def test_choose_first():
generator = ChooseFirstLanguageGenerator(DummyLanguageGenerator())
# pycharm fails to recognize converter
# noinspection PyTypeChecker
situation = LocatedObjectSituation([(situation_object(BALL),
Point(0, 0, 0))])
generated_descriptions = generator.generate_language(
situation, RandomChooser.for_seed(0))
assert len(generated_descriptions) == 1
assert generated_descriptions[0].as_token_sequence() == ("hello", "world")
class SceneNode:
"""
Node type used for creating graph structure from a Perception of a scene.
This kind of hierarchical grouping of objects within the scene is helpful for adjusting
the positions of the objects within the rendering engine.
"""
name: str = attr.ib()
perceived_obj: ObjectPerception = attr.ib()
children: List["SceneNode"] = attr.ib(factory=list)
parent: "SceneNode" = attr.ib(default=None)
position: Point = attr.ib(default=Point(0, 0, 0))
def test_single_object_generator():
# warning due to PyCharm bug
# noinspection PyTypeChecker
situation = LocatedObjectSituation(
objects_to_locations=((situation_object(BALL), Point(0, 0, 0)), ))
single_obj_generator = SingleObjectLanguageGenerator(
GAILA_PHASE_1_ENGLISH_LEXICON)
languages_for_situation = single_obj_generator.generate_language(
situation, RandomChooser.for_seed(0))
assert len(languages_for_situation) == 1
assert languages_for_situation[0].as_token_sequence() == ("ball", )
def test_simple_experiment():
language_generator = SingleObjectLanguageGenerator(
GAILA_PHASE_1_ENGLISH_LEXICON)
perception_generator = DummyVisualPerceptionGenerator()
only_show_truck = GeneratedFromSituationsInstanceGroup(
name="only-ball",
situations=[
LocatedObjectSituation([(
SituationObject.instantiate_ontology_node(
BALL, ontology=GAILA_PHASE_1_ONTOLOGY),
Point(0.0, 0.0, 0.0),
)])
],
language_generator=language_generator,
perception_generator=perception_generator,
chooser=RandomChooser.for_seed(0),
)
pre_acc = CandidateAccuracyObserver("pre")
post_acc = CandidateAccuracyObserver("post")
test_acc = CandidateAccuracyObserver("test")
experiment = Experiment(
name="simple",
training_stages=[only_show_truck],
learner_factory=MemorizingLanguageLearner,
pre_example_training_observers=[
TopChoiceExactMatchObserver("pre"), pre_acc
],
post_example_training_observers=[
TopChoiceExactMatchObserver("post"), post_acc
],
warm_up_test_instance_groups=[only_show_truck],
test_instance_groups=[only_show_truck],
test_observers=[TopChoiceExactMatchObserver("test"), test_acc],
sequence_chooser=RandomChooser.for_seed(0),
)
execute_experiment(experiment)
assert pre_acc.accuracy() == 0.0
assert post_acc.accuracy() == 1.0
assert test_acc.accuracy() == 1.0
def _locations_in_a_line_1m_apart():
for x_coordinate in range(0, sys.maxsize):
yield Point(float(x_coordinate), 0.0, 0.0)
def test_dummy_visual_perception():
located_truck = DummyVisualPerceptionFrame.SingleObjectPerception(
"truck", location=Point(1.0, 2.0, 3.0)
)
assert located_truck.tag == "truck"
assert located_truck.location == Point(1.0, 2.0, 3.0)