def _make_m6_beside_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
return phase1_instances(
"Preposition on",
situations=chain(*[
sampled(
_beside_template(
object_1,
object_2,
make_noise_objects(noise_objects),
is_training=True,
is_right=True,
),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=num_samples if num_samples else 1,
) for object_1 in r.sample(SMALL_OBJECT_VARS, 3)
for object_2 in r.sample(LARGE_OBJECT_VARS, 3)
]),
perception_generator=GAILA_M6_PERCEPTION_GENERATOR,
language_generator=language_generator,
)
def _make_in_front_tests(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object(
"figure_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figure_1 = standard_object(
"figure_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_0 = standard_object(
"ground_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_1 = standard_object(
"ground_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
speaker = standard_object("speaker", PERSON, added_properties=[IS_SPEAKER])
addressee = standard_object("addressee",
LEARNER,
added_properties=[IS_ADDRESSEE])
computed_background = [speaker, addressee]
return phase1_instances(
"Preposition Testing In Front",
chain(*[
flatten([
sampled(
_in_front_template(
figure,
ground,
flatten([
make_noise_objects(noise_objects),
computed_background,
]),
is_training=False,
is_near=close,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
block_multiple_of_the_same_type=True,
) for figure in figures for ground in grounds
for close in BOOL_SET
])
]),
language_generator=language_generator,
)
def make_move_imprecise_temporal_descriptions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
self_mover_0 = standard_object(
"self-mover_0",
THING,
required_properties=[SELF_MOVING],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
other_mover_0 = standard_object("mover_0",
THING,
required_properties=[ANIMATE])
movee_0 = standard_object("movee_0",
THING,
required_properties=[INANIMATE])
move_goal_reference = standard_object("move-goal-reference",
THING,
required_properties=[INANIMATE])
background = make_noise_objects(noise_objects)
return phase1_instances(
"move-with-temporal-descriptions",
chain(
# bare move (e.g. "a box moves") is about half of uses in child speed
flatten(
sampled(
bare_move_template(
self_mover_0,
move_goal_reference,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
# Transitive Move
flatten(
sampled(
transitive_move_template(
other_mover_0,
movee_0,
move_goal_reference,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
),
language_generator=language_generator,
)
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 _big_x_template(theme: TemplateObjectVariable,
noise_objects: Optional[int]) -> Phase1SituationTemplate:
learner = learner_template_factory()
computed_background = [learner]
computed_background.extend(make_noise_objects(noise_objects))
return Phase1SituationTemplate(
f"big-{theme.handle}",
salient_object_variables=[theme],
background_object_variables=computed_background,
asserted_always_relations=[bigger_than(theme, learner)],
)
def _make_on_tests(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object(
"figure_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figure_1 = standard_object(
"figure_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_0 = standard_object(
"ground_0",
THING,
required_properties=[CAN_HAVE_THINGS_RESTING_ON_THEM],
banned_properties=[HOLLOW],
)
ground_1 = standard_object(
"ground_1",
THING,
required_properties=[CAN_HAVE_THINGS_RESTING_ON_THEM],
banned_properties=[HOLLOW],
)
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Testing On",
chain(*[
flatten([
sampled(
_on_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=False,
),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=num_samples if num_samples else 5,
block_multiple_of_the_same_type=True,
) for figure in figures for ground in grounds
])
]),
language_generator=language_generator,
)
def make_push_shove_subtle_verb_distinctions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
pusher = standard_object(
"pusher_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
pushee = standard_object("pushee_0",
THING,
required_properties=[INANIMATE])
push_surface = standard_object("push_surface_0",
THING,
required_properties=[INANIMATE])
push_goal = standard_object("push_goal_0",
THING,
required_properties=[INANIMATE])
background = make_noise_objects(noise_objects)
# get all possible templates
templates = flatten([
make_push_templates(
pusher,
pushee,
push_surface,
push_goal,
use_adverbial_path_modifier=use_adverbial_path_modifier,
operator=operator,
spatial_properties=[HARD_FORCE] if hard_force else [SOFT_FORCE],
background=background,
) for hard_force in BOOL_SET
for use_adverbial_path_modifier in BOOL_SET
for operator in [TOWARD, AWAY_FROM]
])
return phase1_instances(
"pushing-shoving",
chain(
flatten([
sampled(
template,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for template in templates
])),
language_generator=language_generator,
)
def _short_x_template(theme: TemplateObjectVariable,
noise_objects: Optional[int]) -> Phase1SituationTemplate:
learner = learner_template_factory()
computed_background = [learner]
computed_background.extend(make_noise_objects(noise_objects))
# TODO: This difference should be an axis size but we can't yet
# implement that. See: https://github.com/isi-vista/adam/issues/832
return Phase1SituationTemplate(
f"tall-{theme.handle}",
salient_object_variables=[theme],
background_object_variables=computed_background,
asserted_always_relations=[bigger_than(learner, theme)],
syntax_hints=[USE_VERTICAL_MODIFIERS],
)
def _make_over_tests(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object(
"figure_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figure_1 = standard_object(
"figure_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_0 = standard_object(
"ground_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_1 = standard_object(
"ground_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Testing Over",
chain(*[
sampled(
_over_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=False,
is_distal=use_above_below,
syntax_hints=[USE_ABOVE_BELOW] if use_above_below else [],
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
block_multiple_of_the_same_type=True,
) for figure in figures for ground in grounds
# for distance in BOOL_SET
for use_above_below in BOOL_SET
]),
language_generator=language_generator,
)
def _make_behind_tests(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object(
"figure_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figure_1 = standard_object(
"figure_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_0 = standard_object(
"ground_0",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
ground_1 = standard_object(
"ground_1",
THING,
banned_properties=[HOLLOW, IS_SPEAKER, IS_ADDRESSEE])
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Testing Behind",
chain(*[
flatten([
sampled(
_behind_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=False,
is_near=close,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for figure in figures for ground in grounds
for close in BOOL_SET
])
]),
language_generator=language_generator,
)
def make_fall_imprecise_temporal_descriptions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
arbitary_object = standard_object("object_0", THING)
syntax_hints_options = ([], [USE_ADVERBIAL_PATH_MODIFIER]) # type: ignore
background = make_noise_objects(noise_objects)
return phase1_instances(
f"fall-imprecise-temporal-description",
chain(
# Any Object Falling
flatten(
sampled(
falling_template(
arbitary_object,
lands_on_ground=object_ends_up_on_ground,
syntax_hints=syntax_hints,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for object_ends_up_on_ground in BOOL_SET
for syntax_hints in syntax_hints_options
for is_fast in BOOL_SET),
# Fall on Ground
flatten(
sampled(
fall_on_ground_template(
arbitary_object,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
),
language_generator=language_generator,
)
def make_animal_eat_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
object_to_eat = standard_object("object_0", required_properties=[EDIBLE])
animal = standard_object("eater_0", NONHUMAN_ANIMAL)
background = make_noise_objects(noise_objects)
return phase1_instances(
"Animal-Eat-Curriculum",
# Fressen
sampled(
make_eat_template(animal, object_to_eat, background),
max_to_sample=num_samples if num_samples else 5,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
),
language_generator=language_generator,
)
def make_pass_toss_subtle_verb_distinction(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
tosser = standard_object("tosser_passer_0",
THING,
required_properties=[ANIMATE])
tossee = standard_object("tossee_passee_0",
THING,
required_properties=[INANIMATE])
goal = standard_object("move-goal-reference",
THING,
required_properties=[INANIMATE])
background = make_noise_objects(noise_objects)
return phase1_instances(
"tossing_passing",
chain(
flatten([
sampled(
make_pass_template(
tosser,
tossee,
goal,
use_adverbial_path_modifier=use_adverbial_path_modifier,
operator=operator,
spatial_properties=[HARD_FORCE]
if hard_force else [SOFT_FORCE],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for use_adverbial_path_modifier in BOOL_SET
for hard_force in BOOL_SET for operator in [TOWARD, AWAY_FROM]
])),
language_generator=language_generator,
)
def _make_behind_training(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object("ball", BALL)
figure_1 = standard_object("book", BOOK)
figure_2 = standard_object("dad", DAD)
ground_0 = standard_object("cookie", COOKIE)
ground_1 = standard_object("table", TABLE)
ground_2 = standard_object("person",
PERSON,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
figures = immutableset([figure_0, figure_1, figure_2])
grounds = immutableset([ground_0, ground_1, ground_2])
return phase1_instances(
"Preposition Training Behind",
chain(*[
flatten([
sampled(
_behind_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=True,
is_near=close,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for figure in figures for ground in grounds
for close in BOOL_SET
])
]),
language_generator=language_generator,
)
def _make_beside_training(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object("ball", BALL)
figure_1 = standard_object("book", BOOK)
figure_2 = standard_object("mom", MOM)
ground_0 = standard_object("cookie", COOKIE)
ground_1 = standard_object("table", TABLE)
ground_2 = standard_object("dad", DAD)
figures = immutableset([figure_0, figure_1, figure_2])
grounds = immutableset([ground_0, ground_1, ground_2])
return phase1_instances(
"Preposition Training Beside",
chain(*[
flatten([
sampled(
_beside_template(
figure,
ground,
make_noise_objects(noise_objects),
is_right=True,
is_training=True,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
block_multiple_of_the_same_type=True,
) for figure in figures for ground in grounds
# for direction in BOOL_SET
])
]),
language_generator=language_generator,
)
def make_human_eat_curriculum(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
object_to_eat = standard_object("object_0", required_properties=[EDIBLE])
human = standard_object("eater_0",
PERSON,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
background = make_noise_objects(noise_objects)
return phase1_instances(
"Human-Eat-Curriculum",
# Essen
sampled(
make_eat_template(human, object_to_eat, background),
max_to_sample=num_samples if num_samples else 5,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
),
language_generator=language_generator,
)
def _make_over_training(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = standard_object("ball", BALL)
figure_1 = standard_object("book", BOOK)
figure_2 = standard_object("mom", MOM)
ground_0 = standard_object("cookie", COOKIE)
ground_1 = standard_object("table", TABLE)
figures = immutableset([figure_0, figure_1, figure_2])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Training Over",
chain(*[
sampled(
_over_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=True,
is_distal=use_above_below,
syntax_hints=[USE_ABOVE_BELOW] if use_above_below else [],
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for figure in figures for ground in grounds
# for distance in BOOL_SET
for use_above_below in BOOL_SET
]),
language_generator=language_generator,
)
def make_walk_run_subtle_verb_distinction(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
agent = standard_object(
"walker_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
background = make_noise_objects(noise_objects)
return phase1_instances(
"walking-running",
chain(
flatten([
sampled(
make_walk_run_template(
agent,
use_adverbial_path_modifier=use_adverbial_path_modifier,
operator=operator,
spatial_properties=[HARD_FORCE]
if hard_force else [SOFT_FORCE],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for use_adverbial_path_modifier in BOOL_SET
for hard_force in BOOL_SET for operator in [AWAY_FROM, TOWARD]
])),
language_generator=language_generator,
)
def make_jump_imprecise_temporal_descriptions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
jumper = standard_object(
"jumper_0",
THING,
required_properties=[CAN_JUMP],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
background = make_noise_objects(noise_objects)
return phase1_instances(
"jumping",
chain(
flatten([
sampled(
# "A person jumps"
make_jump_template(
jumper,
use_adverbial_path_modifier=use_adverbial_path_modifier,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for use_adverbial_path_modifier in (True, False)
for is_fast in BOOL_SET
])),
language_generator=language_generator,
)
def make_roll_imprecise_temporal_descriptions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
animate_0 = standard_object("object_0",
THING,
required_properties=[ANIMATE])
rollable_0 = standard_object("object_1", required_properties=[ROLLABLE])
rolling_surface = standard_object(
"surface",
THING,
required_properties=[CAN_HAVE_THINGS_RESTING_ON_THEM])
background = make_noise_objects(noise_objects)
return phase1_instances(
"roll-imprecise-temporal-descriptions",
chain(
# rolls intransitively
flatten(
sampled(
intransitive_roll(
animate_0,
rolling_surface,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
# rolls transitively
flatten(
sampled(
transitive_roll(
animate_0,
rollable_0,
rolling_surface,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
# rolls on a surface
flatten(
sampled(
transitive_roll_with_surface(
animate_0,
rollable_0,
rolling_surface,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
),
language_generator=language_generator,
)
def make_throw_imprecise_temporal_descriptions(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
thrower = standard_object(
"thrower_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
catcher = standard_object(
"catcher_0",
THING,
required_properties=[ANIMATE],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
object_thrown = standard_object("object_0",
required_properties=[INANIMATE])
implicit_goal_reference = standard_object("implicit_throw_goal_object",
BOX)
background = make_noise_objects(noise_objects)
return phase1_instances(
"throwing-with-temporal-descriptions",
chain(
# Throw on Ground
flatten(
sampled(
throw_on_ground_template(
thrower,
object_thrown,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
# Throw
flatten(
sampled(
throw_template(
thrower,
object_thrown,
implicit_goal_reference,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
# Throw up, down
flatten(
sampled(
throw_up_down_template(
thrower,
object_thrown,
implicit_goal_reference,
is_up=is_up,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET for is_up in BOOL_SET),
# Throw To
flatten(
sampled(
throw_to_template(
thrower,
object_thrown,
catcher,
spatial_properties=[FAST] if is_fast else [SLOW],
background=background,
),
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
max_to_sample=num_samples if num_samples else 5,
) for is_fast in BOOL_SET),
),
language_generator=language_generator,
)
