def _enrich_post_process(
perception_graph_after_matching: PerceptionGraph,
immutable_new_nodes: AbstractSet[SemanticNode],
) -> Tuple[PerceptionGraph, AbstractSet[SemanticNode]]:
new_nodes = []
perception_graph_after_processing = perception_graph_after_matching
for candiate_object_graph in extract_candidate_objects(
perception_graph_after_matching,
sort_by_increasing_size=False):
fake_pattern_graph = PerceptionGraphPattern.from_graph(
candiate_object_graph)
fake_object_semantic_node = ObjectSemanticNode(
concept=FunctionalObjectConcept("unknown_object"))
perception_graph_after_processing = replace_match_with_object_graph_node(
matched_object_node=fake_object_semantic_node,
current_perception=perception_graph_after_processing,
pattern_match=PerceptionGraphPatternMatch(
matched_pattern=fake_pattern_graph.
perception_graph_pattern,
graph_matched_against=perception_graph_after_processing,
matched_sub_graph=candiate_object_graph,
pattern_node_to_matched_graph_node=fake_pattern_graph.
perception_graph_node_to_pattern_node,
),
).perception_graph_after_replacement
new_nodes.append(fake_object_semantic_node)
return (
perception_graph_after_processing,
immutableset(chain(immutable_new_nodes, new_nodes)),
)
def _hypotheses_from_perception(
self,
learning_state: LanguagePerceptionSemanticAlignment,
bound_surface_template: SurfaceTemplateBoundToSemanticNodes,
) -> AbstractSet[PerceptionGraphTemplate]:
if bound_surface_template.slot_to_semantic_node:
raise RuntimeError(
"Object learner should not have slot to semantic node alignments!"
)
return immutableset(
PerceptionGraphTemplate(
graph_pattern=PerceptionGraphPattern.from_graph(
candidate_object).perception_graph_pattern,
template_variable_to_pattern_node=immutabledict(),
) for candidate_object in extract_candidate_objects(
learning_state.perception_semantic_alignment.perception_graph))