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Refactor modeld for flexible input rates and output parsing.

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Add support for both 20Hz and variable input rates in `modeld.py` by introducing conditional data updates based on a new `is_20hz` flag. Additionally, enhance `parse_outputs` in `parse_model_outputs.py` to handle `desired_curvature` parsing when relevant input keys are present.
This commit is contained in:
DevTekVE
2025-01-06 21:37:05 +01:00
parent 5e889d9ec6
commit e28a7a5bee
2 changed files with 23 additions and 9 deletions
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selfdrive/modeld/modeld.py
+19 -8
View File
@@ -50,8 +50,9 @@ class ModelState:
self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
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)
self.is_20hz = False
# Initialize model runner
self.model_runner = TinygradRunner(self.frames)# if TICI else ONNXRunner(self.frames)
self.model_runner = TinygradRunner(self.frames) if TICI else ONNXRunner(self.frames)
# img buffers are managed in openCL transform code
self.numpy_inputs = {}
@@ -77,9 +78,14 @@ class ModelState:
new_desire = np.where(inputs['desire'] - self.prev_desire > .99, inputs['desire'], 0)
self.prev_desire[:] = inputs['desire']
self.desire_20Hz[:-1] = self.desire_20Hz[1:]
self.desire_20Hz[-1] = new_desire
self.numpy_inputs['desire'][:] = self.desire_20Hz.reshape(self.desire_reshape_dims).max(axis=2)
if self.is_20hz:
self.desire_20Hz[:-1] = self.desire_20Hz[1:]
self.desire_20Hz[-1] = new_desire
self.numpy_inputs['desire'][:] = self.desire_20Hz.reshape(self.desire_reshape_dims).max(axis=2)
else:
len = inputs['desire'].shape[0]
self.numpy_inputs['desire'][0, :-1] = self.numpy_inputs['desire'][0, 1:]
self.numpy_inputs['desire'][0, -1, :len] = new_desire[:len]
for key in self.numpy_inputs:
if key in inputs and key not in ['desire']:
@@ -96,12 +102,17 @@ class ModelState:
# Run model inference
self.output = self.model_runner.run_model()
outputs = self.parser.parse_outputs(self.model_runner.slice_outputs(self.output))
outputs = self.parser.parse_outputs(self.model_runner.slice_outputs(self.output), self.numpy_inputs.keys())
self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
self.full_features_20Hz[-1] = outputs['hidden_state'][0, :]
if self.is_20hz:
self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
self.full_features_20Hz[-1] = outputs['hidden_state'][0, :]
self.numpy_inputs['features_buffer'][:] = self.full_features_20Hz[self.full_features_20Hz_idxs]
else:
feature_len = outputs['hidden_state'].shape[1]
self.numpy_inputs['features_buffer'][0, :-1] = self.numpy_inputs['features_buffer'][0, 1:]
self.numpy_inputs['features_buffer'][0, -1, :feature_len] = outputs['hidden_state'][0, :feature_len]
self.numpy_inputs['features_buffer'][:] = self.full_features_20Hz[self.full_features_20Hz_idxs]
if "desired_curvature" in outputs:
input_name_prev = None
selfdrive/modeld/parse_model_outputs.py
+4 -1
View File
@@ -84,7 +84,8 @@ class Parser:
outs[name] = pred_mu_final.reshape(final_shape)
outs[name + '_stds'] = pred_std_final.reshape(final_shape)
def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
def parse_outputs(self, outs: dict[str, np.ndarray], input_keys: [str]) -> dict[str, np.ndarray]:
""" Parse the model outputs into a dictionary of numpy arrays. The input_keys are used to determine how the output should be parsed. """
self.parse_mdn('plan', outs, in_N=ModelConstants.PLAN_MHP_N, out_N=ModelConstants.PLAN_MHP_SELECTION,
out_shape=(ModelConstants.IDX_N,ModelConstants.PLAN_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))
@@ -96,6 +97,8 @@ class Parser:
out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
if 'lat_planner_solution' in outs:
self.parse_mdn('lat_planner_solution', outs, in_N=0, out_N=0, out_shape=(ModelConstants.IDX_N,ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
if 'desired_curvature' in outs and "prev_desired_curv" in input_keys:
self.parse_mdn('desired_curvature', outs, in_N=0, out_N=0, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
for k in ['lead_prob', 'lane_lines_prob', 'meta']:
self.parse_binary_crossentropy(k, outs)
self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))