mawei/onepilot
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Update model input names to match onnx inputs (#29855)

Browse Source 
Updated model input names to match onnx inputs
old-commit-hash: 2d69f3d63e5462e8b095fab097c4aff00bd1c83a
This commit is contained in:
Mitchell Goff
2023-09-08 16:06:16 -07:00
committed by GitHub
parent 8ca1aceb8c
commit 65fc9f14b3
3 changed files with 17 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
selfdrive/modeld/dmonitoringmodeld.py
View File

@@ -61,11 +61,11 @@ class ModelState:
assert ctypes.sizeof(DMonitoringModelResult) == OUTPUT_SIZE * ctypes.sizeof(ctypes.c_float)
self.output = np.zeros(OUTPUT_SIZE, dtype=np.float32)
self.inputs = {
'input_imgs': np.zeros(MODEL_HEIGHT * MODEL_WIDTH, dtype=np.uint8),
'input_img': np.zeros(MODEL_HEIGHT * MODEL_WIDTH, dtype=np.uint8),
'calib': np.zeros(CALIB_LEN, dtype=np.float32)}
self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.DSP, True, None)
self.model.addInput("input_imgs", None)
self.model.addInput("input_img", None)
self.model.addInput("calib", self.inputs['calib'])
def run(self, buf:VisionBuf, calib:np.ndarray) -> Tuple[np.ndarray, float]:
@@ -74,11 +74,11 @@ class ModelState:
v_offset = buf.height - MODEL_HEIGHT
h_offset = (buf.width - MODEL_WIDTH) // 2
buf_data = buf.data.reshape(-1, buf.stride)
input_data = self.inputs['input_imgs'].reshape(MODEL_HEIGHT, MODEL_WIDTH)
input_data = self.inputs['input_img'].reshape(MODEL_HEIGHT, MODEL_WIDTH)
input_data[:] = buf_data[v_offset:v_offset+MODEL_HEIGHT, h_offset:h_offset+MODEL_WIDTH]
t1 = time.perf_counter()
self.model.setInputBuffer("input_imgs", self.inputs['input_imgs'].view(np.float32))
self.model.setInputBuffer("input_img", self.inputs['input_img'].view(np.float32))
self.model.execute()
t2 = time.perf_counter()
return self.output, t2 - t1

18
selfdrive/modeld/modeld.py
View File

@@ -46,11 +46,11 @@ class ModelState:
self.prev_desire = np.zeros(DESIRE_LEN, dtype=np.float32)
self.output = np.zeros(NET_OUTPUT_SIZE, dtype=np.float32)
self.inputs = {
'desire_pulse': np.zeros(DESIRE_LEN * (HISTORY_BUFFER_LEN+1), dtype=np.float32),
'desire': np.zeros(DESIRE_LEN * (HISTORY_BUFFER_LEN+1), dtype=np.float32),
'traffic_convention': np.zeros(TRAFFIC_CONVENTION_LEN, dtype=np.float32),
'nav_features': np.zeros(NAV_FEATURE_LEN, dtype=np.float32),
'nav_instructions': np.zeros(NAV_INSTRUCTION_LEN, dtype=np.float32),
'feature_buffer': np.zeros(HISTORY_BUFFER_LEN * FEATURE_LEN, dtype=np.float32),
'features_buffer': np.zeros(HISTORY_BUFFER_LEN * FEATURE_LEN, dtype=np.float32),
}
self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.GPU, False, context)
@@ -62,10 +62,10 @@ class ModelState:
def run(self, buf: VisionBuf, wbuf: VisionBuf, transform: np.ndarray, transform_wide: np.ndarray,
inputs: Dict[str, np.ndarray], prepare_only: bool) -> Optional[np.ndarray]:
# Model decides when action is completed, so desire input is just a pulse triggered on rising edge
inputs['desire_pulse'][0] = 0
self.inputs['desire_pulse'][:-DESIRE_LEN] = self.inputs['desire_pulse'][DESIRE_LEN:]
self.inputs['desire_pulse'][-DESIRE_LEN:] = np.where(inputs['desire_pulse'] - self.prev_desire > .99, inputs['desire_pulse'], 0)
self.prev_desire[:] = inputs['desire_pulse']
inputs['desire'][0] = 0
self.inputs['desire'][:-DESIRE_LEN] = self.inputs['desire'][DESIRE_LEN:]
self.inputs['desire'][-DESIRE_LEN:] = np.where(inputs['desire'] - self.prev_desire > .99, inputs['desire'], 0)
self.prev_desire[:] = inputs['desire']
self.inputs['traffic_convention'][:] = inputs['traffic_convention']
self.inputs['nav_features'][:] = inputs['nav_features']
@@ -81,8 +81,8 @@ class ModelState:
return None
self.model.execute()
self.inputs['feature_buffer'][:-FEATURE_LEN] = self.inputs['feature_buffer'][FEATURE_LEN:]
self.inputs['feature_buffer'][-FEATURE_LEN:] = self.output[OUTPUT_SIZE:OUTPUT_SIZE+FEATURE_LEN]
self.inputs['features_buffer'][:-FEATURE_LEN] = self.inputs['features_buffer'][FEATURE_LEN:]
self.inputs['features_buffer'][-FEATURE_LEN:] = self.output[OUTPUT_SIZE:OUTPUT_SIZE+FEATURE_LEN]
return self.output
@@ -232,7 +232,7 @@ def main():
cloudlog.error(f"skipping model eval. Dropped {vipc_dropped_frames} frames")
inputs:Dict[str, np.ndarray] = {
'desire_pulse': vec_desire,
'desire': vec_desire,
'traffic_convention': traffic_convention,
'driving_style': driving_style,
'nav_features': nav_features,

8
selfdrive/modeld/navmodeld.py
View File

@@ -48,15 +48,15 @@ class ModelState:
def __init__(self):
assert ctypes.sizeof(NavModelResult) == NAV_OUTPUT_SIZE * ctypes.sizeof(ctypes.c_float)
self.output = np.zeros(NAV_OUTPUT_SIZE, dtype=np.float32)
self.inputs = {'map': np.zeros(NAV_INPUT_SIZE, dtype=np.uint8)}
self.inputs = {'input_img': np.zeros(NAV_INPUT_SIZE, dtype=np.uint8)}
self.model = ModelRunner(MODEL_PATHS, self.output, Runtime.DSP, True, None)
self.model.addInput("map", None)
self.model.addInput("input_img", None)
def run(self, buf:np.ndarray) -> Tuple[np.ndarray, float]:
self.inputs['map'][:] = buf
self.inputs['input_img'][:] = buf
t1 = time.perf_counter()
self.model.setInputBuffer("map", self.inputs['map'].view(np.float32))
self.model.setInputBuffer("input_img", self.inputs['input_img'].view(np.float32))
self.model.execute()
t2 = time.perf_counter()
return self.output, t2 - t1