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Refactor model input indexing for clarity and efficiency.

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Replaced hardcoded indexing with precomputed indices for feature buffers and desire reshape dimensions. This improves code readability, reduces redundancy, and ensures consistent array slicing throughout the model pipeline.
This commit is contained in:
DevTekVE
2024-12-27 18:42:56 +01:00
parent deaf0c485c
commit e23e078c5b
1 changed files with 8 additions and 3 deletions
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selfdrive/modeld/modeld.py
+8 -3
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@@ -84,6 +84,12 @@ class ModelState:
else:
self.onnx_cpu_runner = make_onnx_cpu_runner(MODEL_PATH)
num_elements = self.numpy_inputs['features_buffer'].shape[1]
step_size = int(-100 / num_elements)
self.full_features_20Hz_idxs = np.arange(step_size, step_size * (num_elements + 1), step_size)[::-1]
self.desire_reshape_dims = (self.numpy_inputs['desire'].shape[0], self.numpy_inputs['desire'].shape[1], -1, self.numpy_inputs['desire'].shape[2])
def slice_outputs(self, model_outputs: np.ndarray) -> dict[str, np.ndarray]:
parsed_model_outputs = {k: model_outputs[np.newaxis, v] for k,v in self.output_slices.items()}
if SEND_RAW_PRED:
@@ -99,7 +105,7 @@ class ModelState:
self.desire_20Hz[:-1] = self.desire_20Hz[1:]
self.desire_20Hz[-1] = new_desire
self.numpy_inputs['desire'][:] = self.desire_20Hz.reshape((1,25,4,-1)).max(axis=2)
self.numpy_inputs['desire'][:] = self.desire_20Hz.reshape(self.desire_reshape_dims).max(axis=2)
self.numpy_inputs['traffic_convention'][:] = inputs['traffic_convention']
imgs_cl = {'input_imgs': self.frames['input_imgs'].prepare(buf, transform.flatten()),
@@ -127,8 +133,7 @@ class ModelState:
self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
self.full_features_20Hz[-1] = outputs['hidden_state'][0, :]
idxs = np.arange(-4,-100,-4)[::-1]
self.numpy_inputs['features_buffer'][:] = self.full_features_20Hz[idxs]
self.numpy_inputs['features_buffer'][:] = self.full_features_20Hz[self.full_features_20Hz_idxs]
return outputs