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cherry pick from devtekve as base

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This commit is contained in:
Jason Wen
2025-01-06 13:54:32 -05:00
parent 5809ab3baa
commit d957a92fbf
2 changed files with 14 additions and 23 deletions
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sunnypilot/modeld/modeld.py
+5 -4
View File
@@ -52,11 +52,9 @@ class ModelState:
self.full_features_20Hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32)
self.desire_20Hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.DESIRE_LEN), dtype=np.float32)
# img buffers are managed in openCL transform code
self.inputs = prepare_inputs()
model_paths = load_model()
model_metadata = load_metadata()
self.inputs = prepare_inputs(model_metadata)
self.output_slices = model_metadata['output_slices']
net_output_size = model_metadata['output_shapes']['outputs'][1]
@@ -239,7 +237,10 @@ def main(demo=False):
if prepare_only:
cloudlog.error(f"skipping model eval. Dropped {vipc_dropped_frames} frames")
inputs = parse_runner_inputs(vec_desire, traffic_convention)
inputs: dict[str, np.ndarray] = {
'desire': vec_desire,
'traffic_convention': traffic_convention,
}
mt1 = time.perf_counter()
model_output = model.run(buf_main, buf_extra, model_transform_main, model_transform_extra, inputs, prepare_only)
sunnypilot/modeld/runners/run_helpers.py
+9 -19
View File
@@ -13,30 +13,30 @@ from openpilot.sunnypilot.models.helpers import get_active_bundle
from openpilot.system.hardware import PC
from openpilot.system.hardware.hw import Paths
USE_ONNX = int(os.getenv('USE_ONNX', str(int(PC))))
USE_ONNX = os.getenv('USE_ONNX', PC)
CUSTOM_MODEL_PATH = Paths.model_root()
METADATA_PATH = Path(__file__).parent / 'models/supercombo_metadata.pkl'
METADATA_PATH = Path(__file__).parent / '../models/supercombo_metadata.pkl'
ModelManager = custom.ModelManagerSP
def load_model():
if USE_ONNX:
model_paths = {ModelRunner.ONNX: Path(__file__).parent / 'models/supercombo.onnx'}
model_paths = {ModelRunner.ONNX: Path(__file__).parent / '../models/supercombo.onnx'}
elif bundle := get_active_bundle():
drive_model = next(model for model in bundle.models if model.type == ModelManager.Type.drive)
model_paths = {ModelRunner.THNEED: f"{CUSTOM_MODEL_PATH}/{drive_model.fileName}"}
else:
model_paths = {ModelRunner.THNEED: Path(__file__).parent / 'models/supercombo.thneed'}
model_paths = {ModelRunner.THNEED: Path(__file__).parent / '../models/supercombo.thneed'}
return model_paths
def load_metadata():
if bundle := get_active_bundle():
drive_model = next(model for model in bundle.models if model.type == ModelManager.Type.metadata)
metadata_path = f"{CUSTOM_MODEL_PATH}/{drive_model.fileName}"
metadata_model = next(model for model in bundle.models if model.type == ModelManager.Type.metadata)
metadata_path = f"{CUSTOM_MODEL_PATH}/{metadata_model.fileName}"
else:
metadata_path = METADATA_PATH
@@ -46,22 +46,12 @@ def load_metadata():
return metadata
def prepare_inputs() -> dict[str, np.ndarray]:
model_metadata = load_metadata()
def prepare_inputs(metadata) -> dict[str, np.ndarray]:
# img buffers are managed in openCL transform code
inputs: dict[str, np.ndarray] = {
key: np.zeros(shape, dtype=np.float32)
for key, shape in model_metadata['input_shapes'].items()
for key, shape in metadata['input_shapes'].items()
if key not in ['input_imgs', 'big_input_imgs']
}
return inputs
def parse_runner_inputs(vec_desire, traffic_convention) -> dict[str, np.ndarray]:
inputs: dict[str, np.ndarray] = {
'desire': vec_desire,
'traffic_convention': traffic_convention,
}
return inputs