
-
-    seen = set()
-    for col_name in columns:
-        if col_name in seen:
-            continue
-        seen.add(col_name)
-        count = _get_column_row_count(dataset, col_name)
-        if count is not None:
-            counts[col_name] = count
-
-    safe_limit = min(counts.values())
-    return safe_limit, counts
-
-
-def _chunk_has_content(episode_chunk: dict[str, Any]) -> tuple[bool, str | None]:
-    for col_name, value in episode_chunk.items():
-        if not isinstance(value, (torch.Tensor, np.ndarray)):
-            continue
-        shape = getattr(value, "shape", None)
-        if shape is None or len(shape) < 1:
-            continue
-        if int(shape[0]) == 0:
-            return False, col_name
-    return True, None
-
-
-def _select_valid_eval_rows(
-    dataset,
-    candidate_indices: np.ndarray,
-    episode_idx_col: np.ndarray,
-    step_idx_col: np.ndarray,
-    goal_offset_steps: int,
-    num_eval: int,
-) -> np.ndarray:
-    selected: list[int] = []
-    skipped_empty = 0
-
-    for row_idx in candidate_indices.tolist():
-        ep_id = int(episode_idx_col[row_idx])
-        start_step = int(step_idx_col[row_idx])
-        end_step = int(start_step + goal_offset_steps)
-
-        try:
-            chunk = dataset.load_chunk(
-                np.array([ep_id]),
-                np.array([start_step]),
-                np.array([end_step]),
-            )
-        except Exception as exc:
-            print(
-                f"[eval] skipping candidate row={row_idx} episode={ep_id} start={start_step} "
-                f"because load_chunk failed: {type(exc).__name__}: {exc}",
-                flush=True,
-            )
-            continue
-
-        if not chunk:
-            skipped_empty += 1
-            continue
-
-        has_content, bad_col = _chunk_has_content(chunk[0])
-        if not has_content:
-            if skipped_empty < 5:
-                print(
-                    f"[eval] skipping empty candidate row={row_idx} episode={ep_id} "
-                    f"start={start_step} bad_col={bad_col}",
-                    flush=True,
-                )
-            skipped_empty += 1
-            continue
-
-        selected.append(int(row_idx))
-        if len(selected) >= num_eval:
-            break
-
-    if skipped_empty:
-        print(f"[eval] skipped {skipped_empty} empty/invalid evaluation candidates.", flush=True)
-
-    if len(selected) < num_eval:
-        raise ValueError(
-            f"Only found {len(selected)} valid evaluation starts after load_chunk validation, "
-            f"but {num_eval} were requested."
-        )
-
-    return np.asarray(selected, dtype=np.int64)
-
-
-def _register_hyperbolic_main_aliases():
-    import __main__ as main_mod
-
-    for obj in (
-        HyperbolicJEPA,
-        LorentzManifold,
-        LorentzContrastiveLoss,
-        AdaptiveEntailmentConeLoss,
-        ARPredictor,
-        Embedder,
-        MLP,
-        SIGReg,
-    ):
-        setattr(main_mod, obj.__name__, obj)
-
-    if hasattr(torch.serialization, "add_safe_globals"):
-        torch.serialization.add_safe_globals(
-            [
-                HyperbolicJEPA,
-                LorentzManifold,
-                LorentzContrastiveLoss,
-                AdaptiveEntailmentConeLoss,
-                ARPredictor,
-                Embedder,
-                MLP,
-                SIGReg,
-            ]
-        )
-
-
-def _resolve_policy_artifact_candidates(policy_name: str, cache_dir: Path) -> list[tuple[Path, Path]]:
-    raw = Path(policy_name)
-    candidates = []
-
-    def add_exact_prefix_artifacts(path_like: Path):
-        path_text = str(path_like)
-        for suffix in ("_state.ckpt", "_object.ckpt", "_weights.ckpt"):
-            if path_text.endswith(suffix):
-                add_exact_prefix_artifacts(Path(path_text[: -len(suffix)]))
-                return
-        if path_text.endswith(".ckpt"):
-            candidates.append(path_like)
-            return
-
-        # Prefer device-neutral epoch state dicts when they exist. Object
-        # checkpoints saved on Ascend may require torch_npu to unpickle.
-        candidates.append(Path(f"{path_like}_state.ckpt"))
-        candidates.append(Path(f"{path_like}_object.ckpt"))
-        candidates.append(Path(f"{path_like}_weights.ckpt"))
-
-    def add_parent_weights(path_like: Path):
-        parent = path_like.parent
-        if parent.is_dir():
-            weights = sorted(parent.glob("*_weights.ckpt"))
-            if len(weights) == 1:
-                candidates.append(weights[0])
-
-    add_exact_prefix_artifacts(raw)
-    add_exact_prefix_artifacts(cache_dir / raw)
-
-    if raw.is_file():
-        candidates.append(raw)
-        add_parent_weights(raw)
-    cache_raw = cache_dir / raw
-    if cache_raw.is_file():
-        candidates.append(cache_raw)
-        add_parent_weights(cache_raw)
-    if cache_raw.is_dir():
-        weights = sorted(cache_raw.glob("*_weights.ckpt"))
-        if len(weights) == 1:
-            candidates.append(weights[0])
-
-    add_parent_weights(raw)
-    add_parent_weights(cache_raw)
-
-    seen = set()
-    resolved = []
-    for candidate in candidates:
-        candidate = Path(candidate)
-        if candidate in seen:
-            continue
-        seen.add(candidate)
-        if candidate.is_file():
-            config_path = candidate.parent / "config.yaml"
-            if config_path.is_file():
-                resolved.append((candidate, config_path))
-
-    if resolved:
-        return resolved
-
-    raise FileNotFoundError(
-        f"Could not resolve checkpoint/config for policy '{policy_name}' under cache '{cache_dir}'."
-    )
-
-
-def _resolve_policy_artifacts(policy_name: str, cache_dir: Path) -> tuple[Path, Path]:
-    return _resolve_policy_artifact_candidates(policy_name, cache_dir)[0]
-
-
-def _should_prefer_hyperbolic_loader(cfg: DictConfig) -> bool:
-    checkpoint_cfg = cfg.get("checkpoint", {})
-    if bool(getattr(checkpoint_cfg, "force_rebuild", False)):
-        return True
-
-    policy_name = str(cfg.get("policy", "") or "")
-    if "hyperbolic" in policy_name.lower():
-        return True
-
-    return False
-
-
-def _load_hyperbolic_policy_model(cfg: DictConfig, dataset, runtime_device: str):
-    cache_dir = Path(cfg.cache_dir or swm.data.utils.get_cache_dir())
-    checkpoint_candidates = _resolve_policy_artifact_candidates(cfg.policy, cache_dir)
-    _register_hyperbolic_main_aliases()
-    failures = []
-
-    for checkpoint_path, config_path in checkpoint_candidates:
-        print(
-            f"[eval] trying hyperbolic checkpoint={checkpoint_path} config={config_path}",
-            flush=True,
-        )
-        train_cfg = OmegaConf.load(config_path)
-        ensure_hyperbolic_defaults(train_cfg)
-        action_dim = int(getattr(train_cfg.wm, "action_dim", dataset.get_dim("action")))
-        model = build_hyperbolic_world_model(train_cfg, action_dim=action_dim)
-
-        try:
-            checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False)
-            if isinstance(checkpoint, torch.nn.Module):
-                model = checkpoint
-            else:
-                if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
-                    state_dict = checkpoint["state_dict"]
-                elif isinstance(checkpoint, dict):
-                    state_dict = checkpoint
-                else:
-                    raise TypeError(
-                        f"Unsupported checkpoint type '{type(checkpoint).__name__}' for '{checkpoint_path}'."
-                    )
-
-                if any(key.startswith("model.") for key in state_dict.keys()):
-                    state_dict = {
-                        key[len("model."):]: value
-                        for key, value in state_dict.items()
-                        if key.startswith("model.")
-                    }
-
-                strict = bool(getattr(cfg.get("checkpoint", {}), "strict", True))
-                missing, unexpected = model.load_state_dict(state_dict, strict=strict)
-                print(
-                    f"[eval] loaded hyperbolic checkpoint {checkpoint_path} "
-                    f"(strict={strict}, missing={len(missing)}, unexpected={len(unexpected)})",
-                    flush=True,
-                )
-                if missing:
-                    print(f"[eval] missing keys: {missing}", flush=True)
-                if unexpected:
-                    print(f"[eval] unexpected keys: {unexpected}", flush=True)
-        except ModuleNotFoundError as exc:
-            if exc.name == "torch_npu" or str(exc.name).startswith("torch_npu."):
-                failures.append(
-                    f"{checkpoint_path}: requires torch_npu during object deserialization"
-                )
-                print(
-                    f"[eval] checkpoint {checkpoint_path} requires torch_npu; "
-                    "trying the next portable/state checkpoint candidate.",
-                    flush=True,
-                )
-                continue
-            raise
-        except RuntimeError as exc:
-            failures.append(f"{checkpoint_path}: {type(exc).__name__}: {exc}")
-            print(
-                f"[eval] checkpoint {checkpoint_path} did not match the rebuilt model; "
-                "trying the next checkpoint candidate.",
-                flush=True,
-            )
-            continue
-
-        model = model.to(runtime_device)
-        model = model.eval()
-        model.requires_grad_(False)
-        model.interpolate_pos_encoding = True
-        return model
-
-    failure_details = "\n".join(f"  - {failure}" for failure in failures)
-    raise RuntimeError(
-        "Unable to load a hyperbolic policy checkpoint on this runtime. "
-        "For an object checkpoint saved from NPU training, evaluate in a torch_npu "
-        "environment once or train/export a matching '<prefix>_state.ckpt' portable state dict.\n"
-        f"Tried:\n{failure_details}"
-    )
-
-
-def load_policy_model(cfg: DictConfig, dataset, runtime_device: str):
-    if _should_prefer_hyperbolic_loader(cfg):
-        print(
-            f"[eval] bypassing AutoCostModel and preferring rebuilt hyperbolic weights for "
-            f"policy='{cfg.policy}'",
-            flush=True,
-        )
-        return _load_hyperbolic_policy_model(cfg, dataset, runtime_device)
-
-    try:
-        model = swm.policy.AutoCostModel(cfg.policy)
-        model = model.to(runtime_device)
-        model = model.eval()
-        model.requires_grad_(False)
-        model.interpolate_pos_encoding = True
-        print(f"[eval] loaded policy via AutoCostModel: {cfg.policy}", flush=True)
-        return model
-    except Exception as exc:
-        print(
-            f"[eval] AutoCostModel failed for '{cfg.policy}': {type(exc).__name__}: {exc}",
-            flush=True,
-        )
-        print("[eval] falling back to hyperbolic checkpoint loader", flush=True)
-        return _load_hyperbolic_policy_model(cfg, dataset, runtime_device)
-
-
-def _wrap_step_with_static_info(env, static_info: dict[str, Any]) -> None:
-    """Inject dataset goal info into env.step infos for envs that do not emit it."""
-    env._lewm_static_step_info = static_info
-    if getattr(env, "_lewm_static_step_info_wrapped", False):
-        return
-
-    original_step = env.step
-
-    def step_with_static_info(action):
-        result = original_step(action)
-        if len(result) == 5:
-            obs, reward, terminated, truncated, info = result
-            info = {} if info is None else dict(info)
-            for key, value in getattr(env, "_lewm_static_step_info", {}).items():
-                info.setdefault(key, deepcopy(value))
-            return obs, reward, terminated, truncated, info
-
-        obs, reward, done, info = result
-        info = {} if info is None else dict(info)
-        for key, value in getattr(env, "_lewm_static_step_info", {}).items():
-            info.setdefault(key, deepcopy(value))
-        return obs, reward, done, info
-
-    env.step = step_with_static_info
-    env._lewm_static_step_info_wrapped = True
-
-
-def _iter_vector_env_lists(root) -> list:
-    env_lists = []
-    queue = [root]
-    seen = set()
-    while queue:
-        obj = queue.pop(0)
-        if obj is None or id(obj) in seen:
-            continue
-        seen.add(id(obj))
-
-        envs = getattr(obj, "envs", None)
-        if isinstance(envs, (list, tuple)) and envs:
-            env_lists.append(envs)
-
-        for attr in ("env", "unwrapped"):
-            try:
-                child = getattr(obj, attr, None)
-            except Exception:
-                child = None
-            if child is not None and id(child) not in seen:
-                queue.append(child)
-    return env_lists
-
-
-def _prepare_static_info_for_env(goal_step: dict[str, np.ndarray], env_index: int) -> dict[str, Any]:
-    static_info = {}
-    for key, value in goal_step.items():
-        if not isinstance(value, np.ndarray) or value.shape[0] <= env_index:
-            continue
-        env_value = value[env_index]
-        if isinstance(env_value, np.ndarray) and env_value.ndim > 0:
-            env_value = env_value[-1]
-        static_info[key] = deepcopy(env_value)
-    return static_info
-
-
-def _inject_static_goal_info_into_envs(world, goal_step: dict[str, np.ndarray]) -> None:
-    """Make dataset goals visible in per-step env info for wrappers that require them."""
-    wrapped_count = 0
-    for envs in _iter_vector_env_lists(world.envs):
-        if len(envs) != world.num_envs:
-            continue
-        for env_index, env in enumerate(envs):
-            static_info = _prepare_static_info_for_env(goal_step, env_index)
-            if not static_info:
-                continue
-
-            current_env = env
-            seen = set()
-            while current_env is not None and id(current_env) not in seen:
-                seen.add(id(current_env))
-                _wrap_step_with_static_info(current_env, static_info)
-                wrapped_count += 1
-                current_env = getattr(current_env, "env", None)
-
-    if wrapped_count == 0:
-        print("[eval] warning: did not find env wrappers to inject static goal info", flush=True)
-    else:
-        print(f"[eval] injected static goal info into {wrapped_count} env wrapper(s)", flush=True)
-
-
-def _set_dataset_goal_xy(world, goal_step: dict[str, np.ndarray]) -> None:
-    """Align OGBench MazeEnv success checks with the dataset goal state."""
-    goal_qpos = goal_step.get("goal_qpos")
-    if goal_qpos is None:
-        return
-
-    applied = 0
-    for i, env in enumerate(_get_unwrapped_envs(world)):
-        env_unwrapped = env.unwrapped
-        set_goal = getattr(env_unwrapped, "set_goal", None)
-        if not callable(set_goal):
-            continue
-        goal_xy = np.asarray(goal_qpos[i], dtype=np.float64).reshape(-1)[:2]
-        if goal_xy.shape[0] != 2:
-            continue
-        try:
-            set_goal(goal_xy=goal_xy)
-            applied += 1
-        except TypeError:
-            continue
-
-    if applied:
-        print(f"[eval] set dataset goal_xy for {applied} env(s)", flush=True)
-
-
-def _get_unwrapped_envs(world):
-    envs = getattr(getattr(world.envs, "unwrapped", world.envs), "envs", None)
-    if isinstance(envs, (list, tuple)) and envs:
-        return envs
-    for envs in _iter_vector_env_lists(world.envs):
-        if len(envs) == world.num_envs:
-            return envs
-    return []
-
-
-def _set_vector_autoreset_flags(world) -> None:
-    for obj in (world.envs, getattr(world.envs, "unwrapped", None)):
-        if obj is not None and hasattr(obj, "_autoreset_envs"):
-            obj._autoreset_envs = np.zeros((world.num_envs,))
-            return
-
-
-def _resize_hwc_frames(frames: np.ndarray, height: int, width: int) -> np.ndarray:
-    if frames.shape[-3:-1] == (height, width):
-        return frames.astype(np.uint8, copy=False)
-
-    import torch.nn.functional as F
-
-    tensor = torch.as_tensor(frames)
-    original_shape = tensor.shape
-    tensor = tensor.reshape(-1, *original_shape[-3:]).permute(0, 3, 1, 2).float()
-    tensor = F.interpolate(tensor, size=(height, width), mode="bilinear", align_corners=False)
-    tensor = tensor.clamp(0, 255).byte().permute(0, 2, 3, 1)
-    return tensor.reshape(*original_shape[:-3], height, width, original_shape[-1]).cpu().numpy()
-
-
-def evaluate_from_dataset_with_progress(
-    world,
-    dataset: Any,
-    episodes_idx: Sequence[int],
-    start_steps: Sequence[int],
-    goal_offset_steps: int,
-    eval_budget: int,
-    callables: list[dict] | None = None,
-    save_video: bool = True,
-    video_path: str | Path = "./",
-):
-    """Copy of stable_worldmodel's dataset evaluation with tqdm progress bars."""
-    assert (
-        world.envs.envs[0].spec.max_episode_steps is None
-        or world.envs.envs[0].spec.max_episode_steps >= goal_offset_steps
-    ), "env max_episode_steps must be greater than eval_budget"
-
-    ep_idx_arr = np.array(episodes_idx)
-    start_steps_arr = np.array(start_steps)
-    end_steps = start_steps_arr + goal_offset_steps
-
-    if len(ep_idx_arr) != len(start_steps_arr):
-        raise ValueError("episodes_idx and start_steps must have the same length")
-    if len(ep_idx_arr) != world.num_envs:
-        raise ValueError("Number of episodes to evaluate must match number of envs")
-
-    data = dataset.load_chunk(ep_idx_arr, start_steps_arr, end_steps)
-    columns = dataset.column_names
-
-    init_step_per_env: dict[str, list[Any]] = defaultdict(list)
-    goal_step_per_env: dict[str, list[Any]] = defaultdict(list)
-    for ep in data:
-        for col in columns:
-            if col.startswith("goal"):
-                continue
-            if col.startswith("pixels"):
-                ep[col] = ep[col].permute(0, 2, 3, 1)
-            if not isinstance(ep[col], (torch.Tensor, np.ndarray)):
-                continue
-
-            init_data = ep[col][0]
-            goal_data = ep[col][-1]
-            if not isinstance(init_data, (np.ndarray, torch.Tensor)):
-                continue
-
-            init_data = init_data.numpy() if isinstance(init_data, torch.Tensor) else init_data
-            goal_data = goal_data.numpy() if isinstance(goal_data, torch.Tensor) else goal_data
-            init_step_per_env[col].append(init_data)
-            goal_step_per_env[col].append(goal_data)
-
-    init_step = {k: np.stack(v) for k, v in deepcopy(init_step_per_env).items()}
-    goal_step = {}
-    for key, value in goal_step_per_env.items():
-        goal_key = "goal" if key == "pixels" else f"goal_{key}"
-        goal_step[goal_key] = np.stack(value)
-
-    seeds = init_step.get("seed")
-    vkey = "variation."
-    variations_dict = {
-        k.removeprefix(vkey): v for k, v in init_step.items() if k.startswith(vkey)
-    }
-    options = [{} for _ in range(world.num_envs)]
-    if variations_dict:
-        for i in range(world.num_envs):
-            options[i]["variation"] = list(variations_dict.keys())
-            options[i]["variation_values"] = {k: v[i] for k, v in variations_dict.items()}
-
-    init_step.update(deepcopy(goal_step))
-    world.reset(seed=seeds, options=options)
-
-    callables = callables or []
-    for i, env in enumerate(_get_unwrapped_envs(world)):
-        env_unwrapped = env.unwrapped
-        for spec in callables:
-            method_name = spec["method"]
-            if not hasattr(env_unwrapped, method_name):
-                continue
-            method = getattr(env_unwrapped, method_name)
-            args = spec.get("args", spec)
-
-            prepared_args = {}
-            for args_name, args_data in args.items():
-                value = args_data.get("value", None)
-                is_in_dataset = args_data.get("in_dataset", True)
-                if is_in_dataset:
-                    if value not in init_step:
-                        continue
-                    prepared_args[args_name] = deepcopy(init_step[value][i])
-                else:
-                    prepared_args[args_name] = value
-
-            method(**prepared_args)
-
-    for i, env in enumerate(_get_unwrapped_envs(world)):
-        env_unwrapped = env.unwrapped
-        if "goal_state" in init_step and "goal_state" in goal_step:
-            assert np.array_equal(
-                init_step["goal_state"][i], goal_step["goal_state"][i]
-            ), f"Goal state info does not match at reset for env {env_unwrapped}"
-
-    _set_dataset_goal_xy(world, goal_step)
-
-    results: dict = {
-        "success_rate": 0.0,
-        "episode_successes": np.zeros(len(episodes_idx)),
-        "seeds": seeds,
-    }
-
-    shape_prefix = world.infos["pixels"].shape[:2]
-    init_step = {
-        k: np.broadcast_to(v[:, None, ...], shape_prefix + v.shape[1:]) for k, v in init_step.items()
-    }
-    goal_step = {
-        k: np.broadcast_to(v[:, None, ...], shape_prefix + v.shape[1:]) for k, v in goal_step.items()
-    }
-    _inject_static_goal_info_into_envs(world, goal_step)
-
-    world.infos.update(deepcopy(init_step))
-    world.infos.update(deepcopy(goal_step))
-
-    if "goal" in goal_step and "goal" in world.infos:
-        assert np.allclose(world.infos["goal"], goal_step["goal"]), "Goal info does not match"
-
-    target_frames = torch.stack([ep["pixels"] for ep in data]).numpy()
-    video_frames = None
-    video_height = video_width = None
-
-    env_label = getattr(world.envs.envs[0].spec, "id", "WorldModel Eval")
-    with tqdm(total=eval_budget, desc=f"Evaluating {env_label}", unit="step") as pbar:
-        for step_idx in range(eval_budget):
-            current_frame = world.infos["pixels"][:, -1]
-            if video_frames is None:
-                video_height, video_width = map(int, current_frame.shape[-3:-1])
-                video_frames = np.empty(
-                    (world.num_envs, eval_budget, *current_frame.shape[-3:]),
-                    dtype=np.uint8,
-                )
-                target_frames = _resize_hwc_frames(target_frames, video_height, video_width)
-            video_frames[:, step_idx] = _resize_hwc_frames(current_frame, video_height, video_width)
-            world.infos.update(deepcopy(goal_step))
-            world.step()
-            results["episode_successes"] = np.logical_or(
-                results["episode_successes"], world.terminateds
-            )
-            _set_vector_autoreset_flags(world)
-            success_count = int(np.sum(results["episode_successes"]))
-            pbar.set_postfix(success=f"{success_count}/{len(episodes_idx)}")
-            pbar.update(1)
-
-    if video_frames is None:
-        current_frame = world.infos["pixels"][:, -1]
-        video_height, video_width = map(int, current_frame.shape[-3:-1])
-        video_frames = np.empty(
-            (world.num_envs, eval_budget, *current_frame.shape[-3:]),
-            dtype=np.uint8,
-        )
-        target_frames = _resize_hwc_frames(target_frames, video_height, video_width)
-    video_frames[:, -1] = _resize_hwc_frames(world.infos["pixels"][:, -1], video_height, video_width)
-    n_episodes = len(episodes_idx)
-    results["success_rate"] = float(np.sum(results["episode_successes"])) / n_episodes * 100.0
-
-    if save_video:
-        import imageio
-
-        target_len = target_frames.shape[1]
-        video_path_obj = Path(video_path)
-        video_path_obj.mkdir(parents=True, exist_ok=True)
-        for i in tqdm(range(world.num_envs), desc="Writing videos", unit="video"):
-            out = imageio.get_writer(
-                video_path_obj / f"rollout_{i}.mp4",
-                fps=15,
-                codec="libx264",
-            )
-            goals = np.vstack([target_frames[i, -1], target_frames[i, -1]])
-            for t in range(eval_budget):
-                stacked_frame = np.vstack([video_frames[i, t], target_frames[i, t % target_len]])
-                frame = np.hstack([stacked_frame, goals])
-                out.append_data(frame)
-            out.close()
-        print(f"Video saved to {video_path_obj}")
-
-    if results["seeds"] is not None:
-        assert np.unique(results["seeds"]).shape[0] == n_episodes, "Some episode seeds are identical!"
-
-    return results
-
-@hydra.main(version_base=None, config_path="./config/eval", config_name="hyperbolic")
-def run(cfg: DictConfig):
-    """Run evaluation of dinowm vs random policy."""
-    assert (
-        cfg.plan_config.horizon * cfg.plan_config.action_block <= cfg.eval.eval_budget
-    ), "Planning horizon must be smaller than or equal to eval_budget"
-    print(f"[eval] mujoco backend: {MUJOCO_GL_BACKEND}", flush=True)
-
-    runtime_device = resolve_runtime_device(
-        cfg.solver.get("device", "auto"),
-        allow_fallback=True,
-    )
-    with open_dict(cfg):
-        cfg.solver.device = runtime_device
-        cfg.world.env_name = _resolve_world_env_name(str(cfg.world.env_name))
-
-    # create world environment
-    cfg.world.max_episode_steps = 2 * cfg.eval.eval_budget
-    world_image_shape = (
-        int(cfg.world.get("height", cfg.eval.img_size)),
-        int(cfg.world.get("width", cfg.eval.img_size)),
-    )
-    world = swm.World(**cfg.world, image_shape=world_image_shape)
-
-    # create the transform
-    transform = {
-        "pixels": img_transform(cfg),
-        "goal": img_transform(cfg),
-    }
-
-    dataset = get_dataset(cfg, cfg.eval.dataset_name)
-    stats_dataset = dataset  # get_dataset(cfg, cfg.dataset.stats)
-    col_name = "episode_idx" if "episode_idx" in dataset.column_names else "ep_idx"
-    ep_indices, _ = np.unique(stats_dataset.get_col_data(col_name), return_index=True)
-
-    process = {}
-    process_columns = cfg.dataset.get("keys_to_process", cfg.dataset.keys_to_cache)
-    for col in process_columns:
-        if col in ["pixels"]:
-            continue
-        processor = preprocessing.StandardScaler()
-        col_data = stats_dataset.get_col_data(col)
-        col_data = col_data[~np.isnan(col_data).any(axis=1)]
-        processor.fit(col_data)
-        process[col] = processor
-
-        if col != "action":
-            process[f"goal_{col}"] = process[col]
-
-    # -- run evaluation
-    policy = cfg.get("policy", "random")
-
-    if policy != "random":
-        model = load_policy_model(cfg, dataset, runtime_device)
-        planning_node = cfg.get("planning")
-        planning_cfg = (
-            OmegaConf.to_container(planning_node, resolve=True)
-            if planning_node is not None
-            else {}
-        )
-        if hasattr(model, "set_planning_config"):
-            model.set_planning_config(planning_cfg)
-            if isinstance(model, HyperbolicJEPA):
-                resolved_cfg = (
-                    model._planning_hyperbolic_config()
-                    if hasattr(model, "_planning_hyperbolic_config")
-                    else planning_cfg.get("hyperbolic", "<defaults>")
-                )
-                print(
-                    f"[eval] hyperbolic planning config: {resolved_cfg}",
-                    flush=True,
-                )
-        config = swm.PlanConfig(**cfg.plan_config)
-        solver = hydra.utils.instantiate(cfg.solver, model=model)
-        policy = swm.policy.WorldModelPolicy(
-            solver=solver, config=config, process=process, transform=transform
-        )
-
-    else:
-        policy = swm.policy.RandomPolicy()
-
-    results_path = (
-        Path(swm.data.utils.get_cache_dir(), cfg.policy).parent
-        if cfg.policy != "random"
-        else Path(__file__).parent
-    )
-
-    # sample the episodes and the starting indices
-    episode_len = get_episodes_length(dataset, ep_indices)
-    max_start_idx = episode_len - cfg.eval.goal_offset_steps - 1
-    max_start_idx_dict = {ep_id: max_start_idx[i] for i, ep_id in enumerate(ep_indices)}
-    # Map each dataset row’s episode_idx to its max_start_idx
-    col_name = "episode_idx" if "episode_idx" in dataset.column_names else "ep_idx"
-    safe_row_limit, row_counts = _resolve_safe_row_limit(dataset, [col_name, "step_idx"])
-    dataset_len = int(len(dataset))
-    unique_counts = sorted(set(row_counts.values()))
-    if len(unique_counts) > 1:
-        print(
-            f"[eval] warning: inconsistent row counts detected across dataset columns: "
-            f"{row_counts}. Dataset length is {dataset_len}; row-wise safe limit is {safe_row_limit}.",
-            flush=True,
-        )
-    episode_idx_col = np.asarray(dataset.get_col_data(col_name))
-    step_idx_col = np.asarray(dataset.get_col_data("step_idx"))
-    if len(episode_idx_col) != len(step_idx_col):
-        raise ValueError(
-            f"Dataset metadata is inconsistent: len({col_name})={len(episode_idx_col)} "
-            f"but len(step_idx)={len(step_idx_col)}."
-        )
-    episode_idx_col = episode_idx_col[:safe_row_limit]
-    step_idx_col = step_idx_col[:safe_row_limit]
-    max_start_per_row = np.array([max_start_idx_dict[ep_id] for ep_id in episode_idx_col])
-
-    # remove all the lines of dataset for which dataset['step_idx'] > max_start_per_row
-    valid_mask = step_idx_col <= max_start_per_row
-    valid_indices = np.nonzero(valid_mask)[0]
-    valid_indices = valid_indices[valid_indices < safe_row_limit]
-    print(valid_mask.sum(), "valid starting points found for evaluation.")
-
-    if len(valid_indices) < cfg.eval.num_eval:
-        raise ValueError(
-            f"Not enough valid starting points for evaluation: requested {cfg.eval.num_eval}, "
-            f"found {len(valid_indices)} after filtering against dataset length {dataset_len}."
-        )
-
-    g = np.random.default_rng(cfg.seed)
-    candidate_indices = g.permutation(valid_indices)
-    random_episode_indices = np.sort(
-        _select_valid_eval_rows(
-            dataset,
-            candidate_indices=candidate_indices,
-            episode_idx_col=episode_idx_col,
-            step_idx_col=step_idx_col,
-            goal_offset_steps=cfg.eval.goal_offset_steps,
-            num_eval=cfg.eval.num_eval,
-        )
-    )
-
-    print(random_episode_indices)
-
-    eval_episodes = episode_idx_col[random_episode_indices]
-    eval_start_idx = step_idx_col[random_episode_indices]
-
-    if len(eval_episodes) < cfg.eval.num_eval:
-        raise ValueError("Not enough episodes with sufficient length for evaluation.")
-
-    world.set_policy(policy)
-
-    start_time = time.time()
-    metrics = evaluate_from_dataset_with_progress(
-        world,
-        dataset,
-        start_steps=eval_start_idx.tolist(),
-        goal_offset_steps=cfg.eval.goal_offset_steps,
-        eval_budget=cfg.eval.eval_budget,
-        episodes_idx=eval_episodes.tolist(),
-        callables=OmegaConf.to_container(cfg.eval.get("callables"), resolve=True),
-        save_video=bool(cfg.eval.get("save_video", True)),
-        video_path=results_path,
-    )
-    end_time = time.time()
-    
-    print(metrics)
-
-    results_path = results_path / cfg.output.filename
-    results_path.parent.mkdir(parents=True, exist_ok=True)
-
-    with results_path.open("a") as f:
-        f.write("\n")  # separate from previous runs
-
-        f.write("==== CONFIG ====\n")
-        f.write(OmegaConf.to_yaml(cfg))
-        f.write("\n")
-
-        f.write("==== RESULTS ====\n")
-        f.write(f"metrics: {metrics}\n")
-        f.write(f"evaluation_time: {end_time - start_time} seconds\n")
-
-
-if __name__ == "__main__":
-    run()