Space Lab Model 🛰️ (#35804)

* 95c34be5-4fa1-4875-8b1d-fb7162140f10/400

* 660235c5-1647-40c4-8493-880de8f662d0/400

Author: YassineYousfi

selfdrive/modeld/models/driving_policy.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ef059460a95076f9a8600abb8c9d56c8c3b7384505b158064e2e875458b965bb
-size 15701037
+oid sha256:02cf21ce7e9784c6009cc32a6f1ea22482911897b6d944419477973284052716
+size 15583374

selfdrive/modeld/models/driving_vision.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:afee02876ed51f21883e731990a252fd57afda6a78700c41fbf22d8cb288b4b5
-size 46147818
+oid sha256:c824f68646a3b94f117f01c70dc8316fb466e05fbd42ccdba440b8a8dc86914b
+size 46265993

selfdrive/modeld/parse_model_outputs.py

@@ -93,6 +93,9 @@ def parse_vision_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndar
     self.parse_binary_crossentropy('lane_lines_prob', outs)
     self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
     self.parse_binary_crossentropy('meta', outs)
+    self.parse_binary_crossentropy('lead_prob', outs)
+    self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
+      out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
     return outs
 
   def parse_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
@@ -103,9 +106,6 @@ def parse_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndar
     if 'desired_curvature' in outs:
       self.parse_mdn('desired_curvature', outs, in_N=0, out_N=0, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
     self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
-    self.parse_binary_crossentropy('lead_prob', outs)
-    self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
-      out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
     return outs
 
   def parse_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]: