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IQ.Pilot Release Commit @ 4521b0f

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IQ.Lvbs CI [bot]
2026-08-22 10:38:43 -05:00
parent e142a0001a
commit 11cefcb266
29 changed files with 822 additions and 111 deletions
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26
iqpilot/selfdrive/iqmodeld/models/runners/tinygrad/fused_runner.py
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@@ -112,13 +112,26 @@ class TinygradFusedRunner(ModelRunner):
}
# shapes must match the captured run_policy JIT inputs
on_shapes = self._on_meta['input_shapes']
captured = self._run_policy.captured
jit_shapes = {
name: tuple(int(s) for s in view.shape)
for name, (view, _vars, _dtype, _device) in zip(captured.expected_names, captured.expected_input_info)
}
def policy_input_shape(name):
shape = on_shapes.get(name, jit_shapes.get(name))
if shape is None:
raise ValueError(f"fused pkl declares no shape for policy input {name}")
return shape
self._npy_buffers = {
'desire': np.zeros(dp[2], dtype=np.float32),
'traffic_convention': np.zeros(on_shapes['traffic_convention'], dtype=np.float32),
'action_t': np.zeros(on_shapes['action_t'], dtype=np.float32),
'traffic_convention': np.zeros(policy_input_shape('traffic_convention'), dtype=np.float32),
'tfm': np.zeros((3, 3), dtype=np.float32),
'big_tfm': np.zeros((3, 3), dtype=np.float32),
}
if 'action_t' in jit_shapes:
self._npy_buffers['action_t'] = np.zeros(policy_input_shape('action_t'), dtype=np.float32)
self._cam_resolution = (cam_w, cam_h)
def run_fused(self, bufs: dict, transforms: dict[str, np.ndarray], numpy_inputs: NumpyDict) -> NumpyDict:
@@ -134,7 +147,7 @@ class TinygradFusedRunner(ModelRunner):
self._npy_buffers['desire'][:] = numpy_inputs[desire_key]
if 'traffic_convention' in numpy_inputs:
self._npy_buffers['traffic_convention'][:] = numpy_inputs['traffic_convention']
if 'action_t' in numpy_inputs:
if 'action_t' in numpy_inputs and 'action_t' in self._npy_buffers:
self._npy_buffers['action_t'][:] = numpy_inputs['action_t']
self._npy_buffers['tfm'][:] = transforms['img']
self._npy_buffers['big_tfm'][:] = transforms['big_img']
@@ -149,9 +162,12 @@ class TinygradFusedRunner(ModelRunner):
img, big_img = warp_jit(img_q=self._queues['img_q'], big_img_q=self._queues['big_img_q'],
tfm=npy('tfm'), big_tfm=npy('big_tfm'), frame=frame, big_frame=big_frame)
vision_out_t, on_out_t, off_out_t = self._run_policy(
policy_inputs = dict(
img=img, big_img=big_img, feat_q=self._queues['feat_q'], desire_q=self._queues['desire_q'],
desire=npy('desire'), traffic_convention=npy('traffic_convention'), action_t=npy('action_t'))
desire=npy('desire'), traffic_convention=npy('traffic_convention'))
if 'action_t' in self._npy_buffers:
policy_inputs['action_t'] = npy('action_t')
vision_out_t, on_out_t, off_out_t = self._run_policy(**policy_inputs)
# parse each model's output on its own sliced dict; parsing a merged dict
# would run parse_dynamic_outputs twice and double-parse plan/lead