sunnypilot v2026.002.000

version: sunnypilot v2026.002.000 (staging)
date: 2026-07-21T07:44:02
master commit: 2c334ede443d7391d27575af8c854a095ba702a8

openpilot/sunnypilot/models/helpers.py

@@ -0,0 +1,207 @@
+"""
+Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
+
+This file is part of sunnypilot and is licensed under the MIT License.
+See the LICENSE.md file in the root directory for more details.
+"""
+
+import hashlib
+import os
+import pickle
+from pathlib import Path
+import numpy as np
+
+from openpilot.cereal import custom
+from openpilot.common.params import Params
+from openpilot.common.swaglog import cloudlog
+from openpilot.sunnypilot.models.constants import Meta, MetaSimPose, MetaTombRaider
+from openpilot.common.hardware.hw import Paths
+
+# SET ME TO THE EXACT JSON VERSION WE SET IN SUNNYPILOT_MODELS REPO
+REQUIRED_JSON_VERSION = 15
+
+CUSTOM_MODEL_PATH = Paths.model_root()
+METADATA_PATH = Path(__file__).parent / '../models/supercombo_metadata.pkl'
+ModelManager = custom.ModelManagerSP
+_LAST_VALIDATED_RAW = None
+
+
+def _compute_hash(file_path: str) -> str | None:
+  from openpilot.common.file_chunker import open_file_chunked
+  try:
+    with open_file_chunked(file_path) as file:
+      return hashlib.file_digest(file, "sha256").hexdigest().lower()
+  except FileNotFoundError:
+    return None
+
+
+async def verify_file(file_path: str, expected_hash: str) -> bool:
+  file_hash = _compute_hash(file_path)
+  return file_hash == expected_hash.lower() if file_hash else False
+
+
+def _verify_file(file_path: str, expected_hash: str) -> bool:
+  file_hash = _compute_hash(file_path)
+  return file_hash == expected_hash.lower() if file_hash else False
+
+
+def is_bundle_version_compatible(bundle: dict) -> bool:
+  """
+  The bundle parsed from the json specifies a `minimum_selector_version`, which defines the minimum selector version
+  required to load the model. This function ensures that:
+    the bundle MUST match the `REQUIRED_JSON_VERSION` set here in helpers.
+  """
+  return bundle.get("minimumSelectorVersion", 0) == REQUIRED_JSON_VERSION
+
+
+def _bundle_artifacts(bundle: custom.ModelManagerSP.ModelBundle) -> list[tuple[str, str]]:
+  artifacts = []
+  for model in getattr(bundle, 'models', []) or []:
+    for artifact in (getattr(model, 'artifact', None), getattr(model, 'metadata', None)):
+      if artifact and getattr(artifact, 'fileName', None) and getattr(artifact, 'downloadUri', None):
+        sha256 = getattr(artifact.downloadUri, 'sha256', None)
+        if sha256:
+          artifacts.append((artifact.fileName, sha256))
+  return artifacts
+
+
+def _bundle_is_valid_locally(bundle: custom.ModelManagerSP.ModelBundle) -> bool:
+  model_root = Paths.model_root()
+  return all(_verify_file(os.path.join(model_root, file_name), expected_hash)
+             for file_name, expected_hash in _bundle_artifacts(bundle))
+
+
+def _bundle_needs_reset(active_bundle: custom.ModelManagerSP.ModelBundle, available_bundles: list[custom.ModelManagerSP.ModelBundle] | None) -> bool:
+  if active_bundle is None:
+    return False
+
+  if available_bundles is not None:
+    matching_bundle = None
+    for bundle in available_bundles:
+      if getattr(active_bundle, 'ref', None) and getattr(bundle, 'ref', None):
+        if active_bundle.ref == bundle.ref:
+          matching_bundle = bundle
+          break
+      elif getattr(active_bundle, 'internalName', None) == getattr(bundle, 'internalName', None):
+        matching_bundle = bundle
+        break
+
+    if matching_bundle is None:
+      return True
+    if active_bundle.minimumSelectorVersion != matching_bundle.minimumSelectorVersion:
+      return True
+
+    active_runner = getattr(active_bundle, 'runner', None)
+    matching_runner = getattr(matching_bundle, 'runner', None)
+    if active_runner is not None and matching_runner is not None:
+      if getattr(active_runner, 'raw', active_runner) != getattr(matching_runner, 'raw', matching_runner):
+        return True
+    if set(_bundle_artifacts(active_bundle)) != set(_bundle_artifacts(matching_bundle)):
+      return True
+
+  return not _bundle_is_valid_locally(active_bundle)
+
+
+def validate_active_bundle(params: Params, available_bundles: list[custom.ModelManagerSP.ModelBundle] | None = None) -> None:
+  global _LAST_VALIDATED_RAW
+
+  raw_bundle = params.get("ModelManager_ActiveBundle")
+  if not raw_bundle:
+    return
+
+  if raw_bundle == _LAST_VALIDATED_RAW:
+    return
+
+  active_bundle = get_active_bundle(params, raw_bundle_dict=raw_bundle)
+  if active_bundle is None or _bundle_needs_reset(active_bundle, available_bundles):
+    cloudlog.warning("Active model bundle invalid; resetting to default")
+    params.remove("ModelManager_ActiveBundle")
+    params.put("ModelRunnerTypeCache", int(custom.ModelManagerSP.Runner.stock), block=True)
+    _LAST_VALIDATED_RAW = None
+  else:
+    _LAST_VALIDATED_RAW = raw_bundle
+
+
+def get_active_bundle(params: Params | None = None, raw_bundle_dict: dict | bytes | None = None) -> "custom.ModelManagerSP.ModelBundle | None":
+  params = params or Params()
+  try:
+    active_bundle_dict = raw_bundle_dict if raw_bundle_dict is not None else (params.get("ModelManager_ActiveBundle") or {})
+    if active_bundle_dict and is_bundle_version_compatible(active_bundle_dict):
+      return custom.ModelManagerSP.ModelBundle(**active_bundle_dict)
+  except Exception:
+    pass
+  return None
+
+
+def get_active_model_runner(params: Params | None = None, force_check: bool = False) -> int:
+  params = params or Params()
+  cached_runner_type = params.get("ModelRunnerTypeCache")
+  if cached_runner_type is not None and not force_check:
+    return cached_runner_type
+  runner_type = custom.ModelManagerSP.Runner.stock
+  if active_bundle := get_active_bundle(params):
+    runner_type = active_bundle.runner.raw
+
+  if cached_runner_type != runner_type:
+    params.put("ModelRunnerTypeCache", int(runner_type), block=True)
+
+  return runner_type
+
+
+def _get_model():
+  if bundle := get_active_bundle():
+    drive_model = next(model for model in bundle.models if model.type == ModelManager.Model.Type.supercombo)
+    return drive_model
+  return None
+
+
+def load_metadata():
+  model = _get_model()
+  metadata_path = f"{CUSTOM_MODEL_PATH}/{model.metadata.fileName}" if model else METADATA_PATH
+
+  with open(metadata_path, 'rb') as f:
+    return pickle.load(f)
+
+
+def prepare_inputs(model_metadata: dict) -> dict[str, np.ndarray]:
+  return {
+    key: np.zeros(shape, dtype=np.float32).flatten()
+    for key, shape in model_metadata['input_shapes'].items()
+    if 'img' not in key
+  }
+
+
+def load_meta_constants(model_metadata: dict):
+  """ Loads the appropriate meta model class based on key shapes"""
+  if 'sim_pose' in model_metadata['input_shapes']:
+    return MetaSimPose
+
+  meta_slice = model_metadata['output_slices']['meta']
+  if (meta_slice.start, meta_slice.stop, meta_slice.step) == (5868, 5921, None):
+    return MetaTombRaider
+
+  return Meta
+
+
+# The following method(s) are modeld helper methods
+def plan_x_idxs_helper(constants, plan, model_output) -> list[float]:
+  # times at X_IDXS according to plan.
+  LINE_T_IDXS = [np.nan] * constants.IDX_N
+  LINE_T_IDXS[0] = 0.0
+  plan_x = model_output['plan'][0, :, plan.POSITION][:, 0].tolist()
+  for xidx in range(1, constants.IDX_N):
+    tidx = 0
+    # increment tidx until we find an element that's further away than the current xidx
+    while tidx < constants.IDX_N - 1 and plan_x[tidx + 1] < constants.X_IDXS[xidx]:
+      tidx += 1
+    if tidx == constants.IDX_N - 1:
+      # if the plan doesn't extend far enough, set plan_t to the max value (10s), then break
+      LINE_T_IDXS[xidx] = constants.T_IDXS[constants.IDX_N - 1]
+      break
+    # interpolate to find `t` for the current xidx
+    current_x_val = plan_x[tidx]
+    next_x_val = plan_x[tidx + 1]
+    p = (constants.X_IDXS[xidx] - current_x_val) / (next_x_val - current_x_val) if abs(
+      next_x_val - current_x_val) > 1e-9 else float('nan')
+    LINE_T_IDXS[xidx] = p * constants.T_IDXS[tidx + 1] + (1 - p) * constants.T_IDXS[tidx]
+  return LINE_T_IDXS