update strong pointnet++

runners/inferencer.py

@@ -137,7 +137,7 @@ class Inferencer(Runner):
         pred_cr_seq = [last_pred_cr]
         success = 0
         last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], voxel_threshold).shape[0]
-        import time
+        #import time
         while len(pred_cr_seq) < max_iter and retry < max_retry and success < max_success:
             Log.green(f"iter: {len(pred_cr_seq)}, retry: {retry}/{max_retry}, success: {success}/{max_success}")
             combined_scanned_pts = np.vstack(scanned_view_pts)
@@ -229,7 +229,6 @@ class Inferencer(Runner):
                     Log.success(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
 
                 last_pts_num = pts_num
-                break
             
 
         input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()

runners/simulator.py

@@ -0,0 +1,23 @@
+from PytorchBoot.runners.runner import Runner
+import PytorchBoot.stereotype as stereotype
+
+@stereotype.runner("simulator")
+class Simulator(Runner):
+    def __init__(self, config_path):
+        super().__init__(config_path)
+        self.config_path = config_path
+        
+    def run(self):
+        print()
+
+    def prepare_env(self):
+        pass
+
+    def create_env(self):
+        pass
+
+    def create_experiment(self, backup_name=None):
+        return super().create_experiment(backup_name)
+    
+    def load_experiment(self, backup_name=None):
+        super().load_experiment(backup_name)