def test_dynamic_perception_graph_instantiation():
ball = ObjectPerception("ball", _BALL_SCHEMA.geon.copy())
table = ObjectPerception("table", axes=_TABLE_SCHEMA.axes.copy())
first_frame = DevelopmentalPrimitivePerceptionFrame(
perceived_objects=[ball, table],
relations=[
above(ball, table),
Relation(IN_REGION, ball,
Region(table, distance=EXTERIOR_BUT_IN_CONTACT)),
Relation(IN_REGION, table,
Region(ball, distance=EXTERIOR_BUT_IN_CONTACT)),
],
)
second_frame = DevelopmentalPrimitivePerceptionFrame(
perceived_objects=[ball, table],
relations=[Relation(IN_REGION, ball, Region(table, distance=DISTAL))],
)
perception_graph = PerceptionGraph.from_dynamic_perceptual_representation(
PerceptualRepresentation(frames=[first_frame, second_frame]))
assert perception_graph.dynamic
# Ensure we don't attempt to handle more than two frames yet.
with pytest.raises(ValueError):
PerceptionGraph.from_dynamic_perceptual_representation(
PerceptualRepresentation(
frames=[first_frame, second_frame, second_frame]))
def test_trivial_dynamic_situation_with_schemaless_object(language_mode):
dad_situation_object = SituationObject.instantiate_ontology_node(
ontology_node=DAD, ontology=GAILA_PHASE_1_ONTOLOGY)
situation = HighLevelSemanticsSituation(
ontology=GAILA_PHASE_1_ONTOLOGY,
salient_objects=[dad_situation_object])
perception_generator = HighLevelSemanticsSituationToDevelopmentalPrimitivePerceptionGenerator(
GAILA_PHASE_1_ONTOLOGY)
# We explicitly exclude ground in perception generation
# this generates a static perception...
perception = perception_generator.generate_perception(
situation, chooser=RandomChooser.for_seed(0), include_ground=False)
# so we need to construct a dynamic one by hand from two identical scenes
dynamic_perception = PerceptualRepresentation(
frames=[perception.frames[0], perception.frames[0]])
perception_graph = PerceptionGraph.from_dynamic_perceptual_representation(
dynamic_perception)
perception_semantic_alignment = PerceptionSemanticAlignment.create_unaligned(
perception_graph)
(_, description_to_matched_semantic_node
) = LANGUAGE_MODE_TO_OBJECT_RECOGNIZER[language_mode].match_objects(
perception_semantic_alignment)
assert len(description_to_matched_semantic_node) == 1
assert (language_mode == LanguageMode.ENGLISH and
("Dad", ) in description_to_matched_semantic_node) or (
language_mode == LanguageMode.CHINESE and
("ba4 ba4", ) in description_to_matched_semantic_node)
def _extract_perception_graph(
self, perception: PerceptualRepresentation[DevelopmentalPrimitivePerceptionFrame]
) -> PerceptionGraph:
if perception.is_dynamic():
return PerceptionGraph.from_dynamic_perceptual_representation(perception)
else:
return PerceptionGraph.from_frame(perception.frames[0])
def test_recognize_in_transfer_of_possession(language_mode):
dad = object_variable("person_0", DAD)
baby = object_variable("person_1", BABY)
chair = object_variable("give_object_0", CHAIR)
giving_template = Phase1SituationTemplate(
"dad-transfer-of-possession",
salient_object_variables=[dad, baby, chair],
actions=[
Action(
GIVE,
argument_roles_to_fillers=[(AGENT, dad), (GOAL, baby),
(THEME, chair)],
)
],
syntax_hints=[PREFER_DITRANSITIVE],
)
(_, _, perception) = first(
phase1_instances(
"foo",
sampled(
giving_template,
max_to_sample=1,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
block_multiple_of_the_same_type=True,
),
).instances())
perception_graph = PerceptionGraph.from_dynamic_perceptual_representation(
perception)
perception_semantic_alignment = PerceptionSemanticAlignment.create_unaligned(
perception_graph)
(_, description_to_matched_semantic_node
) = LANGUAGE_MODE_TO_OBJECT_RECOGNIZER[language_mode].match_objects(
perception_semantic_alignment)
assert len(description_to_matched_semantic_node) == 4
assert (language_mode == LanguageMode.ENGLISH and
("Dad", ) in description_to_matched_semantic_node) or (
language_mode == LanguageMode.CHINESE and
("ba4 ba4", ) in description_to_matched_semantic_node)
def _extract_perception_graph(
self, perception: PerceptualRepresentation[
DevelopmentalPrimitivePerceptionFrame]
) -> PerceptionGraph:
return PerceptionGraph.from_dynamic_perceptual_representation(
perception)