North America Model (#34260)

* 0e1c9c12-0472-4a0c-8963-611ad240ec62/400

* rm outputs

selfdrive/modeld/modeld.py

@@ -57,14 +57,11 @@ class ModelState:
     self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
     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.prev_desired_curv_20hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32)
 
     # img buffers are managed in openCL transform code
     self.inputs = {
       'desire': np.zeros(ModelConstants.DESIRE_LEN * (ModelConstants.HISTORY_BUFFER_LEN+1), dtype=np.float32),
       'traffic_convention': np.zeros(ModelConstants.TRAFFIC_CONVENTION_LEN, dtype=np.float32),
-      'lateral_control_params': np.zeros(ModelConstants.LATERAL_CONTROL_PARAMS_LEN, dtype=np.float32),
-      'prev_desired_curv': np.zeros(ModelConstants.PREV_DESIRED_CURV_LEN * (ModelConstants.HISTORY_BUFFER_LEN+1), dtype=np.float32),
       'features_buffer': np.zeros(ModelConstants.HISTORY_BUFFER_LEN * ModelConstants.FEATURE_LEN, dtype=np.float32),
     }
 
@@ -100,7 +97,6 @@ class ModelState:
     self.inputs['desire'][:] = self.desire_20Hz.reshape((25,4,-1)).max(axis=1).flatten()
 
     self.inputs['traffic_convention'][:] = inputs['traffic_convention']
-    self.inputs['lateral_control_params'][:] = inputs['lateral_control_params']
 
     self.model.setInputBuffer("input_imgs", self.frame.prepare(buf, transform.flatten(), self.model.getCLBuffer("input_imgs")))
     self.model.setInputBuffer("big_input_imgs", self.wide_frame.prepare(wbuf, transform_wide.flatten(), self.model.getCLBuffer("big_input_imgs")))
@@ -114,13 +110,8 @@ class ModelState:
     self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
     self.full_features_20Hz[-1] = outputs['hidden_state'][0, :]
 
-    self.prev_desired_curv_20hz[:-1] = self.prev_desired_curv_20hz[1:]
-    self.prev_desired_curv_20hz[-1] = outputs['desired_curvature'][0, :]
-
     idxs = np.arange(-4,-100,-4)[::-1]
     self.inputs['features_buffer'][:] = self.full_features_20Hz[idxs].flatten()
-    # TODO model only uses last value now, once that changes we need to input strided action history buffer
-    self.inputs['prev_desired_curv'][-ModelConstants.PREV_DESIRED_CURV_LEN:] = 0. * self.prev_desired_curv_20hz[-4, :]
     return outputs
 
 
@@ -231,7 +222,6 @@ def main(demo=False):
     is_rhd = sm["driverMonitoringState"].isRHD
     frame_id = sm["roadCameraState"].frameId
     v_ego = max(sm["carState"].vEgo, 0.)
-    lateral_control_params = np.array([v_ego, steer_delay], dtype=np.float32)
     if sm.updated["liveCalibration"] and sm.seen['roadCameraState'] and sm.seen['deviceState']:
       device_from_calib_euler = np.array(sm["liveCalibration"].rpyCalib, dtype=np.float32)
       dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['roadCameraState'].sensor))]
@@ -262,7 +252,6 @@ def main(demo=False):
     inputs:dict[str, np.ndarray] = {
       'desire': vec_desire,
       'traffic_convention': traffic_convention,
-      'lateral_control_params': lateral_control_params,
       }
 
     mt1 = time.perf_counter()

selfdrive/modeld/models/supercombo.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dfe3ee4516187abac1198fda50d5961d6329b07e61e9d295be01a0ef2303f536
-size 50320584
+oid sha256:0c896681fd6851de3968433e12f37834429eba265e938cf383200be3e5835cec
+size 49096168

selfdrive/modeld/parse_model_outputs.py

@@ -96,8 +96,6 @@ class Parser:
                    out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_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,))
     for k in ['lead_prob', 'lane_lines_prob', 'meta']:
       self.parse_binary_crossentropy(k, outs)
     self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))