feat: adapted simulate to trained config
This commit is contained in:
Jona Reynaert
parent
c4447976ab
commit
395b04d9a8
4 changed files with 334 additions and 30 deletions
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@ -4,13 +4,12 @@
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# Path to the trained model (optional)
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model_path: null
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# Type of model to use if no path is provided (e.g., random)
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model_type: "random"
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# Execution backend (MJX or BRAX)
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backend: "MJC"
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# Script behavior
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headless: false
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# In headless mode this is required; in viewer mode null means "infinite".
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max_steps: null
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# Optional: path to the Hydra config.yaml used during training.
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# When set, scripts/simulate.py will use it to default morphology/arena/environment/architecture
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# to match training (unless you explicitly override those keys via CLI).
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trained_config_path: null
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@ -3,6 +3,10 @@
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Uses Hydra to load the same BrittleStarConfig that was used during training.
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Override settings via CLI, e.g.:
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python scripts/simulate.py morphology=3_arms
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To replay a run using the *exact* Hydra config used during training, pass:
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python scripts/simulate.py simulation.trained_config_path=runs/.../.hydra/config.yaml \
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simulation.model_path=runs/.../final_model.flax
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"""
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from __future__ import annotations
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@ -17,11 +21,16 @@ import hydra
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import jax
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import jax.numpy as jnp
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import numpy as np
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from omegaconf import DictConfig, OmegaConf
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import yaml
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from omegaconf import DictConfig, OmegaConf, open_dict
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from brittle_star_project import BrittleStarEnv, BrittleStarEnvFactory
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from brittle_star_project import Backend, BrittleStarEnv, BrittleStarEnvFactory
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from brittle_star_project.configs.main_config import BrittleStarConfig
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from brittle_star_project.configs.register_configs import register_configs
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from brittle_star_project.environment.padded_obs_wrapper import (
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compute_padding_masks,
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pad_observation,
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)
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_ALLOWED_OBS_KEYS = {
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"joint_position",
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@ -36,6 +45,74 @@ _ALLOWED_OBS_KEYS = {
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"xy_distance_to_target",
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}
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def _dense_layer_sizes_from_params(params: Any) -> list[int]:
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"""Infer GenericDenseLayersWithActivation.layer_sizes from a Flax params tree."""
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try:
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dense_params = params["params"]
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except Exception as exc:
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raise ValueError("Unexpected sensor params structure (missing 'params')") from exc
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layer_sizes: list[int] = []
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idx = 0
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while True:
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key = f"Dense_{idx}"
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if key not in dense_params:
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break
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kernel = dense_params[key]["kernel"]
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layer_sizes.append(int(np.asarray(kernel).shape[1]))
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idx += 1
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if not layer_sizes:
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raise ValueError("Could not infer Dense_* layers from sensor params")
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return layer_sizes
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def _infer_action_dim_from_actor_params(params: Any) -> int | None:
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"""Best-effort infer action_dim from a Flax Actor params tree."""
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try:
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dense0 = params["params"]["Dense_0"]
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bias = dense0.get("bias")
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kernel = dense0.get("kernel")
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except Exception:
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return None
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if bias is not None:
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try:
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return int(np.asarray(bias).shape[0])
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except Exception:
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return None
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if kernel is not None:
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try:
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return int(np.asarray(kernel).shape[1])
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except Exception:
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return None
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return None
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def _has_cli_override(overrides: list[str], key: str) -> bool:
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prefixes = (f"{key}=", f"{key}.", f"+{key}=", f"+{key}.")
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return any(str(o).startswith(prefixes) for o in overrides)
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def _maybe_clip_action(
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action: np.ndarray,
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low: np.ndarray | None,
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high: np.ndarray | None,
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) -> np.ndarray:
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if low is None or high is None:
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return action
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low = np.asarray(low, dtype=np.float32).ravel()
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high = np.asarray(high, dtype=np.float32).ravel()
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if low.shape != action.shape or high.shape != action.shape:
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return action
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return np.clip(action, low, high)
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def _transform_obs_dict(obs_dict: dict[str, Any]) -> jnp.ndarray:
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"""Flatten the env's observation dict into a 1D vector.
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@ -69,9 +146,8 @@ class CleanRLPPOPolicy:
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) -> None:
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from brittle_star_project.MLPs.mlps import Actor, GenericDenseLayersWithActivation
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hidden_dim = int(sensor_params["params"]["Dense_0"]["kernel"].shape[1])
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self._sensor = GenericDenseLayersWithActivation(layer_sizes=[hidden_dim, hidden_dim])
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layer_sizes = _dense_layer_sizes_from_params(sensor_params)
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self._sensor = GenericDenseLayersWithActivation(layer_sizes=layer_sizes)
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self._actor = Actor(action_dim=action_dim)
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self._sensor_apply = jax.jit(self._sensor.apply)
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self._actor_apply = jax.jit(self._actor.apply)
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@ -156,6 +232,28 @@ class CleanRLPPOPolicy:
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feature_extractor_params,
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)
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# Accept a plain dict-shaped Flax params mapping commonly produced
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# by saving `agent_state.params` directly. Typical keys are
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# 'sensor_params' and 'actor_params', or sometimes nested under 'params'.
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if isinstance(restored_obj, dict):
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# Top-level params dict
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params_sub = restored_obj.get("params", {})
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sensor_params = restored_obj.get("sensor_params") or params_sub.get("sensor_params")
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actor_params = restored_obj.get("actor_params") or params_sub.get("actor_params")
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critic_params = restored_obj.get("critic_params") or params_sub.get("critic_params")
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feature_extractor_params = restored_obj.get(
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"feature_extractor_params"
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) or params_sub.get("feature_extractor_params")
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# Some checkpoints only save actor+sensor as top-level
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if sensor_params is not None and actor_params is not None:
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return (
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cfg_part,
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sensor_params,
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actor_params,
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critic_params,
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feature_extractor_params,
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)
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raise ValueError(
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f"Unexpected checkpoint structure in {path}. "
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"Expected [config_dict, [sensor_params, actor_params, critic_params, "
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@ -168,6 +266,16 @@ class CleanRLPPOPolicy:
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_parse_checkpoint(restored)
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)
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ckpt_action_dim = _infer_action_dim_from_actor_params(actor_params)
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if ckpt_action_dim is not None and ckpt_action_dim != action_dim:
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raise ValueError(
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"Checkpoint/env mismatch: "
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f"checkpoint expects action_dim={ckpt_action_dim}, "
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f"env provides action_dim={action_dim}. "
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"Use the same Hydra config (morphology/arena/environment) "
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"that was used during training."
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)
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return CleanRLPPOPolicy(
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sensor_params=sensor_params,
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actor_params=actor_params,
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@ -193,7 +301,7 @@ def _get_xy_distance_to_target(observations: dict[str, Any]) -> float | None:
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def _target_reached(*, state: Any) -> bool:
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return bool(getattr(state, "terminated", False))
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return bool(getattr(state, "terminated", False) or getattr(state, "truncated", False))
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def _rollout_one_episode_headless(
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@ -202,6 +310,9 @@ def _rollout_one_episode_headless(
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policy: CleanRLPPOPolicy,
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seed: int,
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max_steps: int,
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action_low: np.ndarray | None,
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action_high: np.ndarray | None,
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padding_masks: dict[str, Any] | None,
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) -> tuple[float, int, bool, float | None]:
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"""Run one rollout up to max_steps.
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@ -220,7 +331,12 @@ def _rollout_one_episode_headless(
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steps = 0
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for _ in range(int(max_steps)):
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action = policy.act(observations=observations)
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obs_dict = observations or {}
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if padding_masks is not None:
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obs_dict = pad_observation(obs_dict, padding_masks)
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action = policy.act(observations=obs_dict)
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action = _maybe_clip_action(action, action_low, action_high)
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nu = int(state.mj_model.nu)
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if nu > 0 and action.shape != (nu,):
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@ -251,6 +367,9 @@ def _run_one_episode_viewer(
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state: Any,
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control_dt: float,
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max_steps: int | None,
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action_low: np.ndarray | None,
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action_high: np.ndarray | None,
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padding_masks: dict[str, Any] | None,
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) -> None:
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import mujoco.viewer
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@ -272,7 +391,12 @@ def _run_one_episode_viewer(
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break
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step_start = time.time()
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action = policy.act(observations=observations or {})
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obs_dict = observations or {}
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if padding_masks is not None:
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obs_dict = pad_observation(obs_dict, padding_masks)
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action = policy.act(observations=obs_dict)
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action = _maybe_clip_action(action, action_low, action_high)
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if model.nu > 0 and action.shape != (int(model.nu),):
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raise ValueError(
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f"Policy returned action shape {action.shape}, expected ({int(model.nu)},)"
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@ -321,16 +445,126 @@ def _infer_checkpoint_obs_dim(policy: CleanRLPPOPolicy) -> int | None:
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return None
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def _load_trained_config(path: Path) -> DictConfig:
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"""Load a trained config YAML.
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Supports both:
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- Hydra's run config (e.g. runs/.../.hydra/config.yaml)
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- This project's logger metadata YAMLs, which may contain
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``!!python/object/apply:...`` tags for Enums.
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For safety, we *do not* execute Python constructors from YAML; we only
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treat these tags as data and extract their scalar arguments.
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"""
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if not path.exists():
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raise FileNotFoundError(f"trained_config_path does not exist: '{path}'.")
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if not path.is_file():
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raise ValueError(f"trained_config_path must be a file, got: '{path}'.")
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try:
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return OmegaConf.load(path)
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except Exception as exc:
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python_apply_prefix = "tag:yaml.org,2002:python/object/apply:"
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class _SafeLoaderWithPythonApply(yaml.SafeLoader):
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pass
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def _construct_python_apply(
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loader: yaml.SafeLoader,
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_tag_suffix: str,
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node: yaml.Node,
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) -> Any:
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if isinstance(node, yaml.SequenceNode):
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seq = loader.construct_sequence(node)
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if len(seq) == 1:
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return seq[0]
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return seq
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if isinstance(node, yaml.MappingNode):
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return loader.construct_mapping(node)
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return loader.construct_scalar(node)
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_SafeLoaderWithPythonApply.add_multi_constructor(
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python_apply_prefix, _construct_python_apply
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)
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try:
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data = yaml.load(path.read_text(encoding="utf-8"), Loader=_SafeLoaderWithPythonApply)
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except Exception as yaml_exc:
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raise ValueError(
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"Failed to load trained_config_path as YAML. "
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"If this is a Hydra run, pass the run's '.hydra/config.yaml' file. "
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f"Got: '{path}'."
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) from yaml_exc
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if not isinstance(data, dict):
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raise ValueError(
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"trained_config_path must contain a YAML mapping (dict-like) at the root. "
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f"Got type={type(data).__name__} from '{path}'."
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) from exc
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# Normalize known enum-like strings to their Enum *names* so OmegaConf's
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# structured config merge behaves like the normal Hydra config.
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from brittle_star_project.environment.env_types import Task
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env_cfg = data.get("environment")
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if isinstance(env_cfg, dict) and isinstance(env_cfg.get("task"), str):
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task_str = str(env_cfg["task"])
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try:
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env_cfg["task"] = Task[task_str].name
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except Exception:
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try:
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env_cfg["task"] = Task(task_str).name
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except Exception:
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pass
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return OmegaConf.create(data)
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@hydra.main(config_path="../configs", config_name="main_config", version_base="1.3")
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def main(dict_cfg: DictConfig) -> None:
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# Convert DictConfig to structured dataclass, ensuring the root schema is applied.
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config: BrittleStarConfig = OmegaConf.to_object(
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OmegaConf.merge(OmegaConf.structured(BrittleStarConfig), dict_cfg)
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)
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# Compose against the structured schema first, so missing keys are validated.
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cfg = OmegaConf.merge(OmegaConf.structured(BrittleStarConfig), dict_cfg)
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backend = config.simulation.backend
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# Optional: override env-defining sections (morphology/arena/environment/architecture)
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# using the exact Hydra config that was used for training.
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trained_cfg_path = cfg.simulation.trained_config_path
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if trained_cfg_path:
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overrides_raw = OmegaConf.select(cfg, "hydra.overrides.task") or []
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overrides = [str(o) for o in overrides_raw]
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trained_cfg_path_abs = Path(hydra.utils.to_absolute_path(trained_cfg_path))
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trained_cfg = _load_trained_config(trained_cfg_path_abs)
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if "hydra" in trained_cfg:
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with open_dict(trained_cfg):
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del trained_cfg["hydra"]
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with open_dict(cfg):
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for key in ("morphology", "arena", "environment", "architecture"):
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if key in trained_cfg and not _has_cli_override(overrides, key):
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base_node = OmegaConf.select(cfg, key)
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override_node = OmegaConf.select(trained_cfg, key)
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try:
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cfg[key] = OmegaConf.merge(base_node, override_node)
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except Exception as exc:
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raise ValueError(
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"Failed to merge trained config into the active Hydra config. "
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f"Key={key!r}, trained_config_path='{trained_cfg_path_abs}'."
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) from exc
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# Convert DictConfig to structured dataclass.
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config: BrittleStarConfig = OmegaConf.to_object(cfg)
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backend = Backend.MJC
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seed = int(config.experiment.seed)
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if getattr(config.architecture, "name", None) != "centralized":
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raise ValueError(
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"simulate.py currently only supports architecture=centralized. "
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f"Got architecture.name={getattr(config.architecture, 'name', None)!r}. "
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"(Training supports decentralized, but simulation wiring for it isn't implemented.)"
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)
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model_path_str = config.simulation.model_path
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if model_path_str is None:
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raise ValueError(
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@ -359,15 +593,28 @@ def main(dict_cfg: DictConfig) -> None:
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state0 = env.reset(seed=seed)
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# Match training's padded observation layout for amputated morphologies.
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padding_masks = compute_padding_masks(config.morphology.segments_per_arm)
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# Match training's action clipping behavior.
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action_space = getattr(raw_env, "action_space", None)
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action_low = (
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None if action_space is None else np.asarray(action_space.low, dtype=np.float32).ravel()
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)
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action_high = (
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None if action_space is None else np.asarray(action_space.high, dtype=np.float32).ravel()
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)
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# ======= MODEL SETUP =======
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nu = int(state0.mj_model.nu)
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policy = CleanRLPPOPolicy.load(model_path, action_dim=nu)
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# Helpful early failure when configs don't match the checkpoint.
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observations0 = _get_observations(state0)
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env_obs_dim = int(_transform_obs_dict(observations0 or {}).shape[0])
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obs0_dict = pad_observation(observations0 or {}, padding_masks)
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env_obs_dim = int(_transform_obs_dict(obs0_dict).shape[0])
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ckpt_obs_dim = _infer_checkpoint_obs_dim(policy)
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if ckpt_obs_dim is not None and ckpt_obs_dim != env_obs_dim:
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raise ValueError(
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"Checkpoint/env mismatch: "
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@ -375,7 +622,7 @@ def main(dict_cfg: DictConfig) -> None:
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"Use the same Hydra config (morphology/arena/environment) "
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"that was used during training."
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)
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# ======= SIMULATION =======
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headless = bool(config.simulation.headless)
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max_steps = config.simulation.max_steps
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@ -392,6 +639,9 @@ def main(dict_cfg: DictConfig) -> None:
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policy=policy,
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seed=seed,
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max_steps=max_steps_i,
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action_low=action_low,
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action_high=action_high,
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padding_masks=padding_masks,
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)
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final_dist_str = "n/a" if final_dist is None else f"{final_dist:.3f}"
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print(
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@ -418,6 +668,9 @@ def main(dict_cfg: DictConfig) -> None:
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state=state0,
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control_dt=control_dt,
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max_steps=max_steps_val,
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action_low=action_low,
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action_high=action_high,
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padding_masks=padding_masks,
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)
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env.close()
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@ -1,18 +1,19 @@
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from dataclasses import dataclass
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from typing import Optional
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from brittle_star_project.environment.env_types import Backend
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||||
@dataclass
|
||||
class SimulationSettings:
|
||||
"""Settings for the simulation script."""
|
||||
|
||||
model_path: Optional[str] = None
|
||||
model_type: str = "random"
|
||||
backend: Backend = Backend.MJC
|
||||
|
||||
# Script behavior
|
||||
headless: bool = False
|
||||
# If None, viewer mode runs until window closed or target reached.
|
||||
max_steps: Optional[int] = None
|
||||
|
||||
# Optional: point to a Hydra config.yaml from a training run (e.g. runs/.../.hydra/config.yaml).
|
||||
# When set, the simulation script can override
|
||||
# morphology/arena/environment/architecture to match.
|
||||
trained_config_path: Optional[str] = None
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ This logger ensures all experimental data is preserved by writing to:
|
|||
3. stdout (for real-time monitoring)
|
||||
"""
|
||||
|
||||
from enum import Enum
|
||||
import logging
|
||||
import yaml
|
||||
import sys
|
||||
|
|
@ -25,6 +26,33 @@ _active_logger: Optional[Any] = None
|
|||
_proxy_instance: Optional["LoggerProxy"] = None
|
||||
|
||||
|
||||
def _sanitize_for_yaml(obj: Any) -> Any:
|
||||
"""Convert non-primitive values into YAML-safe structures.
|
||||
|
||||
In particular, avoids PyYAML serializing Enums as
|
||||
``!!python/object/apply:...`` which OmegaConf will not load.
|
||||
"""
|
||||
|
||||
if isinstance(obj, Enum):
|
||||
return obj.name
|
||||
if isinstance(obj, Path):
|
||||
return str(obj)
|
||||
if isinstance(obj, (np.generic, jnp.ndarray)):
|
||||
try:
|
||||
return obj.item()
|
||||
except Exception:
|
||||
pass
|
||||
if isinstance(obj, np.ndarray):
|
||||
return obj.tolist()
|
||||
if isinstance(obj, dict):
|
||||
return {str(k): _sanitize_for_yaml(v) for k, v in obj.items()}
|
||||
if isinstance(obj, list):
|
||||
return [_sanitize_for_yaml(v) for v in obj]
|
||||
if isinstance(obj, tuple):
|
||||
return [_sanitize_for_yaml(v) for v in obj]
|
||||
return obj
|
||||
|
||||
|
||||
def get_logger() -> "LoggerProxy":
|
||||
"""Retrieve the global LoggerProxy.
|
||||
|
||||
|
|
@ -216,7 +244,13 @@ class UnifiedLogger:
|
|||
"""Save configuration to disk."""
|
||||
try:
|
||||
with open(self.config_file, "w") as f:
|
||||
yaml.dump(self.full_config, f, default_flow_style=False, indent=2, sort_keys=False)
|
||||
yaml.safe_dump(
|
||||
_sanitize_for_yaml(self.full_config),
|
||||
f,
|
||||
default_flow_style=False,
|
||||
indent=2,
|
||||
sort_keys=False,
|
||||
)
|
||||
self.info(f"Config saved to {self.config_file}")
|
||||
except Exception as e:
|
||||
self.error(f"Error saving config: {e}")
|
||||
|
|
@ -296,7 +330,12 @@ class UnifiedLogger:
|
|||
else:
|
||||
serializable_metric[k] = v
|
||||
f.write("---\n")
|
||||
yaml.dump(serializable_metric, f, default_flow_style=False)
|
||||
yaml.safe_dump(
|
||||
_sanitize_for_yaml(serializable_metric),
|
||||
f,
|
||||
default_flow_style=False,
|
||||
sort_keys=False,
|
||||
)
|
||||
self.metrics_buffer.clear()
|
||||
except Exception as e:
|
||||
self.error(f"Error flushing metrics: {e}")
|
||||
|
|
@ -321,7 +360,13 @@ class UnifiedLogger:
|
|||
if metadata:
|
||||
metadata_path = self.checkpoints_dir / f"{prefix}_step_{step}_metadata.yaml"
|
||||
with open(metadata_path, "w") as f:
|
||||
yaml.dump(metadata, f, default_flow_style=False)
|
||||
yaml.safe_dump(
|
||||
_sanitize_for_yaml(metadata),
|
||||
f,
|
||||
default_flow_style=False,
|
||||
indent=2,
|
||||
sort_keys=False,
|
||||
)
|
||||
|
||||
self.info(f"Checkpoint saved: {checkpoint_path}")
|
||||
|
||||
|
|
@ -357,7 +402,13 @@ class UnifiedLogger:
|
|||
if metadata:
|
||||
metadata_path = self.run_dir / "final_model_metadata.yaml"
|
||||
with open(metadata_path, "w") as f:
|
||||
yaml.dump(metadata, f, default_flow_style=False)
|
||||
yaml.safe_dump(
|
||||
_sanitize_for_yaml(metadata),
|
||||
f,
|
||||
default_flow_style=False,
|
||||
indent=2,
|
||||
sort_keys=False,
|
||||
)
|
||||
|
||||
self.info(f"Final model saved: {final_model_path}")
|
||||
|
||||
|
|
|
|||
Reference in a new issue