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modeld: no hardcoded frame names (#35476)

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* from model

* juggle
This commit is contained in:
ZwX1616
2025-06-06 13:05:54 -07:00
committed by GitHub
parent e389b19ed7
commit ba2d2677c1
1 changed files with 20 additions and 21 deletions
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selfdrive/modeld/modeld.py
+20 -21
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@@ -86,10 +86,20 @@ class ModelState:
prev_desire: np.ndarray # for tracking the rising edge of the pulse
def __init__(self, context: CLContext):
self.frames = {
'input_imgs': DrivingModelFrame(context, ModelConstants.TEMPORAL_SKIP),
'big_input_imgs': DrivingModelFrame(context, ModelConstants.TEMPORAL_SKIP)
}
with open(VISION_METADATA_PATH, 'rb') as f:
vision_metadata = pickle.load(f)
self.vision_input_shapes = vision_metadata['input_shapes']
self.vision_input_names = list(self.vision_input_shapes.keys())
self.vision_output_slices = vision_metadata['output_slices']
vision_output_size = vision_metadata['output_shapes']['outputs'][1]
with open(POLICY_METADATA_PATH, 'rb') as f:
policy_metadata = pickle.load(f)
self.policy_input_shapes = policy_metadata['input_shapes']
self.policy_output_slices = policy_metadata['output_slices']
policy_output_size = policy_metadata['output_shapes']['outputs'][1]
self.frames = {name: DrivingModelFrame(context, ModelConstants.TEMPORAL_SKIP) for name in self.vision_input_names}
self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
self.full_features_buffer = np.zeros((1, ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32)
@@ -106,18 +116,6 @@ class ModelState:
'features_buffer': np.zeros((1, ModelConstants.INPUT_HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32),
}
with open(VISION_METADATA_PATH, 'rb') as f:
vision_metadata = pickle.load(f)
self.vision_input_shapes = vision_metadata['input_shapes']
self.vision_output_slices = vision_metadata['output_slices']
vision_output_size = vision_metadata['output_shapes']['outputs'][1]
with open(POLICY_METADATA_PATH, 'rb') as f:
policy_metadata = pickle.load(f)
self.policy_input_shapes = policy_metadata['input_shapes']
self.policy_output_slices = policy_metadata['output_slices']
policy_output_size = policy_metadata['output_shapes']['outputs'][1]
# img buffers are managed in openCL transform code
self.vision_inputs: dict[str, Tensor] = {}
self.vision_output = np.zeros(vision_output_size, dtype=np.float32)
@@ -135,7 +133,7 @@ class ModelState:
parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in output_slices.items()}
return parsed_model_outputs
def run(self, buf: VisionBuf, wbuf: VisionBuf, transform: np.ndarray, transform_wide: np.ndarray,
def run(self, bufs: dict[str, VisionBuf], transforms: dict[str, np.ndarray],
inputs: dict[str, np.ndarray], prepare_only: bool) -> dict[str, np.ndarray] | None:
# Model decides when action is completed, so desire input is just a pulse triggered on rising edge
inputs['desire'][0] = 0
@@ -148,8 +146,7 @@ class ModelState:
self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
self.numpy_inputs['lateral_control_params'][:] = inputs['lateral_control_params']
imgs_cl = {'input_imgs': self.frames['input_imgs'].prepare(buf, transform.flatten()),
'big_input_imgs': self.frames['big_input_imgs'].prepare(wbuf, transform_wide.flatten())}
imgs_cl = {name: self.frames[name].prepare(bufs[name], transforms[name].flatten()) for name in self.vision_input_names}
if TICI and not USBGPU:
# The imgs tensors are backed by opencl memory, only need init once
@@ -328,14 +325,16 @@ def main(demo=False):
if prepare_only:
cloudlog.error(f"skipping model eval. Dropped {vipc_dropped_frames} frames")
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}
inputs:dict[str, np.ndarray] = {
'desire': vec_desire,
'traffic_convention': traffic_convention,
'lateral_control_params': lateral_control_params,
}
}
mt1 = time.perf_counter()
model_output = model.run(buf_main, buf_extra, model_transform_main, model_transform_extra, inputs, prepare_only)
model_output = model.run(bufs, transforms, inputs, prepare_only)
mt2 = time.perf_counter()
model_execution_time = mt2 - mt1