sunnypilot v2026.002.000

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

openpilot/selfdrive/modeld/compile_dm_warp.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+import argparse
+import pickle
+import time
+
+from tinygrad.tensor import Tensor
+from tinygrad.device import Device
+from tinygrad.engine.jit import TinyJit
+
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+from openpilot.selfdrive.modeld.compile_modeld import NV12Frame, warp_perspective_tinygrad, _parse_size
+
+
+def make_warp_dm(nv12: NV12Frame, dm_w, dm_h):
+  cam_w, cam_h, stride, _, _, _ = nv12
+  stride_pad = stride - cam_w
+
+  def warp_dm(input_frame, M_inv):
+    M_inv = M_inv.to(Device.DEFAULT).realize()
+    return warp_perspective_tinygrad(input_frame[:cam_h*stride], M_inv,
+                                     (dm_w, dm_h), (cam_h, cam_w), stride_pad, border_fill_val=16).reshape(-1, dm_h * dm_w) # Y
+  return warp_dm
+
+
+def compile_dm_warp(nv12: NV12Frame, dm_w, dm_h, pkl_path):
+  print(f"Compiling DM warp for {nv12.width}x{nv12.height} -> {dm_w}x{dm_h}...")
+
+  warp_dm_jit = TinyJit(make_warp_dm(nv12, dm_w, dm_h), prune=True)
+
+  for i in range(10):
+    frame = Tensor.randint(nv12.size, low=0, high=256, dtype='uint8').realize()
+    M_inv = Tensor(Tensor.randn(3, 3).mul(8).realize().numpy(), device='NPY')
+    Device.default.synchronize()
+    st = time.perf_counter()
+    warp_dm_jit(frame, M_inv).realize()
+    mt = time.perf_counter()
+    Device.default.synchronize()
+    et = time.perf_counter()
+    print(f"  [{i+1}/10] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
+
+  with open(pkl_path, "wb") as f:
+    pickle.dump(warp_dm_jit, f)
+  print(f"  Saved to {pkl_path}")
+
+
+if __name__ == "__main__":
+  p = argparse.ArgumentParser()
+  p.add_argument('--camera-resolution', type=_parse_size, required=True, help='camera resolution WxH')
+  p.add_argument('--warp-to', type=_parse_size, required=True, help='DM input WxH')
+  p.add_argument('--output', required=True)
+  args = p.parse_args()
+
+  cam_w, cam_h = args.camera_resolution
+  nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
+  dm_w, dm_h = args.warp_to
+  compile_dm_warp(nv12, dm_w, dm_h, args.output)

openpilot/selfdrive/modeld/compile_modeld.py

@@ -0,0 +1,336 @@
+#!/usr/bin/env python3
+import argparse
+import atexit
+import math
+import os
+import pickle
+import tempfile
+import time
+import shutil
+from functools import partial
+from collections import namedtuple
+
+import numpy as np
+
+from openpilot.selfdrive.modeld.helpers import dump_oob, load_oob
+
+def _patch_tinygrad_fetch_fw():
+  import hashlib
+  import pathlib
+  import zstandard
+  from tinygrad import helpers
+  _orig = helpers.fetch_fw
+  def fetch_fw(path, name, sha256):
+    p = pathlib.Path(f"/lib/firmware/{path}/{name}.zst")
+    if p.is_file():
+      blob = zstandard.ZstdDecompressor().stream_reader(p.read_bytes()).read()
+      if hashlib.sha256(blob).hexdigest() == sha256:
+        return blob
+    return _orig(path, name, sha256)
+  helpers.fetch_fw = fetch_fw
+_patch_tinygrad_fetch_fw()
+
+def _patch_tinygrad_buffer_reduce():
+  from tinygrad.device import Buffer
+  def __reduce_ex__(self, protocol):
+    buf = None
+    if self._base is not None:
+      return self.__class__, (self.device, self.size, self.dtype, None, None, None, 0, self.base, self.offset, self.is_allocated())
+    if self.device == "NPY":
+      return self.__class__, (self.device, self.size, self.dtype, self._buf, self.options, None, self.uop_refcount)
+    if self.is_allocated():
+      buf = bytearray(self.nbytes)
+      self.copyout(memoryview(buf))
+      if protocol >= 5:
+        buf = pickle.PickleBuffer(buf)
+    return self.__class__, (self.device, self.size, self.dtype, None, self.options, buf, self.uop_refcount)
+  Buffer.__reduce_ex__ = __reduce_ex__
+_patch_tinygrad_buffer_reduce()
+
+from tinygrad.tensor import Tensor
+from tinygrad.helpers import Context
+from tinygrad.device import Device
+from tinygrad.engine.jit import TinyJit
+
+
+NV12Frame = namedtuple("NV12Frame", ['width', 'height', 'stride', 'y_height', 'uv_height', 'size'])
+WARP_INPUTS = ['tfm', 'big_tfm']
+POLICY_INPUTS = ['img_q', 'big_img_q', 'feat_q', 'desire_q', 'packed_npy_inputs']
+
+UV_SCALE_MATRIX = np.array([[0.5, 0, 0], [0, 0.5, 0], [0, 0, 1]], dtype=np.float32)
+UV_SCALE_MATRIX_INV = np.linalg.inv(UV_SCALE_MATRIX)
+
+WARP_DEV = os.getenv('WARP_DEV')
+
+
+def make_random_images(keys, shape, device=None):
+  return {k: Tensor.randint(shape, low=0, high=256, dtype='uint8', device=device).realize() for k in keys}
+
+
+def warp_perspective_tinygrad(src_flat, M_inv, dst_shape, src_shape, stride_pad, border_fill_val=None):
+  w_dst, h_dst = dst_shape
+  h_src, w_src = src_shape
+
+  x = Tensor.arange(w_dst).reshape(1, w_dst).expand(h_dst, w_dst).reshape(-1)
+  y = Tensor.arange(h_dst).reshape(h_dst, 1).expand(h_dst, w_dst).reshape(-1)
+
+  # inline 3x3 matmul as elementwise to avoid reduce op (enables fusion with gather)
+  src_x = M_inv[0, 0] * x + M_inv[0, 1] * y + M_inv[0, 2]
+  src_y = M_inv[1, 0] * x + M_inv[1, 1] * y + M_inv[1, 2]
+  src_w = M_inv[2, 0] * x + M_inv[2, 1] * y + M_inv[2, 2]
+
+  src_x = src_x / src_w
+  src_y = src_y / src_w
+
+  x_round = Tensor.round(src_x)
+  y_round = Tensor.round(src_y)
+  x_nn_clipped = x_round.clip(0, w_src - 1).cast('int')
+  y_nn_clipped = y_round.clip(0, h_src - 1).cast('int')
+  idx = y_nn_clipped * (w_src + stride_pad) + x_nn_clipped
+  sampled = src_flat[idx]
+
+  if border_fill_val is None:
+    return sampled
+
+  in_bounds = ((x_round >= 0) & (x_round <= w_src - 1) &
+               (y_round >= 0) & (y_round <= h_src - 1)).cast(sampled.dtype)
+  return sampled * in_bounds + Tensor(border_fill_val, dtype=sampled.dtype) * (1 - in_bounds)
+
+
+def frames_to_tensor(frames):
+  H = (frames.shape[0] * 2) // 3
+  W = frames.shape[1]
+  in_img1 = Tensor.cat(frames[0:H:2, 0::2],
+                       frames[1:H:2, 0::2],
+                       frames[0:H:2, 1::2],
+                       frames[1:H:2, 1::2],
+                       frames[H:H+H//4].reshape((H//2, W//2)),
+                       frames[H+H//4:H+H//2].reshape((H//2, W//2)), dim=0).reshape((6, H//2, W//2))
+  return in_img1
+
+
+def make_frame_prepare(nv12: NV12Frame, model_w, model_h):
+  cam_w, cam_h, stride, y_height, uv_height, _ = nv12
+  uv_offset = stride * y_height
+  stride_pad = stride - cam_w
+
+  def frame_prepare_tinygrad(input_frame, M_inv):
+    # UV_SCALE @ M_inv @ UV_SCALE_INV simplifies to elementwise scaling
+    M_inv_uv = M_inv * Tensor([[1.0, 1.0, 0.5], [1.0, 1.0, 0.5], [2.0, 2.0, 1.0]], device=WARP_DEV)
+    # deinterleave NV12 UV plane (UVUV... -> separate U, V)
+    uv = input_frame[uv_offset:uv_offset + uv_height * stride].reshape(uv_height, stride)
+    with Context(SPLIT_REDUCEOP=0):
+      y = warp_perspective_tinygrad(input_frame[:cam_h*stride],
+                                    M_inv, (model_w, model_h),
+                                    (cam_h, cam_w), stride_pad).realize()
+      u = warp_perspective_tinygrad(uv[:cam_h//2, :cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+      v = warp_perspective_tinygrad(uv[:cam_h//2, 1:cam_w:2].flatten(),
+                                    M_inv_uv, (model_w//2, model_h//2),
+                                    (cam_h//2, cam_w//2), 0).realize()
+    yuv = y.cat(u).cat(v).reshape((model_h * 3 // 2, model_w))
+    tensor = frames_to_tensor(yuv)
+    return tensor
+  return frame_prepare_tinygrad
+
+
+def make_warp_input_queues(vision_input_shapes, frame_skip, device):
+  img = vision_input_shapes['img']  # (1, 12, 128, 256)
+  n_frames = img[1] // 6
+  img_buf_shape = (frame_skip * (n_frames - 1) + 1, 6, img[2], img[3])
+
+  npy = {
+    'tfm': np.zeros((3, 3), dtype=np.float32),
+    'big_tfm': np.zeros((3, 3), dtype=np.float32),
+  }
+  input_queues = {
+    'img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+    'big_img_q': Tensor(np.zeros(img_buf_shape, dtype=np.uint8), device=device).contiguous().realize(),
+    **{k: Tensor(v, device='NPY').realize() for k, v in npy.items()},
+  }
+  return input_queues, npy
+
+
+def get_policy_npy_shapes(input_shapes):
+  dp = input_shapes['desire_pulse']  # (1, 25, 8)
+  tc = input_shapes['traffic_convention']  # (1, 2)
+  at = input_shapes['action_t']  # (1, 2)
+  fb = input_shapes['features_buffer']  # (1, 24, 512)
+  # TODO prev_feat shouldn't exist and be handled inside the JIT, but corrupt on QCOM for now
+  shapes = {'desire': (dp[2],), 'traffic_convention': tuple(tc), 'action_t': tuple(at), 'prev_feat': (fb[0], fb[2])}
+  return shapes, [math.prod(s) for s in shapes.values()]
+
+
+def make_input_queues(input_shapes, frame_skip, device):
+  input_queues, npy = make_warp_input_queues(input_shapes, frame_skip, device)
+
+  fb = input_shapes['features_buffer']  # (1, 24, 512), past features only; the model appends the current frame's feature
+  dp = input_shapes['desire_pulse']  # (1, 25, 8)
+
+  shapes, sizes = get_policy_npy_shapes(input_shapes)
+  packed_npy_inputs = np.zeros(sum(sizes), dtype=np.float32)
+  # views into the packed inputs, to be refilled at runtime
+  npy.update({k: v.reshape(s) for (k, s), v in zip(shapes.items(), np.split(packed_npy_inputs, np.cumsum(sizes[:-1])), strict=True)})
+  input_queues.update({
+    'feat_q': Tensor(np.zeros((frame_skip * fb[1], fb[0], fb[2]), dtype=np.float32), device=device).contiguous().realize(),
+    'desire_q': Tensor(np.zeros((frame_skip * dp[1], dp[0], dp[2]), dtype=np.float32), device=device).contiguous().realize(),
+    'packed_npy_inputs': Tensor(packed_npy_inputs, device='NPY').realize(),
+  })
+  return input_queues, npy
+
+
+def shift_and_sample(buf, new_val, sample_fn):
+  buf.assign(buf[1:].cat(new_val, dim=0).contiguous())
+  return sample_fn(buf)
+
+
+def sample_skip(buf, frame_skip):
+  return buf[::frame_skip].contiguous().flatten(0, 1).unsqueeze(0)
+
+
+def sample_desire(buf, frame_skip):
+  return buf.reshape(-1, frame_skip, *buf.shape[1:]).max(1).flatten(0, 1).unsqueeze(0)
+
+
+def make_warp(nv12, model_w, model_h, frame_skip):
+  frame_prepare = make_frame_prepare(nv12, model_w, model_h)
+
+  def warp(tfm, big_tfm, frame, big_frame):
+    tfm = tfm.to(WARP_DEV)
+    big_tfm = big_tfm.to(WARP_DEV)
+    Tensor.realize(tfm, big_tfm)
+
+    warped_frame = frame_prepare(frame, tfm).unsqueeze(0)
+    warped_big_frame = frame_prepare(big_frame, big_tfm).unsqueeze(0)
+    return Tensor.cat(warped_frame, warped_big_frame)
+
+  return warp
+
+
+def make_run_policy(model_runner, model_metadata, frame_skip):
+  sample_desire_fn = partial(sample_desire, frame_skip=frame_skip)
+  sample_skip_fn = partial(sample_skip, frame_skip=frame_skip)
+  npy_shapes, npy_sizes = get_policy_npy_shapes(model_metadata['input_shapes'])
+
+  def run_policy(warped, img_q, big_img_q, feat_q, desire_q, packed_npy_inputs):
+    packed_npy_inputs = packed_npy_inputs.to(Device.DEFAULT)
+    warped = warped.to(Device.DEFAULT)
+    Tensor.realize(packed_npy_inputs, warped)
+
+    img = shift_and_sample(img_q, warped[0:1], sample_skip_fn)
+    big_img = shift_and_sample(big_img_q, warped[1:2], sample_skip_fn)
+
+    desire, traffic_convention, action_t, prev_feat = (t.reshape(s) for t, s in zip(packed_npy_inputs.split(npy_sizes), npy_shapes.values(), strict=True))
+    desire_buf = shift_and_sample(desire_q, desire.reshape(1, 1, -1), sample_desire_fn)
+    feat_buf = shift_and_sample(feat_q, prev_feat.reshape(1, 1, -1), sample_skip_fn)
+
+    inputs = {
+      'img': img,
+      'big_img': big_img,
+      'features_buffer': feat_buf,
+      'desire_pulse': desire_buf,
+      'traffic_convention': traffic_convention,
+      'action_t': action_t,
+    }
+    out = next(iter(model_runner(inputs).values())).cast('float32')
+    return out,
+  return run_policy
+
+
+def compile_jit(jit, make_random_inputs, input_keys, make_queues):
+  SEED = 42
+  def random_inputs_run(fn, seed, test_val=None, test_buffers=None, expect_match=True):
+    input_queues, npy = make_queues(Device.DEFAULT)
+    np.random.seed(seed)
+    Tensor.manual_seed(seed)
+
+    testing = test_val is not None or test_buffers is not None
+    n_runs = 1 if testing else 3
+
+    for i in range(n_runs):
+      for v in npy.values():
+        v[:] = np.random.randn(*v.shape).astype(v.dtype)
+      Device.default.synchronize()
+      random_inputs = make_random_inputs()
+      st = time.perf_counter()
+      outs = fn(**{k: input_queues[k] for k in input_keys}, **random_inputs)
+      mt = time.perf_counter()
+      Device.default.synchronize()
+      et = time.perf_counter()
+      print(f"  [{i+1}/{n_runs}] enqueue {(mt-st)*1e3:6.2f} ms -- total {(et-st)*1e3:6.2f} ms")
+
+      if i == 0:
+        val = [np.copy(v.numpy()) for v in outs]
+        buffers = [np.copy(v.numpy().copy()) for v in input_queues.values()]
+
+    if test_val is not None:
+      match = all(np.array_equal(a, b) for a, b in zip(val, test_val, strict=True))
+      assert match == expect_match, f"outputs {'differ from' if expect_match else 'match'} baseline (seed={seed})"
+    if test_buffers is not None:
+      match = all(np.array_equal(a, b) for a, b in zip(buffers, test_buffers, strict=True))
+      assert match == expect_match, f"buffers {'differ from' if expect_match else 'match'} baseline (seed={seed})"
+    return val, buffers
+
+  print('capture + replay')
+  test_val, test_buffers = random_inputs_run(jit, SEED)
+  print('pickle round trip')
+  with tempfile.TemporaryFile(dir=".") as f:
+    dump_oob(jit, f)
+    f.seek(0)
+    jit = load_oob(f)
+  random_inputs_run(jit, SEED, test_val, test_buffers, expect_match=True)
+  random_inputs_run(jit, SEED+1, test_val, test_buffers, expect_match=False)
+  return jit
+
+
+def _parse_size(s):
+  w, h = s.lower().split('x')
+  return int(w), int(h)
+
+
+def read_file_chunked_to_disk(path):
+  from openpilot.common.file_chunker import open_file_chunked
+  tmp_path = f'{path}.unchunked'
+  with open(tmp_path, 'wb') as f, open_file_chunked(path) as src:
+    shutil.copyfileobj(src, f)
+  atexit.register(lambda: os.path.exists(tmp_path) and os.remove(tmp_path))
+  return tmp_path
+
+
+if __name__ == "__main__":
+  from tinygrad.nn.onnx import OnnxRunner
+  from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+  from openpilot.selfdrive.modeld.get_model_metadata import make_metadata_dict
+  p = argparse.ArgumentParser()
+  p.add_argument('--model-size', type=_parse_size, required=True, help='model input WxH')
+  p.add_argument('--camera-resolutions', type=_parse_size, nargs='+', required=True,
+                 help='camera resolutions WxH (one or more)')
+  p.add_argument('--onnx', required=True)
+  p.add_argument('--output', required=True)
+  p.add_argument('--frame-skip', type=int, required=True)
+  args = p.parse_args()
+
+  model_path = read_file_chunked_to_disk(args.onnx)
+  model_w, model_h = args.model_size
+
+  model_runner = OnnxRunner(model_path)
+  out = {'metadata': make_metadata_dict(model_path)}
+
+  run_policy_jit = TinyJit(make_run_policy(model_runner, out['metadata'], args.frame_skip), prune=True)
+
+  make_policy_queues = partial(make_input_queues, out['metadata']['input_shapes'], args.frame_skip)
+  make_random_model_inputs = partial(make_random_images, keys=['warped'], shape=(2, 6, *out['metadata']['input_shapes']['img'][2:]), device=WARP_DEV)
+  out['run_policy'] = compile_jit(run_policy_jit, make_random_model_inputs, POLICY_INPUTS,
+                                  make_policy_queues)
+
+  for cam_w, cam_h in args.camera_resolutions:
+    nv12 = NV12Frame(cam_w, cam_h, *get_nv12_info(cam_w, cam_h))
+    make_random_warp_inputs = partial(make_random_images, keys=['frame', 'big_frame'], shape=nv12.size, device=WARP_DEV)
+    warp = TinyJit(make_warp(nv12, model_w, model_h, args.frame_skip), prune=True)
+    make_warp_queues = partial(make_warp_input_queues, out['metadata']['input_shapes'], args.frame_skip)
+    out[(cam_w,cam_h)] = compile_jit(warp, make_random_warp_inputs, WARP_INPUTS, make_warp_queues)
+
+  with open(args.output, "wb") as f:
+    dump_oob(out, f)
+  print(f"Saved JITs to {args.output} ({os.path.getsize(args.output) / 1e6:.2f} MB)")

openpilot/selfdrive/modeld/constants.py

@@ -0,0 +1,88 @@
+import numpy as np
+
+def index_function(idx, max_val=192, max_idx=32):
+  return (max_val) * ((idx/max_idx)**2)
+
+class ModelConstants:
+  # time and distance indices
+  IDX_N = 33
+  T_IDXS = [index_function(idx, max_val=10.0) for idx in range(IDX_N)]
+  X_IDXS = [index_function(idx, max_val=192.0) for idx in range(IDX_N)]
+  LEAD_T_IDXS = [0., 2., 4., 6., 8., 10.]
+  LEAD_T_OFFSETS = [0., 2., 4.]
+  META_T_IDXS = [2., 4., 6., 8., 10.]
+
+  # model inputs constants
+  N_FRAMES = 2
+  MODEL_RUN_FREQ = 20
+  MODEL_CONTEXT_FREQ = 5 # "model_trained_fps"
+
+  FEATURE_LEN = 512
+
+  DESIRE_LEN = 8
+  TRAFFIC_CONVENTION_LEN = 2
+  LAT_PLANNER_STATE_LEN = 4
+  LATERAL_CONTROL_PARAMS_LEN = 2
+  PREV_DESIRED_CURV_LEN = 1
+
+  # model outputs constants
+  FCW_THRESHOLDS_5MS2 = np.array([.05, .05, .15, .15, .15], dtype=np.float32)
+  FCW_THRESHOLDS_3MS2 = np.array([.7, .7], dtype=np.float32)
+  FCW_5MS2_PROBS_WIDTH = 5
+  FCW_3MS2_PROBS_WIDTH = 2
+
+  DISENGAGE_WIDTH = 5
+  POSE_WIDTH = 6
+  WIDE_FROM_DEVICE_WIDTH = 3
+  LEAD_WIDTH = 4
+  LANE_LINES_WIDTH = 2
+  ROAD_EDGES_WIDTH = 2
+  PLAN_WIDTH = 15
+  ACTION_WIDTH = 2
+  DESIRE_PRED_WIDTH = 8
+  LAT_PLANNER_SOLUTION_WIDTH = 4
+  DESIRED_CURV_WIDTH = 1
+
+  NUM_LANE_LINES = 4
+  NUM_ROAD_EDGES = 2
+
+  LEAD_TRAJ_LEN = 6
+  DESIRE_PRED_LEN = 4
+
+  PLAN_MHP_N = 5
+  LEAD_MHP_N = 2
+  PLAN_MHP_SELECTION = 1
+  LEAD_MHP_SELECTION = 3
+
+  FCW_THRESHOLD_5MS2_HIGH = 0.15
+  FCW_THRESHOLD_5MS2_LOW = 0.05
+  FCW_THRESHOLD_3MS2 = 0.7
+
+  CONFIDENCE_BUFFER_LEN = 5
+  RYG_GREEN = 0.01165
+  RYG_YELLOW = 0.06157
+
+  POLY_PATH_DEGREE = 4
+
+# model outputs slices
+class Plan:
+  POSITION = slice(0, 3)
+  VELOCITY = slice(3, 6)
+  ACCELERATION = slice(6, 9)
+  T_FROM_CURRENT_EULER = slice(9, 12)
+  ORIENTATION_RATE = slice(12, 15)
+
+class Meta:
+  ENGAGED = slice(0, 1)
+  # next 2, 4, 6, 8, 10 seconds
+  GAS_DISENGAGE = slice(1, 31, 6)
+  BRAKE_DISENGAGE = slice(2, 31, 6)
+  STEER_OVERRIDE = slice(3, 31, 6)
+  HARD_BRAKE_3 = slice(4, 31, 6)
+  HARD_BRAKE_4 = slice(5, 31, 6)
+  HARD_BRAKE_5 = slice(6, 31, 6)
+  # next 0, 2, 4, 6, 8, 10 seconds
+  GAS_PRESS = slice(31, 55, 4)
+  BRAKE_PRESS = slice(32, 55, 4)
+  LEFT_BLINKER = slice(33, 55, 4)
+  RIGHT_BLINKER = slice(34, 55, 4)

openpilot/selfdrive/modeld/dmonitoringmodeld.py

@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+import os
+from openpilot.selfdrive.modeld.helpers import MODELS_DIR, get_tg_input_devices
+from tinygrad.tensor import Tensor
+import time
+import pickle
+import numpy as np
+
+from openpilot.cereal import messaging
+from openpilot.cereal.messaging import PubMaster, SubMaster
+from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
+from openpilot.common.swaglog import cloudlog
+from openpilot.common.realtime import config_realtime_process
+from openpilot.common.transformations.model import dmonitoringmodel_intrinsics
+from openpilot.common.transformations.camera import _ar_ox_fisheye, _os_fisheye
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+from openpilot.common.file_chunker import open_file_chunked
+from openpilot.selfdrive.modeld.parse_model_outputs import sigmoid, safe_exp
+
+PROCESS_NAME = "openpilot.selfdrive.modeld.dmonitoringmodeld"
+SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
+MODEL_PKL_PATH = MODELS_DIR / 'dmonitoring_model_tinygrad.pkl'
+METADATA_PATH = MODELS_DIR / 'dmonitoring_model_metadata.pkl'
+
+
+class ModelState:
+  inputs: dict[str, np.ndarray]
+  output: np.ndarray
+
+  def __init__(self, cam_w: int, cam_h: int):
+    self.DEV = get_tg_input_devices(PROCESS_NAME, usbgpu=False)['DEV']
+    with open(METADATA_PATH, 'rb') as f:
+      model_metadata = pickle.load(f)
+      self.input_shapes = model_metadata['input_shapes']
+      self.output_slices = model_metadata['output_slices']
+
+    self.numpy_inputs = {
+      'calib': np.zeros(self.input_shapes['calib'], dtype=np.float32),
+    }
+
+    self.warp_inputs_np = {'transform': np.zeros((3,3), dtype=np.float32)}
+    self.warp_inputs = {k: Tensor(v, device='NPY') for k,v in self.warp_inputs_np.items()}
+    self.frame_buf_params = get_nv12_info(cam_w, cam_h)
+    self.tensor_inputs = {k: Tensor(v, device='NPY').realize() for k,v in self.numpy_inputs.items()}
+    self._blob_cache : dict[int, Tensor] = {}
+    self.model_run = pickle.load(open_file_chunked(str(MODEL_PKL_PATH)))
+    with open(MODELS_DIR / f'dm_warp_{cam_w}x{cam_h}_tinygrad.pkl', "rb") as f:
+      self.image_warp = pickle.load(f)
+
+  def run(self, buf: VisionBuf, calib: np.ndarray, transform: np.ndarray) -> tuple[np.ndarray, float]:
+    self.numpy_inputs['calib'][0,:] = calib
+
+    t1 = time.perf_counter()
+
+    ptr = np.frombuffer(buf.data, dtype=np.uint8).ctypes.data
+    # There is a ringbuffer of imgs, just cache tensors pointing to all of them
+    if ptr not in self._blob_cache:
+      self._blob_cache[ptr] = Tensor.from_blob(ptr, (self.frame_buf_params[3],), dtype='uint8', device=self.DEV)
+
+    self.warp_inputs_np['transform'][:] = transform[:]
+    self.tensor_inputs['input_img'] = self.image_warp(self._blob_cache[ptr], self.warp_inputs['transform'])
+
+    output = self.model_run(**self.tensor_inputs).numpy().flatten()
+
+    t2 = time.perf_counter()
+    return output, t2 - t1
+
+def slice_outputs(model_outputs, output_slices):
+  return  {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
+
+def parse_model_output(model_output):
+  parsed = {}
+  parsed['wheel_on_right'] = sigmoid(model_output['wheel_on_right'])
+  for ds_suffix in ['lhd', 'rhd']:
+    face_descs = model_output[f'face_descs_{ds_suffix}']
+    parsed[f'face_descs_{ds_suffix}'] = face_descs[:, :-6]
+    parsed[f'face_descs_{ds_suffix}_std'] = safe_exp(face_descs[:, -6:])
+    for key in ['face_prob', 'left_eye_prob', 'right_eye_prob','left_blink_prob', 'right_blink_prob', 'sunglasses_prob', 'using_phone_prob', 'sleep_prob']:
+      parsed[f'{key}_{ds_suffix}'] = sigmoid(model_output[f'{key}_{ds_suffix}'])
+  return parsed
+
+def fill_driver_data(msg, model_output, ds_suffix):
+  msg.faceOrientation = model_output[f'face_descs_{ds_suffix}'][0, :3].tolist()
+  msg.faceOrientationStd = model_output[f'face_descs_{ds_suffix}_std'][0, :3].tolist()
+  msg.facePosition = model_output[f'face_descs_{ds_suffix}'][0, 3:5].tolist()
+  msg.facePositionStd = model_output[f'face_descs_{ds_suffix}_std'][0, 3:5].tolist()
+  msg.faceProb = model_output[f'face_prob_{ds_suffix}'][0, 0].item()
+  msg.leftEyeProb = model_output[f'left_eye_prob_{ds_suffix}'][0, 0].item()
+  msg.rightEyeProb = model_output[f'right_eye_prob_{ds_suffix}'][0, 0].item()
+  msg.leftBlinkProb = model_output[f'left_blink_prob_{ds_suffix}'][0, 0].item()
+  msg.rightBlinkProb = model_output[f'right_blink_prob_{ds_suffix}'][0, 0].item()
+  msg.sunglassesProb = model_output[f'sunglasses_prob_{ds_suffix}'][0, 0].item()
+  msg.phoneProb = model_output[f'using_phone_prob_{ds_suffix}'][0, 0].item()
+  msg.sleepProb = model_output[f'sleep_prob_{ds_suffix}'][0, 0].item()
+
+def get_driverstate_packet(model_output, frame_id: int, location_ts: int, exec_time: float, gpu_exec_time: float):
+  msg = messaging.new_message('driverStateV2', valid=True)
+  ds = msg.driverStateV2
+  ds.frameId = frame_id
+  ds.modelExecutionTime = exec_time
+  ds.gpuExecutionTime = gpu_exec_time
+  ds.rawPredictions = model_output['raw_pred']
+  ds.wheelOnRightProb = model_output['wheel_on_right'][0, 0].item()
+  fill_driver_data(ds.leftDriverData, model_output, 'lhd')
+  fill_driver_data(ds.rightDriverData, model_output, 'rhd')
+  return msg
+
+
+def main():
+  config_realtime_process(7, 5)
+
+  cloudlog.warning("connecting to driver stream")
+  vipc_client = VisionIpcClient("camerad", VisionStreamType.VISION_STREAM_DRIVER, True)
+  while not vipc_client.connect(False):
+    time.sleep(0.1)
+  assert vipc_client.is_connected()
+  cloudlog.warning(f"connected with buffer size: {vipc_client.buffer_len}")
+
+  model = ModelState(vipc_client.width, vipc_client.height)
+  cloudlog.warning("models loaded, dmonitoringmodeld starting")
+
+  sm = SubMaster(["liveCalibration"])
+  pm = PubMaster(["driverStateV2"])
+
+  calib = np.zeros(model.numpy_inputs['calib'].size, dtype=np.float32)
+  model_transform = None
+
+  while True:
+    buf = vipc_client.recv()
+    if buf is None:
+      continue
+
+    if model_transform is None:
+      cam = _os_fisheye if buf.width == _os_fisheye.width else _ar_ox_fisheye
+      model_transform = np.linalg.inv(np.dot(dmonitoringmodel_intrinsics, np.linalg.inv(cam.intrinsics))).astype(np.float32)
+
+    sm.update(0)
+    if sm.updated["liveCalibration"]:
+      calib[:] = np.array(sm["liveCalibration"].rpyCalib)
+
+    t1 = time.perf_counter()
+    model_output, gpu_execution_time = model.run(buf, calib, model_transform)
+    t2 = time.perf_counter()
+    raw_pred = model_output.tobytes() if SEND_RAW_PRED else b''
+    model_output = slice_outputs(model_output, model.output_slices)
+    model_output = parse_model_output(model_output)
+    model_output['raw_pred'] = raw_pred
+    msg = get_driverstate_packet(model_output, vipc_client.frame_id, vipc_client.timestamp_sof, t2 - t1, gpu_execution_time)
+    pm.send("driverStateV2", msg)
+
+
+if __name__ == "__main__":
+  try:
+    main()
+  except KeyboardInterrupt:
+    cloudlog.warning("got SIGINT")

openpilot/selfdrive/modeld/fill_model_msg.py

@@ -0,0 +1,192 @@
+import os
+import capnp
+import numpy as np
+from openpilot.cereal import log
+from openpilot.selfdrive.modeld.constants import ModelConstants, Plan, Meta
+from openpilot.sunnypilot.models.helpers import plan_x_idxs_helper
+
+SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
+
+ConfidenceClass = log.ModelDataV2.ConfidenceClass
+
+
+class PublishState:
+  def __init__(self):
+    self.disengage_buffer = np.zeros(ModelConstants.CONFIDENCE_BUFFER_LEN*ModelConstants.DISENGAGE_WIDTH, dtype=np.float32)
+    self.prev_brake_5ms2_probs = np.zeros(ModelConstants.FCW_5MS2_PROBS_WIDTH, dtype=np.float32)
+    self.prev_brake_3ms2_probs = np.zeros(ModelConstants.FCW_3MS2_PROBS_WIDTH, dtype=np.float32)
+
+def fill_xyzt(builder, t, x, y, z, x_std=None, y_std=None, z_std=None):
+  builder.t = t
+  builder.x = x.tolist()
+  builder.y = y.tolist()
+  builder.z = z.tolist()
+  if x_std is not None:
+    builder.xStd = x_std.tolist()
+  if y_std is not None:
+    builder.yStd = y_std.tolist()
+  if z_std is not None:
+    builder.zStd = z_std.tolist()
+
+def fill_xyvat(builder, t, x, y, v, a, x_std=None, y_std=None, v_std=None, a_std=None):
+  builder.t = t
+  builder.x = x.tolist()
+  builder.y = y.tolist()
+  builder.v = v.tolist()
+  builder.a = a.tolist()
+  if x_std is not None:
+    builder.xStd = x_std.tolist()
+  if y_std is not None:
+    builder.yStd = y_std.tolist()
+  if v_std is not None:
+    builder.vStd = v_std.tolist()
+  if a_std is not None:
+    builder.aStd = a_std.tolist()
+
+def fill_xyz_poly(builder, degree, x, y, z):
+  xyz = np.stack([x, y, z], axis=1)
+  coeffs = np.polynomial.polynomial.polyfit(ModelConstants.T_IDXS, xyz, deg=degree)
+  builder.xCoefficients = coeffs[:, 0].tolist()
+  builder.yCoefficients = coeffs[:, 1].tolist()
+  builder.zCoefficients = coeffs[:, 2].tolist()
+
+def fill_lane_line_meta(builder, lane_lines, lane_line_probs):
+  builder.leftY = lane_lines[1].y[0]
+  builder.leftProb = lane_line_probs[1]
+  builder.rightY = lane_lines[2].y[0]
+  builder.rightProb = lane_line_probs[2]
+
+def fill_driving_model_data(msg: capnp._DynamicStructBuilder, modelv2_send: capnp._DynamicStructBuilder) -> None:
+  msg.valid = modelv2_send.valid
+  modelV2 = modelv2_send.modelV2
+  driving_model_data = msg.drivingModelData
+  driving_model_data.frameId = modelV2.frameId
+  driving_model_data.frameIdExtra = modelV2.frameIdExtra
+  driving_model_data.frameDropPerc = modelV2.frameDropPerc
+  driving_model_data.modelExecutionTime = modelV2.modelExecutionTime
+  driving_model_data.action = modelV2.action
+  driving_model_data.meta.laneChangeState = modelV2.meta.laneChangeState
+  driving_model_data.meta.laneChangeDirection = modelV2.meta.laneChangeDirection
+  fill_lane_line_meta(driving_model_data.laneLineMeta, modelV2.laneLines, modelV2.laneLineProbs)
+  fill_xyz_poly(driving_model_data.path, ModelConstants.POLY_PATH_DEGREE, modelV2.position.x, modelV2.position.y, modelV2.position.z)
+
+def fill_model_msg(msg: capnp._DynamicStructBuilder, net_output_data: dict[str, np.ndarray], action: log.ModelDataV2.Action,
+                   publish_state: PublishState, vipc_frame_id: int, vipc_frame_id_extra: int,
+                   frame_id: int, frame_drop: float, timestamp_eof: int, model_execution_time: float,
+                   valid: bool) -> None:
+  frame_age = frame_id - vipc_frame_id if frame_id > vipc_frame_id else 0
+  frame_drop_perc = frame_drop * 100
+  msg.valid = valid
+
+  modelV2 = msg.modelV2
+  modelV2.frameId = vipc_frame_id
+  modelV2.frameIdExtra = vipc_frame_id_extra
+  modelV2.frameAge = frame_age
+  modelV2.frameDropPerc = frame_drop_perc
+  modelV2.timestampEof = timestamp_eof
+  modelV2.modelExecutionTime = model_execution_time
+
+  # plan
+  fill_xyzt(modelV2.position, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.POSITION].T, *net_output_data['plan_stds'][0,:,Plan.POSITION].T)
+  fill_xyzt(modelV2.velocity, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.VELOCITY].T)
+  fill_xyzt(modelV2.acceleration, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.ACCELERATION].T)
+  fill_xyzt(modelV2.orientation, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.T_FROM_CURRENT_EULER].T)
+  fill_xyzt(modelV2.orientationRate, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.ORIENTATION_RATE].T)
+
+  # action
+  modelV2.action = action
+
+  # times at X_IDXS of edges and lines
+  LINE_T_IDXS: list[float] = plan_x_idxs_helper(ModelConstants, Plan, net_output_data)
+
+  # lane lines
+  modelV2.init('laneLines', 4)
+  for i in range(4):
+    lane_line = modelV2.laneLines[i]
+    fill_xyzt(lane_line, LINE_T_IDXS, np.array(ModelConstants.X_IDXS), net_output_data['lane_lines'][0,i,:,0], net_output_data['lane_lines'][0,i,:,1])
+  modelV2.laneLineStds = net_output_data['lane_lines_stds'][0,:,0,0].tolist()
+  modelV2.laneLineProbs = net_output_data['lane_lines_prob'][0,1::2].tolist()
+
+  # road edges
+  modelV2.init('roadEdges', 2)
+  for i in range(2):
+    road_edge = modelV2.roadEdges[i]
+    fill_xyzt(road_edge, LINE_T_IDXS, np.array(ModelConstants.X_IDXS), net_output_data['road_edges'][0,i,:,0], net_output_data['road_edges'][0,i,:,1])
+  modelV2.roadEdgeStds = net_output_data['road_edges_stds'][0,:,0,0].tolist()
+
+  # leads
+  modelV2.init('leadsV3', 3)
+  for i in range(3):
+    lead = modelV2.leadsV3[i]
+    fill_xyvat(lead, ModelConstants.LEAD_T_IDXS, *net_output_data['lead'][0,i].T, *net_output_data['lead_stds'][0,i].T)
+    lead.prob = net_output_data['lead_prob'][0,i].tolist()
+    lead.probTime = ModelConstants.LEAD_T_OFFSETS[i]
+
+  # meta
+  meta = modelV2.meta
+  meta.desireState = net_output_data['desire_state'][0].reshape(-1).tolist()
+  meta.desirePrediction = net_output_data['desire_pred'][0].reshape(-1).tolist()
+  meta.engagedProb = net_output_data['meta'][0,Meta.ENGAGED].item()
+  meta.init('disengagePredictions')
+  disengage_predictions = meta.disengagePredictions
+  disengage_predictions.t = ModelConstants.META_T_IDXS
+  disengage_predictions.brakeDisengageProbs = net_output_data['meta'][0,Meta.BRAKE_DISENGAGE].tolist()
+  disengage_predictions.gasDisengageProbs = net_output_data['meta'][0,Meta.GAS_DISENGAGE].tolist()
+  disengage_predictions.steerOverrideProbs = net_output_data['meta'][0,Meta.STEER_OVERRIDE].tolist()
+  disengage_predictions.brake3MetersPerSecondSquaredProbs = net_output_data['meta'][0,Meta.HARD_BRAKE_3].tolist()
+  disengage_predictions.brake4MetersPerSecondSquaredProbs = net_output_data['meta'][0,Meta.HARD_BRAKE_4].tolist()
+  disengage_predictions.brake5MetersPerSecondSquaredProbs = net_output_data['meta'][0,Meta.HARD_BRAKE_5].tolist()
+  disengage_predictions.gasPressProbs = net_output_data['meta'][0,Meta.GAS_PRESS].tolist()
+  disengage_predictions.brakePressProbs = net_output_data['meta'][0,Meta.BRAKE_PRESS].tolist()
+
+  publish_state.prev_brake_5ms2_probs[:-1] = publish_state.prev_brake_5ms2_probs[1:]
+  publish_state.prev_brake_5ms2_probs[-1] = net_output_data['meta'][0,Meta.HARD_BRAKE_5][0]
+  publish_state.prev_brake_3ms2_probs[:-1] = publish_state.prev_brake_3ms2_probs[1:]
+  publish_state.prev_brake_3ms2_probs[-1] = net_output_data['meta'][0,Meta.HARD_BRAKE_3][0]
+  hard_brake_predicted = (publish_state.prev_brake_5ms2_probs > ModelConstants.FCW_THRESHOLDS_5MS2).all() and \
+    (publish_state.prev_brake_3ms2_probs > ModelConstants.FCW_THRESHOLDS_3MS2).all()
+  meta.hardBrakePredicted = hard_brake_predicted.item()
+
+  # confidence
+  if vipc_frame_id % (2*ModelConstants.MODEL_RUN_FREQ) == 0:
+    # any disengage prob
+    brake_disengage_probs = net_output_data['meta'][0,Meta.BRAKE_DISENGAGE]
+    gas_disengage_probs = net_output_data['meta'][0,Meta.GAS_DISENGAGE]
+    steer_override_probs = net_output_data['meta'][0,Meta.STEER_OVERRIDE]
+    any_disengage_probs = 1-((1-brake_disengage_probs)*(1-gas_disengage_probs)*(1-steer_override_probs))
+    # independent disengage prob for each 2s slice
+    ind_disengage_probs = np.r_[any_disengage_probs[0], np.diff(any_disengage_probs) / (1 - any_disengage_probs[:-1])]
+    # rolling buf for 2, 4, 6, 8, 10s
+    publish_state.disengage_buffer[:-ModelConstants.DISENGAGE_WIDTH] = publish_state.disengage_buffer[ModelConstants.DISENGAGE_WIDTH:]
+    publish_state.disengage_buffer[-ModelConstants.DISENGAGE_WIDTH:] = ind_disengage_probs
+
+  score = 0.
+  for i in range(ModelConstants.DISENGAGE_WIDTH):
+    score += publish_state.disengage_buffer[i*ModelConstants.DISENGAGE_WIDTH+ModelConstants.DISENGAGE_WIDTH-1-i].item() / ModelConstants.DISENGAGE_WIDTH
+  if score < ModelConstants.RYG_GREEN:
+    modelV2.confidence = ConfidenceClass.green
+  elif score < ModelConstants.RYG_YELLOW:
+    modelV2.confidence = ConfidenceClass.yellow
+  else:
+    modelV2.confidence = ConfidenceClass.red
+
+  # raw prediction if enabled
+  if SEND_RAW_PRED:
+    modelV2.rawPredictions = net_output_data['raw_pred'].tobytes()
+
+def fill_pose_msg(msg: capnp._DynamicStructBuilder, net_output_data: dict[str, np.ndarray],
+                  vipc_frame_id: int, vipc_dropped_frames: int, timestamp_eof: int, live_calib_seen: bool) -> None:
+  msg.valid = live_calib_seen & (vipc_dropped_frames < 1)
+  cameraOdometry = msg.cameraOdometry
+
+  cameraOdometry.frameId = vipc_frame_id
+  cameraOdometry.timestampEof = timestamp_eof
+
+  cameraOdometry.trans = net_output_data['pose'][0,:3].tolist()
+  cameraOdometry.rot = net_output_data['pose'][0,3:].tolist()
+  cameraOdometry.wideFromDeviceEuler = net_output_data['wide_from_device_euler'][0,:].tolist()
+  cameraOdometry.roadTransformTrans = net_output_data['road_transform'][0,:3].tolist()
+  cameraOdometry.transStd = net_output_data['pose_stds'][0,:3].tolist()
+  cameraOdometry.rotStd = net_output_data['pose_stds'][0,3:].tolist()
+  cameraOdometry.wideFromDeviceEulerStd = net_output_data['wide_from_device_euler_stds'][0,:].tolist()
+  cameraOdometry.roadTransformTransStd = net_output_data['road_transform_stds'][0,:3].tolist()

openpilot/selfdrive/modeld/get_model_metadata.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+import sys
+import pathlib
+import codecs
+import pickle
+from typing import Any
+
+from tinygrad.nn.onnx import OnnxPBParser
+
+
+class MetadataOnnxPBParser(OnnxPBParser):
+  def _parse_ModelProto(self) -> dict:
+    obj: dict[str, Any] = {"graph": {"input": [], "output": []}, "metadata_props": []}
+    for fid, wire_type in self._parse_message(self.reader.len):
+      match fid:
+        case 7:
+          obj["graph"] = self._parse_GraphProto()
+        case 14:
+          obj["metadata_props"].append(self._parse_StringStringEntryProto())
+        case _:
+          self.reader.skip_field(wire_type)
+    return obj
+
+
+def get_name_and_shape(value_info: dict[str, Any]) -> tuple[str, tuple[int, ...]]:
+  shape = tuple(int(dim) if isinstance(dim, int) else 0 for dim in value_info["parsed_type"].shape)
+  name = value_info["name"]
+  return name, shape
+
+
+def get_metadata_value_by_name(model: dict[str, Any], name: str) -> str | Any:
+  for prop in model["metadata_props"]:
+    if prop["key"] == name:
+      return prop["value"]
+  return None
+
+
+def make_metadata_dict(model_path):
+  model = MetadataOnnxPBParser(model_path).parse()
+  output_slices = get_metadata_value_by_name(model, 'output_slices')
+  assert output_slices is not None, 'output_slices not found in metadata'
+  return {
+    'model_checkpoint': get_metadata_value_by_name(model, 'model_checkpoint'),
+    'output_slices': pickle.loads(codecs.decode(output_slices.encode(), "base64")),
+    'input_shapes': dict(get_name_and_shape(x) for x in model["graph"]["input"]),
+    'output_shapes': dict(get_name_and_shape(x) for x in model["graph"]["output"]),
+  }
+
+
+if __name__ == "__main__":
+  model_path = pathlib.Path(sys.argv[1])
+  metadata_path = model_path.parent / (model_path.stem + '_metadata.pkl')
+  with open(metadata_path, 'wb') as f:
+    pickle.dump(make_metadata_dict(model_path), f)
+  print(f'saved metadata to {metadata_path}')

openpilot/selfdrive/modeld/helpers.py

@@ -0,0 +1,59 @@
+import io
+import json
+import pickle
+import shutil
+import struct
+import tempfile
+from pathlib import Path
+
+MODELS_DIR = Path(__file__).resolve().parent / 'models'
+TG_INPUT_DEVICES_PATH = MODELS_DIR / 'tg_input_devices.json'
+USBGPU_VID = 0xADD1
+USBGPU_PID = 0x0001
+
+
+def get_tg_input_devices(process_name: str, usbgpu: bool):
+  with open(TG_INPUT_DEVICES_PATH) as f:
+    return json.load(f)[process_name]['default' if not usbgpu else 'usbgpu']
+
+def modeld_pkl_path(usbgpu: bool):
+  prefix = 'big_' if usbgpu else ''
+  return MODELS_DIR / f'{prefix}driving_tinygrad.pkl'
+
+def dump_oob(obj, f):
+  with tempfile.TemporaryFile(dir=".") as tmp:
+    def buffer_callback(pb: pickle.PickleBuffer):
+      m = pb.raw()
+      tmp.write(struct.pack('<q', m.nbytes))
+      tmp.write(m)
+      pb.release() # keep peak ram at ~1 buffer
+    stream = io.BytesIO()
+    pickle.Pickler(stream, protocol=5, buffer_callback=buffer_callback).dump(obj)
+    opcodes = stream.getvalue()
+    f.write(struct.pack('<q', len(opcodes)))
+    f.write(opcodes)
+    tmp.seek(0)
+    shutil.copyfileobj(tmp, f)
+
+def load_oob(f):
+  opcodes = f.read(struct.unpack('<q', f.read(8))[0])
+  def buffers():
+    prev = None
+    while (h := f.read(8)):
+      if prev is not None:
+        prev.release()
+      buf = bytearray(struct.unpack('<q', h)[0])
+      f.readinto(buf)
+      prev = pickle.PickleBuffer(buf)
+      yield prev
+  return pickle.load(io.BytesIO(opcodes), buffers=buffers())
+
+def usbgpu_present() -> bool:
+  for d in Path("/sys/bus/usb/devices").glob("*"):
+    try:
+      if int((d / "idVendor").read_text(), 16) == USBGPU_VID and \
+          int((d / "idProduct").read_text(), 16) == USBGPU_PID:
+        return True
+    except Exception:
+      pass
+  return False

openpilot/selfdrive/modeld/modeld.py

@@ -0,0 +1,335 @@
+#!/usr/bin/env python3
+import os
+os.environ['GMMU'] = '0' # for usbgpu fast loading, noop for qcom
+from tinygrad.tensor import Tensor
+import time
+import numpy as np
+import openpilot.cereal.messaging as messaging
+from openpilot.cereal import log
+from opendbc.car.structs import car
+from openpilot.cereal.messaging import PubMaster, SubMaster
+from msgq.visionipc import VisionIpcClient, VisionStreamType, VisionBuf
+from opendbc.car.car_helpers import get_demo_car_params
+from openpilot.common.swaglog import cloudlog
+from openpilot.common.params import Params
+from openpilot.common.filter_simple import FirstOrderFilter
+from openpilot.common.realtime import config_realtime_process, DT_MDL
+from openpilot.common.transformations.camera import DEVICE_CAMERAS
+from openpilot.system.camerad.cameras.nv12_info import get_nv12_info
+from openpilot.common.transformations.model import get_warp_matrix
+from openpilot.selfdrive.controls.lib.desire_helper import DesireHelper
+from openpilot.selfdrive.controls.lib.drive_helpers import get_accel_from_plan, smooth_value, get_curvature_from_plan
+from openpilot.selfdrive.modeld.parse_model_outputs import Parser
+from openpilot.selfdrive.modeld.compile_modeld import make_input_queues, WARP_INPUTS, POLICY_INPUTS
+from openpilot.selfdrive.modeld.fill_model_msg import fill_model_msg, fill_driving_model_data, fill_pose_msg, PublishState
+from openpilot.common.file_chunker import open_file_chunked, get_manifest_path
+from openpilot.selfdrive.modeld.constants import ModelConstants, Plan
+from openpilot.selfdrive.modeld.helpers import usbgpu_present, modeld_pkl_path, get_tg_input_devices, load_oob
+
+from openpilot.sunnypilot.livedelay.helpers import get_lat_delay
+from openpilot.sunnypilot.modeld_v2.modeld_base import ModelStateBase
+
+PROCESS_NAME = "openpilot.selfdrive.modeld.modeld"
+SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
+
+LAT_SMOOTH_SECONDS = 0.0
+LONG_SMOOTH_SECONDS = 0.3
+MIN_LAT_CONTROL_SPEED = 0.3
+
+
+def get_action_from_model(model_output: dict[str, np.ndarray], prev_action: log.ModelDataV2.Action,
+                          lat_action_t: float, long_action_t: float, v_ego: float) -> log.ModelDataV2.Action:
+  if 'action' not in model_output:
+    plan = model_output['plan'][0]
+    desired_accel, should_stop = get_accel_from_plan(plan[:,Plan.VELOCITY][:,0],
+                                                     plan[:,Plan.ACCELERATION][:,0],
+                                                     ModelConstants.T_IDXS,
+                                                     action_t=long_action_t)
+    desired_curvature = get_curvature_from_plan(plan[:,Plan.T_FROM_CURRENT_EULER][:,2],
+                                                plan[:,Plan.ORIENTATION_RATE][:,2],
+                                                ModelConstants.T_IDXS,
+                                                v_ego,
+                                                lat_action_t)
+  else:
+    desired_accel = model_output['action'][0,1]
+    desired_curvature = model_output['action'][0,0] / (max(1.0, v_ego))**2
+    should_stop = (v_ego < 0.3 and desired_accel < 0.1)
+  desired_accel = smooth_value(desired_accel, prev_action.desiredAcceleration, LONG_SMOOTH_SECONDS)
+  if v_ego > MIN_LAT_CONTROL_SPEED:
+    desired_curvature = smooth_value(desired_curvature, prev_action.desiredCurvature, LAT_SMOOTH_SECONDS)
+  else:
+    desired_curvature = prev_action.desiredCurvature
+
+  return log.ModelDataV2.Action(desiredCurvature=float(desired_curvature),
+                                desiredAcceleration=float(desired_accel),
+                                shouldStop=bool(should_stop))
+
+
+class FrameMeta:
+  frame_id: int = 0
+  timestamp_sof: int = 0
+  timestamp_eof: int = 0
+
+  def __init__(self, vipc=None):
+    if vipc is not None:
+      self.frame_id, self.timestamp_sof, self.timestamp_eof = vipc.frame_id, vipc.timestamp_sof, vipc.timestamp_eof
+
+
+class ModelState(ModelStateBase):
+  prev_desire: np.ndarray  # for tracking the rising edge of the pulse
+
+  def __init__(self, cam_w: int, cam_h: int, usbgpu: bool):
+    ModelStateBase.__init__(self)
+    self.LAT_SMOOTH_SECONDS = LAT_SMOOTH_SECONDS
+    input_devices = get_tg_input_devices(PROCESS_NAME, usbgpu)
+    self.WARP_DEV, self.QUEUE_DEV = input_devices['WARP_DEV'], input_devices['QUEUE_DEV']
+    jits = load_oob(open_file_chunked(modeld_pkl_path(usbgpu)))
+    metadata = jits['metadata']
+    self.input_shapes = metadata['input_shapes']
+    self.vision_input_names = [k for k in self.input_shapes if 'img' in k]
+    self.output_slices = metadata['output_slices']
+
+    self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
+
+    self.frame_skip = ModelConstants.MODEL_RUN_FREQ // ModelConstants.MODEL_CONTEXT_FREQ
+    self.input_queues, self.npy = make_input_queues(self.input_shapes, self.frame_skip, device=self.QUEUE_DEV)
+    self.full_frames: dict[str, Tensor] = {}
+    self._blob_cache: dict[int, Tensor] = {}
+    self.parser = Parser()
+    self.frame_buf_params = {k: get_nv12_info(cam_w, cam_h) for k in ('img', 'big_img')}
+    self.run_policy = jits['run_policy']
+    self.warp = jits[(cam_w,cam_h)]
+
+  def slice_outputs(self, model_outputs: np.ndarray, output_slices: dict[str, slice]) -> dict[str, np.ndarray]:
+    parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
+    return parsed_model_outputs
+
+  def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
+          inputs: dict[str, np.ndarray]) -> dict[str, np.ndarray] | None:
+    for key in bufs.keys():
+      ptr = np.frombuffer(bufs[key].data, dtype=np.uint8).ctypes.data
+      yuv_size = self.frame_buf_params[key][3]
+      # There is a ringbuffer of imgs, just cache tensors pointing to all of them
+      cache_key = (key, ptr)
+      if cache_key not in self._blob_cache:
+        self._blob_cache[cache_key] = Tensor.from_blob(ptr, (yuv_size,), dtype='uint8', device=self.WARP_DEV)
+      self.full_frames[key] = self._blob_cache[cache_key]
+
+    # Model decides when action is completed, so desire input is just a pulse triggered on rising edge
+    inputs['desire_pulse'][0] = 0
+    self.npy['desire'][:] = np.where(inputs['desire_pulse'] - self.prev_desire > .99, inputs['desire_pulse'], 0)
+    self.prev_desire[:] = inputs['desire_pulse']
+    self.npy['traffic_convention'][:] = inputs['traffic_convention']
+    self.npy['action_t'][:] = inputs['action_t']
+    self.npy['tfm'][:,:] = transforms['img'][:,:]
+    self.npy['big_tfm'][:,:] = transforms['big_img'][:,:]
+
+    warped = self.warp(**{k: self.input_queues[k] for k in WARP_INPUTS}, frame=self.full_frames['img'], big_frame=self.full_frames['big_img'])
+
+    outs, = self.run_policy(
+      **{k: self.input_queues[k] for k in POLICY_INPUTS if k in self.input_queues}, warped=warped
+    )
+    model_output = outs.numpy()[0]
+    outputs_dict = self.parser.parse_outputs(self.slice_outputs(model_output, self.output_slices))
+    self.npy['prev_feat'][:] = model_output[self.output_slices['hidden_state']]
+
+    if SEND_RAW_PRED:
+      outputs_dict['raw_pred'] = model_output.copy()
+    return outputs_dict
+
+
+def main(demo=False):
+  cloudlog.warning("modeld init")
+
+  _present = usbgpu_present()
+  _compiled = os.path.isfile(get_manifest_path(modeld_pkl_path(usbgpu=True)))
+  USBGPU = _present and _compiled
+  params = Params()
+  params.put_bool("UsbGpuPresent", _present)
+  params.put_bool("UsbGpuCompiled", _compiled)
+
+  config_realtime_process(7, 54)
+
+  # visionipc clients
+  while True:
+    available_streams = VisionIpcClient.available_streams("camerad", block=False)
+    if available_streams:
+      use_extra_client = VisionStreamType.VISION_STREAM_WIDE_ROAD in available_streams and VisionStreamType.VISION_STREAM_ROAD in available_streams
+      main_wide_camera = VisionStreamType.VISION_STREAM_ROAD not in available_streams
+      break
+    time.sleep(.1)
+
+  vipc_client_main_stream = VisionStreamType.VISION_STREAM_WIDE_ROAD if main_wide_camera else VisionStreamType.VISION_STREAM_ROAD
+  vipc_client_main = VisionIpcClient("camerad", vipc_client_main_stream, True)
+  vipc_client_extra = VisionIpcClient("camerad", VisionStreamType.VISION_STREAM_WIDE_ROAD, False)
+  cloudlog.warning(f"vision stream set up, main_wide_camera: {main_wide_camera}, use_extra_client: {use_extra_client}")
+
+  while not vipc_client_main.connect(False):
+    time.sleep(0.1)
+  while use_extra_client and not vipc_client_extra.connect(False):
+    time.sleep(0.1)
+
+  cloudlog.warning(f"connected main cam with buffer size: {vipc_client_main.buffer_len} ({vipc_client_main.width} x {vipc_client_main.height})")
+  if use_extra_client:
+    cloudlog.warning(f"connected extra cam with buffer size: {vipc_client_extra.buffer_len} ({vipc_client_extra.width} x {vipc_client_extra.height})")
+
+  st = time.monotonic()
+  cloudlog.warning("loading model")
+  model = ModelState(vipc_client_main.width, vipc_client_main.height, USBGPU)
+  cloudlog.warning(f"models loaded in {time.monotonic() - st:.1f}s, modeld starting")
+
+  # messaging
+  pm = PubMaster(["modelV2", "drivingModelData", "cameraOdometry", "modelDataV2SP"])
+  sm = SubMaster(["deviceState", "carState", "roadCameraState", "liveCalibration", "driverMonitoringState", "carControl", "liveDelay"])
+
+  publish_state = PublishState()
+  params = Params()
+
+  # setup filter to track dropped frames
+  frame_dropped_filter = FirstOrderFilter(0., 10., 1. / ModelConstants.MODEL_RUN_FREQ)
+  frame_id = 0
+  last_vipc_frame_id = 0
+  run_count = 0
+
+  model_transform_main = np.zeros((3, 3), dtype=np.float32)
+  model_transform_extra = np.zeros((3, 3), dtype=np.float32)
+  live_calib_seen = False
+  buf_main, buf_extra = None, None
+  meta_main = FrameMeta()
+  meta_extra = FrameMeta()
+
+  if demo:
+    CP = get_demo_car_params()
+  else:
+    CP = messaging.log_from_bytes(params.get("CarParams", block=True), car.CarParams)
+  cloudlog.info("modeld got CarParams: %s", CP.brand)
+
+  # TODO this needs more thought, use .2s extra for now to estimate other delays
+  # TODO Move smooth seconds to action function
+  long_delay = CP.longitudinalActuatorDelay + LONG_SMOOTH_SECONDS
+  prev_action = log.ModelDataV2.Action()
+
+  DH = DesireHelper()
+
+  while True:
+    # Keep receiving frames until we are at least 1 frame ahead of previous extra frame
+    while meta_main.timestamp_sof < meta_extra.timestamp_sof + 25000000:
+      buf_main = vipc_client_main.recv()
+      meta_main = FrameMeta(vipc_client_main)
+      if buf_main is None:
+        break
+
+    if buf_main is None:
+      cloudlog.debug("vipc_client_main no frame")
+      continue
+
+    if use_extra_client:
+      # Keep receiving extra frames until frame id matches main camera
+      while True:
+        buf_extra = vipc_client_extra.recv()
+        meta_extra = FrameMeta(vipc_client_extra)
+        if buf_extra is None or meta_main.timestamp_sof < meta_extra.timestamp_sof + 25000000:
+          break
+
+      if buf_extra is None:
+        cloudlog.debug("vipc_client_extra no frame")
+        continue
+
+      if abs(meta_main.timestamp_sof - meta_extra.timestamp_sof) > 10000000:
+        cloudlog.error(f"frames out of sync! main: {meta_main.frame_id} ({meta_main.timestamp_sof / 1e9:.5f}),\
+                         extra: {meta_extra.frame_id} ({meta_extra.timestamp_sof / 1e9:.5f})")
+
+    else:
+      # Use single camera
+      buf_extra = buf_main
+      meta_extra = meta_main
+
+    sm.update(0)
+    desire = DH.desire
+    is_rhd = sm["driverMonitoringState"].isRHD
+    frame_id = sm["roadCameraState"].frameId
+    v_ego = max(sm["carState"].vEgo, 0.)
+    if sm.frame % 60 == 0:
+      model.lat_delay = get_lat_delay(params, sm["liveDelay"].lateralDelay)
+    lat_delay = model.lat_delay + LAT_SMOOTH_SECONDS
+    if sm.updated["liveCalibration"] and sm.seen['roadCameraState'] and sm.seen['deviceState']:
+      device_from_calib_euler = np.array(sm["liveCalibration"].rpyCalib, dtype=np.float32)
+      dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['roadCameraState'].sensor))]
+      model_transform_main = get_warp_matrix(device_from_calib_euler, dc.ecam.intrinsics if main_wide_camera else dc.fcam.intrinsics, False).astype(np.float32)
+      has_wide_camera = use_extra_client or main_wide_camera
+      model_transform_extra = get_warp_matrix(device_from_calib_euler, dc.ecam.intrinsics if has_wide_camera else dc.fcam.intrinsics, True).astype(np.float32)
+      live_calib_seen = True
+
+    traffic_convention = np.zeros(2)
+    traffic_convention[int(is_rhd)] = 1
+
+    vec_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
+    if desire >= 0 and desire < ModelConstants.DESIRE_LEN:
+      vec_desire[desire] = 1
+
+    # tracked dropped frames
+    vipc_dropped_frames = max(0, meta_main.frame_id - last_vipc_frame_id - 1)
+    frames_dropped = frame_dropped_filter.update(min(vipc_dropped_frames, 10))
+    if run_count < 10: # let frame drops warm up
+      frame_dropped_filter.x = 0.
+      frames_dropped = 0.
+    run_count = run_count + 1
+
+    frame_drop_ratio = frames_dropped / (1 + frames_dropped)
+
+    bufs = {name: buf_extra if 'big' in name else buf_main for name in model.vision_input_names}
+    transforms = {name: model_transform_extra if 'big' in name else model_transform_main for name in model.vision_input_names}
+    frame_delay = DT_MDL # compensate for time passed since the frame was captured: current_time - timestamp_eof is 50ms on average
+    action_delay = DT_MDL / 2 # middle of the interval between model output (current state) and next frame (expected state)
+    lat_action_t = lat_delay + frame_delay + action_delay
+    long_action_t = long_delay + frame_delay + action_delay
+    inputs: dict[str, np.ndarray] = {
+      'desire_pulse': vec_desire,
+      'traffic_convention': traffic_convention,
+      'action_t': np.array([lat_action_t, long_action_t], dtype=np.float32),
+    }
+
+    mt1 = time.perf_counter()
+    model_output = model.run(bufs, transforms, inputs)
+    mt2 = time.perf_counter()
+    model_execution_time = mt2 - mt1
+
+    if model_output is not None:
+      modelv2_send = messaging.new_message('modelV2')
+      drivingdata_send = messaging.new_message('drivingModelData')
+      posenet_send = messaging.new_message('cameraOdometry')
+      mdv2sp_send = messaging.new_message('modelDataV2SP')
+
+      action = get_action_from_model(model_output, prev_action, lat_action_t, long_action_t, v_ego)
+      prev_action = action
+      fill_model_msg(modelv2_send, model_output, action,
+                     publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
+                     frame_drop_ratio, meta_main.timestamp_eof, model_execution_time, live_calib_seen)
+
+      desire_state = modelv2_send.modelV2.meta.desireState
+      l_lane_change_prob = desire_state[log.Desire.laneChangeLeft]
+      r_lane_change_prob = desire_state[log.Desire.laneChangeRight]
+      lane_change_prob = l_lane_change_prob + r_lane_change_prob
+      DH.update(sm['carState'], sm['carControl'].latActive, lane_change_prob)
+      modelv2_send.modelV2.meta.laneChangeState = DH.lane_change_state
+      modelv2_send.modelV2.meta.laneChangeDirection = DH.lane_change_direction
+      mdv2sp_send.modelDataV2SP.laneTurnDirection = DH.lane_turn_direction
+
+      fill_driving_model_data(drivingdata_send, modelv2_send)
+      fill_pose_msg(posenet_send, model_output, meta_main.frame_id, vipc_dropped_frames, meta_main.timestamp_eof, live_calib_seen)
+      pm.send('modelV2', modelv2_send)
+      pm.send('drivingModelData', drivingdata_send)
+      pm.send('cameraOdometry', posenet_send)
+      pm.send('modelDataV2SP', mdv2sp_send)
+    last_vipc_frame_id = meta_main.frame_id
+
+
+if __name__ == "__main__":
+  try:
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--demo', action='store_true', help='A boolean for demo mode.')
+    args = parser.parse_args()
+    main(demo=args.demo)
+  except KeyboardInterrupt:
+    cloudlog.warning("got SIGINT")

openpilot/selfdrive/modeld/models/README.md

@@ -0,0 +1,2 @@
+## Neural networks in openpilot
+To view the architecture of the ONNX networks, you can use [netron](https://netron.app/)

openpilot/selfdrive/modeld/models/dmonitoring_model_tinygrad.pkl.chunkmanifest

@@ -0,0 +1 @@
+1

openpilot/selfdrive/modeld/models/driving_tinygrad.pkl.chunkmanifest

@@ -0,0 +1 @@
+2

openpilot/selfdrive/modeld/models/tg_input_devices.json

@@ -0,0 +1 @@
+{"openpilot.selfdrive.modeld.modeld": {"default": {"WARP_DEV": "QCOM", "QUEUE_DEV": "QCOM"}, "usbgpu": {"WARP_DEV": "QCOM", "QUEUE_DEV": "AMD"}}, "openpilot.selfdrive.modeld.dmonitoringmodeld": {"default": {"DEV": "QCOM"}}}

openpilot/selfdrive/modeld/parse_model_outputs.py

@@ -0,0 +1,120 @@
+import numpy as np
+from openpilot.selfdrive.modeld.constants import ModelConstants
+
+def safe_exp(x, out=None):
+  # -11 is around 10**14, more causes float16 overflow
+  return np.exp(np.clip(x, -np.inf, 11), out=out)
+
+def sigmoid(x):
+  return 1. / (1. + safe_exp(-x))
+
+def softmax(x, axis=-1):
+  x -= np.max(x, axis=axis, keepdims=True)
+  if x.dtype == np.float32 or x.dtype == np.float64:
+    safe_exp(x, out=x)
+  else:
+    x = safe_exp(x)
+  x /= np.sum(x, axis=axis, keepdims=True)
+  return x
+
+class Parser:
+  def __init__(self, ignore_missing=False):
+    self.ignore_missing = ignore_missing
+
+  def check_missing(self, outs, name):
+    missing = name not in outs
+    if missing and not self.ignore_missing:
+      raise ValueError(f"Missing output {name}")
+    return missing
+
+  def parse_categorical_crossentropy(self, name, outs, out_shape=None):
+    if self.check_missing(outs, name):
+      return
+    raw = outs[name]
+    if out_shape is not None:
+      raw = raw.reshape((raw.shape[0],) + out_shape)
+    outs[name] = softmax(raw, axis=-1)
+
+  def parse_binary_crossentropy(self, name, outs):
+    if self.check_missing(outs, name):
+      return
+    raw = outs[name]
+    outs[name] = sigmoid(raw)
+
+  def parse_mdn(self, name, outs, in_N=0, out_N=1, out_shape=None):
+    if self.check_missing(outs, name):
+      return
+    raw = outs[name]
+    raw = raw.reshape((raw.shape[0], max(in_N, 1), -1))
+
+    n_values = (raw.shape[2] - out_N)//2
+    pred_mu = raw[:,:,:n_values]
+    pred_std = safe_exp(raw[:,:,n_values: 2*n_values])
+
+    if in_N > 1:
+      weights = np.zeros((raw.shape[0], in_N, out_N), dtype=raw.dtype)
+      for i in range(out_N):
+        weights[:,:,i - out_N] = softmax(raw[:,:,i - out_N], axis=-1)
+
+      if out_N == 1:
+        for fidx in range(weights.shape[0]):
+          idxs = np.argsort(weights[fidx][:,0])[::-1]
+          weights[fidx] = weights[fidx][idxs]
+          pred_mu[fidx] = pred_mu[fidx][idxs]
+          pred_std[fidx] = pred_std[fidx][idxs]
+      full_shape = tuple([raw.shape[0], in_N] + list(out_shape))
+      outs[name + '_weights'] = weights
+      outs[name + '_hypotheses'] = pred_mu.reshape(full_shape)
+      outs[name + '_stds_hypotheses'] = pred_std.reshape(full_shape)
+
+      pred_mu_final = np.zeros((raw.shape[0], out_N, n_values), dtype=raw.dtype)
+      pred_std_final = np.zeros((raw.shape[0], out_N, n_values), dtype=raw.dtype)
+      for fidx in range(weights.shape[0]):
+        for hidx in range(out_N):
+          idxs = np.argsort(weights[fidx,:,hidx])[::-1]
+          pred_mu_final[fidx, hidx] = pred_mu[fidx, idxs[0]]
+          pred_std_final[fidx, hidx] = pred_std[fidx, idxs[0]]
+    else:
+      pred_mu_final = pred_mu
+      pred_std_final = pred_std
+
+    if out_N > 1:
+      final_shape = tuple([raw.shape[0], out_N] + list(out_shape))
+    else:
+      final_shape = tuple([raw.shape[0],] + list(out_shape))
+    outs[name] = pred_mu_final.reshape(final_shape)
+    outs[name + '_stds'] = pred_std_final.reshape(final_shape)
+
+  def is_mhp(self, outs, name, shape):
+    if self.check_missing(outs, name):
+      return False
+    if outs[name].shape[1] == 2 * shape:
+      return False
+    return True
+
+  def parse_vision_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
+    self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
+    self.parse_mdn('wide_from_device_euler', outs, in_N=0, out_N=0, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
+    self.parse_mdn('road_transform', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
+    self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
+    self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
+    self.parse_binary_crossentropy('lane_lines_prob', outs)
+    self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
+    self.parse_binary_crossentropy('meta', outs)
+    self.parse_binary_crossentropy('lead_prob', outs)
+    lead_mhp = self.is_mhp(outs, 'lead', ModelConstants.LEAD_MHP_SELECTION * ModelConstants.LEAD_TRAJ_LEN * ModelConstants.LEAD_WIDTH)
+    lead_in_N, lead_out_N = (ModelConstants.LEAD_MHP_N, ModelConstants.LEAD_MHP_SELECTION) if lead_mhp else (0, 0)
+    lead_out_shape = (ModelConstants.LEAD_TRAJ_LEN, ModelConstants.LEAD_WIDTH) if lead_mhp else \
+        (ModelConstants.LEAD_MHP_SELECTION, ModelConstants.LEAD_TRAJ_LEN, ModelConstants.LEAD_WIDTH)
+    self.parse_mdn('lead', outs, in_N=lead_in_N, out_N=lead_out_N, out_shape=lead_out_shape)
+    return outs
+
+  def parse_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
+    self.parse_mdn('plan', outs, in_N=0, out_N=0, out_shape=(ModelConstants.IDX_N, ModelConstants.PLAN_WIDTH))
+    self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
+    return outs
+
+  def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
+    outs = self.parse_vision_outputs(outs)
+    outs = self.parse_policy_outputs(outs)
+    return outs