def build_actions_and_generics_curriculum(
num_samples: Optional[int],
num_noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Sequence[Phase1InstanceGroup]:
# pylint: disable=unused-argument
return [
_make_eat_curriculum(10, 0, language_generator),
_make_drink_curriculum(10, 0, language_generator),
_make_sit_curriculum(10, 0, language_generator),
_make_jump_curriculum(10, 0, language_generator),
_make_fly_curriculum(10, 0, language_generator),
_make_generic_statements_curriculum(
num_samples=20,
noise_objects=0,
language_generator=language_generator),
]
],
"kinds-and-generics": [
_make_each_object_by_itself_curriculum(num_samples, 0,
language_generator),
_make_kind_predicates_curriculum(None, None,
language_generator),
_make_generic_statements_curriculum(
num_samples=3,
noise_objects=0,
language_generator=language_generator),
],
"obj-actions-kinds-generics": [
_make_each_object_by_itself_curriculum(num_samples, 0,
language_generator),
# Actions - verbs in generics
_make_eat_curriculum(10, 0, language_generator),
_make_drink_curriculum(10, 0, language_generator),
_make_sit_curriculum(10, 0, language_generator),
_make_jump_curriculum(10, 0, language_generator),
_make_fly_curriculum(10, 0, language_generator),
# Plurals
_make_plural_objects_curriculum(None, 0, language_generator),
# Color attributes
_make_objects_with_colors_curriculum(None, None,
language_generator),
# Predicates
_make_colour_predicates_curriculum(None, None,
language_generator),
_make_kind_predicates_curriculum(None, None,
language_generator),
# Generics
def test_eat_curriculum(language_generator):
curriculum_test(_make_eat_curriculum(None, None, language_generator))
"without-chicken-beef-cow": [CHICKEN, BEEF, COW],
"chicken": [BEEF, COW],
"beef-cow": [CHICKEN],
"chicken-beef-cow": immutableset(),
}
for condition, banned_objects in condition_and_banned_objects.items():
pretraining_curricula = [
_make_each_object_by_itself_curriculum(
num_samples,
0,
language_generator,
banned_ontology_types=banned_objects,
),
# Actions - verbs in generics
_make_eat_curriculum(10,
0,
language_generator,
banned_ontology_types=banned_objects),
_make_drink_curriculum(10,
0,
language_generator,
banned_ontology_types=banned_objects),
_make_sit_curriculum(10,
0,
language_generator,
banned_ontology_types=banned_objects),
_make_jump_curriculum(10,
0,
language_generator,
banned_ontology_types=banned_objects),
_make_fly_curriculum(10,
0,
def run_generics_test(learner, language_mode):
def build_object_multiples_situations(
ontology: Ontology,
*,
samples_per_object: int = 3,
chooser: RandomChooser) -> Iterable[HighLevelSemanticsSituation]:
for object_type in PHASE_1_CURRICULUM_OBJECTS:
# Exclude slow objects for now
if object_type.handle in ["bird", "dog", "truck"]:
continue
is_liquid = ontology.has_all_properties(object_type, [LIQUID])
# don't want multiples of named people
if not is_recognized_particular(ontology,
object_type) and not is_liquid:
for _ in range(samples_per_object):
num_objects = chooser.choice(range(2, 4))
yield HighLevelSemanticsSituation(
ontology=GAILA_PHASE_1_ONTOLOGY,
salient_objects=[
SituationObject.instantiate_ontology_node(
ontology_node=object_type,
debug_handle=object_type.handle + f"_{idx}",
ontology=GAILA_PHASE_1_ONTOLOGY,
) for idx in range(num_objects)
],
axis_info=AxesInfo(),
)
language_generator = phase2_language_generator(language_mode)
# Teach plurals
plurals = phase1_instances(
"plurals pretraining",
build_object_multiples_situations(ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY()),
language_generator=language_generator,
)
curricula = [
# Actions - verbs in generics
_make_eat_curriculum(10, 0, language_generator),
# Plurals
plurals,
# Color attributes
_make_objects_with_colors_curriculum(None, None, language_generator),
# Predicates
_make_colour_predicates_curriculum(None, None, language_generator),
_make_kind_predicates_curriculum(None, None, language_generator),
# Generics
_make_generic_statements_curriculum(
num_samples=3,
noise_objects=0,
language_generator=language_generator),
]
for curriculum in curricula:
for (
_,
linguistic_description,
perceptual_representation,
) in curriculum.instances():
# Get the object matches first - preposition learner can't learn without already recognized objects
learner.observe(
LearningExample(perceptual_representation,
linguistic_description))