confidence generation certainly broken

.deprecated/dataset/__init__.py

@@ -1 +1 @@
-from .graph_trav_dataset import get_ablation_module
+from .graph_trav_dataset import get_ablation_module

wild_visual_navigation/utils/confidence_generator.py

@@ -110,6 +110,12 @@ def update_running_mean(self, x: torch.tensor, x_positive: torch.tensor):
             confidence = torch.exp(-(((x - self.mean) / (self.std * self.std_factor)) ** 2) * 0.5)
             confidence[x < self.mean] = 1.0
 
+            # shifted_mean = self.mean + self.std*self.std_factor
+            # interval_min = shifted_mean - 2 * self.std
+            # interval_max = shifted_mean + 2 * self.std
+            # x = torch.clip( x , interval_min, interval_max)
+            # confidence = 1 - ((x - interval_min) / (interval_max - interval_min))
+
         return confidence.type(torch.float32)
 
     def update_moving_average(self, x: torch.tensor, x_positive: torch.tensor):
@@ -184,8 +190,16 @@ def inference_without_update(self, x: torch.tensor):
         if self.anomaly_detection:
             x = torch.clip(x, self.mean - 2 * self.std, self.mean + 2 * self.std)
             confidence = (x - torch.min(x)) / (torch.max(x) - torch.min(x))
+
         else:
+            # shifted_mean = self.mean + self.std*self.std_factor
+            # interval_min = shifted_mean - 2 * self.std
+            # interval_max = shifted_mean + 2 * self.std
+            # x = torch.clip( x , interval_min, interval_max)
+            # confidence = 1 - ((x - interval_min) / (interval_max - interval_min))
+
             confidence = torch.exp(-(((x - self.mean) / (self.std * self.std_factor)) ** 2) * 0.5)
+            confidence[x < self.mean] = 1.0
 
         return confidence.type(torch.float32)
 

wild_visual_navigation_ros/scripts/wvn_feature_extractor_node.py

@@ -390,7 +390,7 @@ def image_callback(self, image_msg: Image, cam: str):  # info_msg: CameraInfo
 
         except Exception as e:
             traceback.print_exc()
-            rospy.logerr(f"[{self._node_name}] error image callback", e)
+            rospy.logerr(f"[self._node_name] error image callback", e)
             self.system_events["image_callback_state"] = {
                 "time": rospy.get_time(),
                 "value": f"failed to execute {e}",
@@ -435,14 +435,18 @@ def load_model(self, stamp):
                 self._confidence_generator.std = cg["std"]
         else:
             if self._ros_params.verbose:
-                rospy.logerr(f"[{self._node_name}] Model Loading Failed: {e}")
+                rospy.logerr(f"[{self._node_name}] Model Loading Failed")
 
 
 if __name__ == "__main__":
     node_name = "wvn_feature_extractor_node"
     rospy.init_node(node_name)
 
-    reload_rosparams(enabled=rospy.get_param("~reload_default_params", True), node_name=node_name, camera_cfg="hdr")
+    reload_rosparams(
+        enabled=rospy.get_param("~reload_default_params", True),
+        node_name=node_name,
+        camera_cfg="wide_angle_dual_resize",
+    )
 
     wvn = WvnFeatureExtractor(node_name)
     rospy.spin()

wild_visual_navigation_ros/scripts/wvn_learning_node.py

@@ -943,6 +943,10 @@ def query_tf(self, parent_frame: str, child_frame: str, stamp: Optional[rospy.Ti
     node_name = "wvn_learning_node"
     rospy.init_node(node_name)
 
-    reload_rosparams(enabled=rospy.get_param("~reload_default_params", True), node_name=node_name, camera_cfg="hdr")
+    reload_rosparams(
+        enabled=rospy.get_param("~reload_default_params", True),
+        node_name=node_name,
+        camera_cfg="wide_angle_dual_resize",
+    )
     wvn = WvnLearning(node_name)
     rospy.spin()