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models: aligning naming on modeld_v2 with upstream naming (#903)

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Refactor variable names for clarity in 20Hz model logic.

Renamed `full_features_20Hz` to `full_features_buffer` and `desire_20Hz` to `full_desire` for better readability and consistency. This improves code maintainability and aligns variable names with their intended purpose.
This commit is contained in:
DevTekVE
2025-05-10 09:46:54 +02:00
committed by GitHub
parent fb934247a2
commit 6142a52de7
1 changed files with 9 additions and 9 deletions
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sunnypilot/modeld_v2/modeld.py
+9 -9
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@@ -53,8 +53,8 @@ class ModelState:
self.frames = {'input_imgs': DrivingModelFrame(context, buffer_length), 'big_input_imgs': DrivingModelFrame(context, buffer_length)}
self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
if self.model_runner.is_20hz:
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.full_features_buffer = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32)
self.full_desire = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.DESIRE_LEN), dtype=np.float32)
# img buffers are managed in openCL transform code
self.numpy_inputs = {}
@@ -66,7 +66,7 @@ class ModelState:
if self.model_runner.is_20hz:
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.full_features_buffer_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 run(self, buf: VisionBuf, wbuf: VisionBuf, transform: np.ndarray, transform_wide: np.ndarray,
@@ -77,9 +77,9 @@ class ModelState:
self.prev_desire[:] = inputs['desire']
if self.model_runner.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)
self.full_desire[:-1] = self.full_desire[1:]
self.full_desire[-1] = new_desire
self.numpy_inputs['desire'][:] = self.full_desire.reshape(self.desire_reshape_dims).max(axis=2)
else:
length = inputs['desire'].shape[0]
self.numpy_inputs['desire'][0, :-1] = self.numpy_inputs['desire'][0, 1:]
@@ -102,9 +102,9 @@ class ModelState:
outputs = self.model_runner.run_model()
if self.model_runner.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]
self.full_features_buffer[:-1] = self.full_features_buffer[1:]
self.full_features_buffer[-1] = outputs['hidden_state'][0, :]
self.numpy_inputs['features_buffer'][:] = self.full_features_buffer[self.full_features_buffer_idxs]
else:
feature_len = outputs['hidden_state'].shape[1]
self.numpy_inputs['features_buffer'][0, :-1] = self.numpy_inputs['features_buffer'][0, 1:]