Simple plan (#35980)

* squash

* double

* proper merge

* better organization

selfdrive/modeld/parse_model_outputs.py

@@ -94,15 +94,21 @@ class Parser:
     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)
+    if outs['lead'].shape[1] == 2 * ModelConstants.LEAD_MHP_SELECTION *ModelConstants.LEAD_TRAJ_LEN * ModelConstants.LEAD_WIDTH:
+      self.parse_mdn('lead', outs, in_N=0, out_N=0,
+                     out_shape=(ModelConstants.LEAD_MHP_SELECTION, ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
+    else:
       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]:
+    if outs['plan'].shape[1] == 2 * ModelConstants.IDX_N * ModelConstants.PLAN_WIDTH:
+      self.parse_mdn('plan', outs, in_N=0, out_N=0,
+                     out_shape=(ModelConstants.IDX_N,ModelConstants.PLAN_WIDTH))
+    else:
       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))
-    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:
       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,))