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Get the model input and prepare the numpy array from the metadata

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DevTekVE
2024-12-27 17:07:11 +01:00
parent 262d17ead2
commit deaf0c485c
1 changed files with 4 additions and 5 deletions
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selfdrive/modeld/modeld.py
+4 -5
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@@ -63,16 +63,15 @@ class ModelState:
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.numpy_inputs = {
'desire': np.zeros((1, (ModelConstants.HISTORY_BUFFER_LEN+1), ModelConstants.DESIRE_LEN), dtype=np.float32),
'traffic_convention': np.zeros((1, ModelConstants.TRAFFIC_CONVENTION_LEN), dtype=np.float32),
'features_buffer': np.zeros((1, ModelConstants.HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32),
}
self.numpy_inputs = {}
with open(METADATA_PATH, 'rb') as f:
model_metadata = pickle.load(f)
self.input_shapes = model_metadata['input_shapes']
for key, shape in self.input_shapes.items():
self.numpy_inputs[key] = np.zeros(shape, dtype=np.float32)
self.output_slices = model_metadata['output_slices']
net_output_size = model_metadata['output_shapes']['outputs'][1]
self.output = np.zeros(net_output_size, dtype=np.float32)