clean up previous legacy version of individual VLM classifier

predicators/envs/spot_env.py

@@ -1196,36 +1196,23 @@ def _object_in_xy_classifier(state: State,
 def _on_classifier(state: State, objects: Sequence[Object]) -> bool:
     obj_on, obj_surface = objects
 
-    currently_visible = all([o in state.visible_objects for o in objects])
-    # If object not all visible and choose to use VLM,
-    # then use predicate values of previous time step
-    if CFG.spot_vlm_eval_predicate and not currently_visible:
-        # TODO: add all previous atoms to the state
-        raise NotImplementedError
-
-    # Call VLM to evaluate predicate value
-    elif CFG.spot_vlm_eval_predicate and currently_visible:
-        # predicate_str = f"""
-        # On({obj_on}, {obj_surface})
-        # (Whether {obj_on} is on {obj_surface} in the image?)
-        # """
-        # return vlm_predicate_classify(predicate_str, state)
+    # NOTE: Legacy version evaluate predicates individually
+    if CFG.spot_vlm_eval_predicate:
         raise RuntimeError(
             "VLM predicate classifier should be evaluated in batch!")
 
-    else:
-        # Check that the bottom of the object is close to the top of the surface.
-        expect = state.get(obj_surface,
-                           "z") + state.get(obj_surface, "height") / 2
-        actual = state.get(obj_on, "z") - state.get(obj_on, "height") / 2
-        classification_val = abs(actual - expect) < _ONTOP_Z_THRESHOLD
-
-        # If so, check that the object is within the bounds of the surface.
-        if not _object_in_xy_classifier(
-                state, obj_on, obj_surface, buffer=_ONTOP_SURFACE_BUFFER):
-            return False
+    # Check that the bottom of the object is close to the top of the surface.
+    expect = state.get(obj_surface,
+                       "z") + state.get(obj_surface, "height") / 2
+    actual = state.get(obj_on, "z") - state.get(obj_on, "height") / 2
+    classification_val = abs(actual - expect) < _ONTOP_Z_THRESHOLD
+
+    # If so, check that the object is within the bounds of the surface.
+    if not _object_in_xy_classifier(
+            state, obj_on, obj_surface, buffer=_ONTOP_SURFACE_BUFFER):
+        return False
 
-        return classification_val
+    return classification_val
 
 
 def _top_above_classifier(state: State, objects: Sequence[Object]) -> bool:
@@ -1240,22 +1227,11 @@ def _top_above_classifier(state: State, objects: Sequence[Object]) -> bool:
 def _inside_classifier(state: State, objects: Sequence[Object]) -> bool:
     obj_in, obj_container = objects
 
-    # currently_visible = all([o in state.visible_objects for o in objects])
-    # # If object not all visible and choose to use VLM,
-    # # then use predicate values of previous time step
-    # if CFG.spot_vlm_eval_predicate and not currently_visible:
-    #     # TODO: add all previous atoms to the state
-    #     raise NotImplementedError
-    #
-    # # Call VLM to evaluate predicate value
-    # elif CFG.spot_vlm_eval_predicate and currently_visible:
-    #     predicate_str = f"""
-    #     Inside({obj_in}, {obj_container})
-    #     (Whether {obj_in} is inside {obj_container} in the image?)
-    #     """
-    #     return vlm_predicate_classify(predicate_str, state)
-    #
-    # else:
+    # NOTE: Legacy version evaluate predicates individually
+    if CFG.spot_vlm_eval_predicate:
+        raise RuntimeError(
+            "VLM predicate classifier should be evaluated in batch!")
+
     if not _object_in_xy_classifier(
             state, obj_in, obj_container, buffer=_INSIDE_SURFACE_BUFFER):
         return False
@@ -1369,20 +1345,10 @@ def _blocking_classifier(state: State, objects: Sequence[Object]) -> bool:
     if blocker_obj == blocked_obj:
         return False
 
-    # currently_visible = all([o in state.visible_objects for o in objects])
-    # # If object not all visible and choose to use VLM,
-    # # then use predicate values of previous time step
-    # if CFG.spot_vlm_eval_predicate and not currently_visible:
-    #     # TODO: add all previous atoms to the state
-    #     raise NotImplementedError
-    #
-    # # Call VLM to evaluate predicate value
-    # elif CFG.spot_vlm_eval_predicate and currently_visible:
-    #     predicate_str = f"""
-    #     (Whether {blocker_obj} is blocking {blocked_obj} for further manipulation in the image?)
-    #     Blocking({blocker_obj}, {blocked_obj})
-    #     """
-    #     return vlm_predicate_classify(predicate_str, state)
+    # NOTE: Legacy version evaluate predicates individually
+    if CFG.spot_vlm_eval_predicate:
+        raise RuntimeError(
+            "VLM predicate classifier should be evaluated in batch!")
 
     # Only consider draggable (non-placeable, movable) objects to be blockers.
     if not blocker_obj.is_instance(_movable_object_type):