def _make_drink_cups_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
) -> Phase1InstanceGroup:
templates = []
for cup in [CUP, CUP_2, CUP_3, CUP_4]:
cup_obj = standard_object("cup", cup)
liquid_0 = object_variable("liquid_0", required_properties=[LIQUID])
person_0 = standard_object(
"person_0", PERSON, banned_properties=[IS_SPEAKER, IS_ADDRESSEE]
)
templates.append(
Phase1SituationTemplate(
"drink-cup",
salient_object_variables=[liquid_0, person_0, cup_obj],
background_object_variables=make_noise_objects(noise_objects),
actions=[
Action(
DRINK,
argument_roles_to_fillers=[(AGENT, person_0), (THEME, liquid_0)],
auxiliary_variable_bindings=[(DRINK_CONTAINER_AUX, cup_obj)],
)
],
asserted_always_relations=[inside(liquid_0, cup_obj)],
)
)
return phase2_instances(
"drink - cup",
chain(
*[
sampled(
cup_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_2_ONTOLOGY,
max_to_sample=num_samples,
block_multiple_of_the_same_type=True,
)
if num_samples
else all_possible(
cup_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_2_ONTOLOGY,
)
for cup_template in templates
]
),
perception_generator=GAILA_PHASE_2_PERCEPTION_GENERATOR,
language_generator=language_generator,
)
def _make_sit_on_chair_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
) -> Phase1InstanceGroup:
templates = []
for chair_type in [CHAIR, CHAIR_2, CHAIR_3, CHAIR_4, CHAIR_5]:
sitter = standard_object(
"sitter_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
seat = standard_object("chair", chair_type)
templates.append(
make_sit_transitive(
sitter, seat, noise_objects, surface=False, syntax_hints=False
)
)
templates.append(
make_sit_template_intransitive(
sitter, seat, noise_objects, surface=False, syntax_hints=False
)
)
return phase2_instances(
"sit on chair",
chain(
*[
sampled(
template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_2_ONTOLOGY,
max_to_sample=num_samples,
block_multiple_of_the_same_type=True,
)
if num_samples
else all_possible(
template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_2_ONTOLOGY,
)
for template in templates
]
),
perception_generator=GAILA_PHASE_2_PERCEPTION_GENERATOR,
language_generator=language_generator,
)
def _prepositional_relation_described_curriculum(
max_to_sample: int,
noise_objects_sets: Iterable[Iterable[TemplateObjectVariable]],
*,
min_noise_relations: int = 0,
max_noise_relations: int = 0,
add_noise: bool,
chooser: RandomChooser,
samples_to_template_den: int = 1,
block_multiple_of_same_type: bool,
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
include_targets_in_noise: bool = False,
min_samples: int = 6,
) -> Phase1InstanceGroup:
target_1 = standard_object(
"target_1", THING, required_properties=[INTEGRATED_EXPERIMENT_PROP]
)
target_2 = standard_object(
"target_2", THING, required_properties=[INTEGRATED_EXPERIMENT_PROP]
)
target_with_object_on = standard_object(
"target with object on",
INANIMATE_OBJECT,
required_properties=[INTEGRATED_EXPERIMENT_PROP, CAN_HAVE_THINGS_RESTING_ON_THEM],
)
templates = (
[
_on_template(
target_1,
target_with_object_on,
background_objects,
is_training=True,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_1,
target_2=target_with_object_on,
include_targets_in_noise=include_targets_in_noise,
),
)
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [
_on_template(
target_1, target_with_object_on, immutableset(), is_training=True
)
]
)
templates.extend(
[
_beside_template(
target_1,
target_2,
background_objects,
is_right=is_right,
is_training=True,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_1,
target_2=target_2,
include_targets_in_noise=include_targets_in_noise,
),
)
for is_right in BOOL_SET
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [
_beside_template(
target_1, target_2, immutableset(), is_right=is_right, is_training=True
)
for is_right in BOOL_SET
]
)
templates.extend(
[
_behind_template(
target_1,
target_2,
background_objects,
is_near=is_near,
is_training=True,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_1,
target_2=target_2,
include_targets_in_noise=include_targets_in_noise,
),
)
for is_near in BOOL_SET
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [
_behind_template(
target_1, target_2, immutableset(), is_near=is_near, is_training=True
)
for is_near in BOOL_SET
]
)
templates.extend(
[
_in_front_template(
target_1,
target_2,
background_objects,
is_near=is_near,
is_training=True,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_1,
target_2=target_2,
include_targets_in_noise=include_targets_in_noise,
),
)
for is_near in BOOL_SET
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [
_in_front_template(
target_1, target_2, immutableset(), is_near=is_near, is_training=True
)
for is_near in BOOL_SET
]
)
return phase2_instances(
"Prepositional Relation",
flatten(
[
sampled(
template,
ontology=INTEGRATED_EXPERIMENT_ONTOLOGY,
chooser=chooser,
max_to_sample=max(
math.ceil(max_to_sample / samples_to_template_den), min_samples
),
block_multiple_of_the_same_type=block_multiple_of_same_type,
)
for template in templates
]
),
language_generator=language_generator,
perception_generator=INTEGRATED_EXPERIMENT_PERCEPTION_GENERATOR,
)
def _single_attribute_described_curriculum(
max_to_sample: int,
target_color_objects: Iterable[TemplateObjectVariable],
noise_objects_sets: Iterable[Iterable[TemplateObjectVariable]],
*,
min_noise_relations: int = 0,
max_noise_relations: int = 0,
add_noise: bool,
chooser: RandomChooser,
samples_to_template_den: int = 1,
block_multiple_of_same_type: bool,
language_generator: LanguageGenerator[
HighLevelSemanticsSituation, LinearizedDependencyTree
],
include_targets_in_noise: bool = False,
min_samples: int = 6,
) -> Phase1InstanceGroup:
def object_with_color(
target_with_color: TemplateObjectVariable,
*,
background_objects: Iterable[TemplateObjectVariable] = immutableset(),
background_relations: Iterable[Relation[Any]] = immutableset(),
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
name=f"single-attribute-color-{target_with_color.handle}",
salient_object_variables=[target_with_color],
background_object_variables=background_objects
if add_noise
else immutableset(),
asserted_always_relations=background_relations
if add_noise
else immutableset(),
)
templates = (
[
object_with_color(
target_object,
background_objects=background_objects,
background_relations=background_relations_builder(
background_objects,
num_relations,
target=target_object,
include_targets_in_noise=include_targets_in_noise,
),
)
for target_object in target_color_objects
for background_objects in noise_objects_sets
for num_relations in range(min_noise_relations, max_noise_relations)
]
if add_noise
else [object_with_color(target_object) for target_object in target_color_objects]
)
return phase2_instances(
"Single Attribute",
flatten(
[
sampled(
template,
ontology=INTEGRATED_EXPERIMENT_ONTOLOGY,
chooser=chooser,
max_to_sample=max(
math.ceil(max_to_sample / samples_to_template_den), min_samples
),
block_multiple_of_the_same_type=block_multiple_of_same_type,
)
for template in templates
]
),
language_generator=language_generator,
perception_generator=INTEGRATED_EXPERIMENT_PERCEPTION_GENERATOR,
)