def test_come(language_mode, learner):
movee = standard_object(
"movee",
required_properties=[SELF_MOVING],
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
)
learner_obj = standard_object("leaner_0", LEARNER)
speaker = standard_object(
"speaker",
PERSON,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
added_properties=[IS_SPEAKER],
)
object_ = standard_object("object_0",
THING,
banned_properties=[IS_SPEAKER, IS_ADDRESSEE])
ground = standard_object("ground", root_node=GROUND)
come_to_speaker = Phase1SituationTemplate(
"come-to-speaker",
salient_object_variables=[movee, speaker],
actions=[
Action(COME,
argument_roles_to_fillers=[(AGENT, movee), (GOAL, speaker)])
],
)
come_to_learner = Phase1SituationTemplate(
"come-to-leaner",
salient_object_variables=[movee],
actions=[
Action(COME,
argument_roles_to_fillers=[(AGENT, movee),
(GOAL, learner_obj)])
],
)
come_to_object = Phase1SituationTemplate(
"come-to-object",
salient_object_variables=[movee, object_],
actions=[
Action(COME,
argument_roles_to_fillers=[(AGENT, movee), (GOAL, object_)])
],
)
for situation_template in [
_make_come_down_template(movee, object_, speaker, ground,
immutableset()),
come_to_speaker,
come_to_learner,
come_to_object,
]:
run_verb_test(
learner(language_mode),
situation_template,
language_generator=phase1_language_generator(language_mode),
)
def test_matching_static_vs_dynamic_graphs():
target_object = BOX
train_obj_object = object_variable("obj-with-color", target_object)
obj_template = Phase1SituationTemplate(
"colored-obj-object", salient_object_variables=[train_obj_object])
template = all_possible(obj_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY)
train_curriculum = phase1_instances("all obj situations",
situations=template)
perceptual_representation = only(train_curriculum.instances())[2]
perception_graph = graph_without_learner(
PerceptionGraph.from_frame(perceptual_representation.frames[0]))
temporal_perception_graph = perception_graph.copy_with_temporal_scopes(
temporal_scopes=[TemporalScope.AFTER])
perception_pattern = PerceptionGraphPattern.from_graph(
perception_graph).perception_graph_pattern
temporal_perception_pattern = perception_pattern.copy_with_temporal_scopes(
required_temporal_scopes=[TemporalScope.AFTER])
# Test runtime error for matching static pattern against dynamic graph and vice versa
with pytest.raises(RuntimeError):
perception_pattern.matcher(temporal_perception_graph,
match_mode=MatchMode.NON_OBJECT)
with pytest.raises(RuntimeError):
temporal_perception_pattern.matcher(perception_graph,
match_mode=MatchMode.NON_OBJECT)
def _under_template(
figure: TemplateObjectVariable,
ground: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_training: bool,
is_distal: bool,
syntax_hints: Iterable[str] = [],
) -> Phase1SituationTemplate:
handle = "training" if is_training else "testing"
# TODO: currently this hack keeps old implementation for English that hasn't solved https://github.com/isi-vista/adam/issues/802
# and returns new implementation for Chinese that does solve this
return Phase1SituationTemplate(
f"preposition-{handle}-{figure.handle}-under-{ground.handle}",
salient_object_variables=[figure, ground],
background_object_variables=background,
asserted_always_relations=[
strictly_under(ground,
figure,
dist=DISTAL if is_distal else PROXIMAL)
],
constraining_relations=[bigger_than(ground, figure)],
gazed_objects=[figure],
syntax_hints=syntax_hints,
)
def _in_front_template(
figure: TemplateObjectVariable,
ground: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_training: bool,
is_near: bool,
speaker_root_node: OntologyNode = PERSON,
) -> Phase1SituationTemplate:
handle = "training" if is_training else "testing"
direction = Direction(positive=True,
relative_to_axis=FacingAddresseeAxis(ground))
speaker = standard_object("speaker",
speaker_root_node,
added_properties=[IS_SPEAKER])
addressee = standard_object("addressee",
LEARNER,
added_properties=[IS_ADDRESSEE])
computed_background = [speaker, addressee]
computed_background.extend(background)
return Phase1SituationTemplate(
f"preposition-{handle}-{figure.handle}-behind-{ground.handle}",
salient_object_variables=[figure, ground],
background_object_variables=computed_background,
asserted_always_relations=[
near(figure, ground, direction=direction)
if is_near else far(figure, ground, direction=direction)
],
gazed_objects=[figure],
)
def _under_template(
figure: TemplateObjectVariable,
ground: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_training: bool,
is_distal: bool,
syntax_hints: Iterable[str] = immutableset(),
background_relations: Iterable[TemplateObjectVariable] = immutableset(),
) -> Phase1SituationTemplate:
handle = "training" if is_training else "testing"
relations = [
negate(on(figure, GROUND_OBJECT_TEMPLATE)),
strictly_under(ground, figure, dist=DISTAL if is_distal else PROXIMAL),
]
relations.extend(background_relations) # type: ignore
return Phase1SituationTemplate(
f"preposition-{handle}-{figure.handle}-under-{ground.handle}",
salient_object_variables=[figure, ground],
background_object_variables=background,
asserted_always_relations=flatten_relations(relations),
constraining_relations=[bigger_than(ground, figure)],
gazed_objects=[figure],
syntax_hints=syntax_hints,
)
def _go_under_template(
agent: TemplateObjectVariable,
goal_object: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_distal: bool, # pylint:disable=unused-argument
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
f"go_under-{agent.handle}-under-{goal_object.handle}",
salient_object_variables=[agent, goal_object],
background_object_variables=background,
actions=[
Action(
GO,
argument_roles_to_fillers=[
(AGENT, agent),
(
GOAL,
Region(
goal_object, distance=PROXIMAL, direction=GRAVITATIONAL_DOWN
),
),
],
)
],
before_action_relations=[negate(on(goal_object, GROUND_OBJECT_TEMPLATE))],
asserted_always_relations=[negate(on(goal_object, GROUND_OBJECT_TEMPLATE))],
after_action_relations=[
negate(on(goal_object, GROUND_OBJECT_TEMPLATE)),
near(agent, goal_object),
],
constraining_relations=flatten_relations(bigger_than(goal_object, agent)),
)
def _beside_template(
figure: TemplateObjectVariable,
ground: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_right: bool,
is_training: bool,
) -> Phase1SituationTemplate:
direction_str = "right" if is_right else "left"
handle = "training" if is_training else "testing"
return Phase1SituationTemplate(
f"preposition-{handle}-{figure.handle}-beside-{ground.handle}-{direction_str}",
salient_object_variables=[figure, ground],
background_object_variables=background,
asserted_always_relations=[
near(
figure,
ground,
direction=Direction(
positive=is_right,
relative_to_axis=HorizontalAxisOfObject(ground, index=0),
),
)
],
gazed_objects=[figure],
)
def test_two_objects():
two_object_template = Phase1SituationTemplate(
"two-objects",
salient_object_variables=[
object_variable("person", root_node=_PERSON),
object_variable("toy_vehicle", required_properties=[_TOY_VEHICLE]),
],
)
reference_object_sets = {
immutableset(["mom", "toy_truck"]),
immutableset(["dad", "toy_truck"]),
immutableset(["learner", "toy_truck"]),
immutableset(["mom", "toy_car"]),
immutableset(["dad", "toy_car"]),
immutableset(["learner", "toy_car"]),
}
generated_object_sets = set(
immutableset(situation_object.ontology_node.handle
for situation_object in situation.salient_objects)
for situation in all_possible(
two_object_template,
ontology=_TESTING_ONTOLOGY,
chooser=RandomChooser.for_seed(0),
default_addressee_node=_LEARNER,
))
assert generated_object_sets == reference_object_sets
def test_before_after_relations_asserted():
ball = object_variable("ball", root_node=BALL)
box = object_variable("box", root_node=BOX)
ground = object_variable("ground", root_node=GROUND)
template_action = Phase1SituationTemplate(
"Before/After Relation",
salient_object_variables=[ball, box],
background_object_variables=[ground],
actions=[
Action(
ROLL,
argument_roles_to_fillers=[(AGENT, ball)],
auxiliary_variable_bindings=[(ROLL_SURFACE_AUXILIARY, ground)],
)
],
before_action_relations=flatten_relations([on(ball, box)]),
after_action_relations=flatten_relations([far(ball, box)]),
)
situation_with_relations = tuple(
sampled(
template_action,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=RandomChooser.for_seed(0),
max_to_sample=1,
))
assert situation_with_relations[0].before_action_relations
assert situation_with_relations[0].after_action_relations
def _put_on_body_part_template(
# X puts Y on body part
agent: TemplateObjectVariable,
theme: TemplateObjectVariable,
goal_reference: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
f"{agent.handle}-puts-{theme.handle}-on-{goal_reference.handle}",
salient_object_variables=[agent, theme, goal_reference],
background_object_variables=background,
actions=[
Action(
PUT,
argument_roles_to_fillers=[
(AGENT, agent),
(THEME, theme),
(
GOAL,
Region(
goal_reference,
distance=EXTERIOR_BUT_IN_CONTACT,
direction=GRAVITATIONAL_UP,
),
),
],
)
],
constraining_relations=flatten_relations(
bigger_than([agent, goal_reference], theme)
),
asserted_always_relations=flatten_relations(has(agent, goal_reference)),
)
def _make_multiple_object_template(
target: TemplateObjectVariable, background: Iterable[TemplateObjectVariable]
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
"object-falls",
salient_object_variables=[target],
background_object_variables=background,
)
def test_successfully_extending_partial_match():
"""
Tests whether we can match a perception pattern against a perception graph
when initializing the search from a partial match.
"""
target_object = BOX
# Create train and test templates for the target objects
train_obj_object = object_variable("obj-with-color", target_object)
obj_template = Phase1SituationTemplate(
"colored-obj-object", salient_object_variables=[train_obj_object])
template = all_possible(obj_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY)
train_curriculum = phase1_instances("all obj situations",
situations=template)
perceptual_representation = only(train_curriculum.instances())[2]
# Original perception graph
perception = PerceptionGraph.from_frame(
perceptual_representation.frames[0])
# Create a perception pattern for the whole thing
# and also a perception pattern for a subset of the whole pattern
whole_perception_pattern = PerceptionGraphPattern.from_graph(
perception).perception_graph_pattern
partial_digraph = whole_perception_pattern.copy_as_digraph()
partial_digraph.remove_nodes_from([
node for node in partial_digraph.nodes
if isinstance(node, IsColorNodePredicate)
])
partial_perception_pattern = PerceptionGraphPattern(partial_digraph)
# get our initial match by matching the partial pattern
matcher = partial_perception_pattern.matcher(
perception, match_mode=MatchMode.NON_OBJECT)
partial_match: PerceptionGraphPatternMatch = first(
matcher.matches(use_lookahead_pruning=True))
partial_mapping = partial_match.pattern_node_to_matched_graph_node
# Try to extend the partial mapping, to create a complete mapping
matcher_2 = whole_perception_pattern.matcher(
perception, match_mode=MatchMode.NON_OBJECT)
complete_match: PerceptionGraphPatternMatch = first(
matcher_2.matches(initial_partial_match=partial_mapping,
use_lookahead_pruning=True),
None,
)
complete_mapping = complete_match.pattern_node_to_matched_graph_node
assert len(complete_mapping) == len(perception.copy_as_digraph().nodes)
assert len(complete_mapping) == len(
whole_perception_pattern.copy_as_digraph().nodes)
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 single_object_described_template(
target: TemplateObjectVariable,
*,
background_objects: Iterable[TemplateObjectVariable] = immutableset(),
background_relations: Iterable[Relation[Any]] = immutableset(),
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
name=f"single-object-{target.handle}",
salient_object_variables=[target],
background_object_variables=background_objects,
asserted_always_relations=background_relations,
syntax_hints=[IGNORE_COLORS],
)
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 _on_template(
figure: TemplateObjectVariable,
ground: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_training: bool,
) -> Phase1SituationTemplate:
handle = "training" if is_training else "testing"
return Phase1SituationTemplate(
f"preposition-{handle}-{figure.handle}-on-{ground.handle}",
salient_object_variables=[figure, ground],
background_object_variables=background,
asserted_always_relations=[on(figure, ground)],
gazed_objects=[figure],
)
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(),
)
def _on_template(
figure: TemplateObjectVariable,
ground: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_training: bool,
background_relations: Iterable[Relation[Any]] = immutableset(),
) -> Phase1SituationTemplate:
handle = "training" if is_training else "testing"
relations = [on(figure, ground)]
relations.extend(background_relations) # type: ignore
return Phase1SituationTemplate(
f"preposition-{handle}-{figure.handle}-on-{ground.handle}",
salient_object_variables=[figure, ground],
background_object_variables=background,
asserted_always_relations=flatten_relations(relations),
gazed_objects=[figure],
)
def test_perception_graph_post_init_edge_cases():
target_object = BOX
train_obj_object = object_variable("obj-with-color", target_object)
obj_template = Phase1SituationTemplate(
"colored-obj-object", salient_object_variables=[train_obj_object])
template = all_possible(obj_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY)
train_curriculum = phase1_instances("all obj situations",
situations=template)
perceptual_representation = only(train_curriculum.instances())[2]
perception_graph = graph_without_learner(
PerceptionGraph.from_frame(perceptual_representation.frames[0]))
temporal_perception_graph = perception_graph.copy_with_temporal_scopes(
temporal_scopes=[TemporalScope.AFTER])
temporal_digraph = temporal_perception_graph.copy_as_digraph()
# Test valid edge label
# The only feasible test seems to be the instation, since creating a corrupt instance throws the same RuntimeError
with pytest.raises(RuntimeError):
TemporallyScopedEdgeLabel(None)
# In a dynamic graph, all edge labels must be wrapped in TemporallyScopedEdgeLabel
new_graph = DiGraph()
for (source, target) in temporal_digraph.edges():
new_graph.add_edge(source, target)
new_graph[source][target]["label"] = None
with pytest.raises(RuntimeError):
PerceptionGraph(new_graph, dynamic=True)
# TemporallyScopedEdgeLabels may not appear in a static graph
new_graph = DiGraph()
for (source, target) in temporal_digraph.edges():
new_graph.add_edge(source, target)
new_graph[source][target]["label"] = TemporallyScopedEdgeLabel(
"attribute", [TemporalScope.AFTER])
with pytest.raises(RuntimeError):
PerceptionGraph(new_graph)
# Every edge in a PerceptionGraph must have a 'label
new_graph = DiGraph()
for (source, target) in temporal_digraph.edges():
new_graph.add_edge(source, target)
with pytest.raises(RuntimeError):
PerceptionGraph(new_graph)
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 _fly_over_template(
# A bird flies over a ball
agent: TemplateObjectVariable,
object_in_path: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
f"{agent.handle}-flies-over-{object_in_path.handle}",
salient_object_variables=[agent, object_in_path],
background_object_variables=background,
actions=[
Action(
FLY,
argument_roles_to_fillers=[(AGENT, agent)],
during=DuringAction(
at_some_point=flatten_relations(strictly_above(agent, object_in_path))
),
)
],
)
def _go_in_template(
agent: TemplateObjectVariable,
goal_object: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
f"go_in-{agent.handle}-in-{goal_object.handle}",
salient_object_variables=[agent, goal_object],
background_object_variables=background,
actions=[
Action(
GO,
argument_roles_to_fillers=[
(AGENT, agent),
(GOAL, Region(goal_object, distance=INTERIOR)),
],
)
],
constraining_relations=flatten_relations(bigger_than(goal_object, agent)),
after_action_relations=[inside(agent, goal_object)],
)
def _go_to_template(
agent: TemplateObjectVariable,
goal_object: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
f"go_to-{agent.handle}-to-{goal_object.handle}",
salient_object_variables=[agent, goal_object],
background_object_variables=background,
actions=[
Action(
GO,
argument_roles_to_fillers=[
(AGENT, agent),
(GOAL, Region(goal_object, distance=PROXIMAL)),
],
)
],
after_action_relations=[near(agent, goal_object)],
gazed_objects=[agent],
)
def _jump_over_template(
# "Mom jumps over a ball"
agent: TemplateObjectVariable,
object_in_path: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
f"{agent.handle}-jumps-over-{object_in_path.handle}",
salient_object_variables=[agent, object_in_path],
background_object_variables=background,
actions=[
Action(
JUMP,
argument_roles_to_fillers=[(AGENT, agent)],
during=DuringAction(
at_some_point=flatten_relations(
strictly_above(agent, object_in_path)
),
objects_to_paths=[
(
agent,
SpatialPath(
operator=VIA,
reference_source_object=Region(
object_in_path,
direction=GRAVITATIONAL_UP,
distance=DISTAL,
),
reference_destination_object=GROUND_OBJECT_TEMPLATE,
),
)
],
),
auxiliary_variable_bindings=[
(JUMP_INITIAL_SUPPORTER_AUX, GROUND_OBJECT_TEMPLATE)
],
)
],
asserted_always_relations=[negate(on(agent, GROUND_OBJECT_TEMPLATE))],
)
def test_copy_with_temporal_scopes_content():
"""
Tests whether copy_with_temporal_scopes converts graphs to be dynamic as intended
"""
# We use a situation to generate the perceptual representation
# for a box with color.
target_object = BOX
train_obj_object = object_variable("obj-with-color", target_object)
obj_template = Phase1SituationTemplate(
"colored-obj-object", salient_object_variables=[train_obj_object])
template = all_possible(obj_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY)
train_curriculum = phase1_instances("all obj situations",
situations=template)
perceptual_representation = only(train_curriculum.instances())[2]
perception_graph = graph_without_learner(
PerceptionGraph.from_frame(perceptual_representation.frames[0]))
temporal_perception_graph = perception_graph.copy_with_temporal_scopes(
temporal_scopes=[TemporalScope.AFTER])
for (source, target) in perception_graph.copy_as_digraph().edges():
assert not isinstance(
perception_graph.copy_as_digraph()[source][target]["label"],
TemporallyScopedEdgeLabel,
)
for (source,
target) in temporal_perception_graph.copy_as_digraph().edges():
# Check type, and then the content
label = temporal_perception_graph.copy_as_digraph(
)[source][target]["label"]
assert isinstance(label, TemporallyScopedEdgeLabel)
assert (label.attribute == perception_graph.copy_as_digraph()[source]
[target]["label"])
assert all(specifier in [TemporalScope.AFTER]
for specifier in label.temporal_specifiers)
def _put_in_template(
agent: TemplateObjectVariable,
theme: TemplateObjectVariable,
goal_reference: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
f"{agent.handle}-puts-{theme.handle}-in-{goal_reference.handle}",
salient_object_variables=[agent, theme, goal_reference],
background_object_variables=background,
actions=[
Action(
PUT,
argument_roles_to_fillers=[
(AGENT, agent),
(THEME, theme),
(GOAL, Region(goal_reference, distance=INTERIOR)),
],
)
],
constraining_relations=flatten_relations(bigger_than(goal_reference, theme)),
)
def _fly_under_template(
# A bird flies under a chair
agent: TemplateObjectVariable,
object_in_path: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
) -> Phase1SituationTemplate:
return Phase1SituationTemplate(
f"{agent.handle}-flies-under-{object_in_path.handle}",
salient_object_variables=[agent, object_in_path],
background_object_variables=background,
actions=[
Action(
FLY,
argument_roles_to_fillers=[(AGENT, agent)],
during=DuringAction(
at_some_point=flatten_relations(strictly_above(object_in_path, agent))
),
)
],
asserted_always_relations=[negate(on(object_in_path, GROUND_OBJECT_TEMPLATE))],
before_action_relations=[negate(on(object_in_path, GROUND_OBJECT_TEMPLATE))],
after_action_relations=[negate(on(object_in_path, GROUND_OBJECT_TEMPLATE))],
constraining_relations=flatten_relations(bigger_than(object_in_path, agent)),
)
def _in_front_template(
figure: TemplateObjectVariable,
ground: TemplateObjectVariable,
background: Iterable[TemplateObjectVariable],
*,
is_training: bool,
is_near: bool,
background_relations: Iterable[Relation[Any]] = immutableset(),
) -> Phase1SituationTemplate:
handle = "training" if is_training else "testing"
direction = Direction(positive=True,
relative_to_axis=FacingAddresseeAxis(ground))
relations = [
near(figure, ground, direction=direction)
if is_near else far(figure, ground, direction=direction)
]
relations.extend(background_relations) # type: ignore
return Phase1SituationTemplate(
f"preposition-{handle}-{figure.handle}-behind-{ground.handle}",
salient_object_variables=[figure, ground],
background_object_variables=background,
asserted_always_relations=flatten_relations(relations),
gazed_objects=[figure],
)
def test_learner_as_default_addressee():
learner = object_variable("learner", root_node=LEARNER)
ball = object_variable("ball", root_node=BALL)
template_with_learner = Phase1SituationTemplate(
"template with learner",
salient_object_variables=[learner, ball],
asserted_always_relations=[near(learner, ball)],
)
template_with_out_learner = Phase1SituationTemplate(
"template with out learner",
salient_object_variables=[object_variable("ball", root_node=BALL)],
)
template_with_addressee = Phase1SituationTemplate(
"template with addressee",
salient_object_variables=[
object_variable("mom",
root_node=MOM,
added_properties=[IS_ADDRESSEE])
],
)
situation_with_learner = tuple(
sampled(
template_with_learner,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=RandomChooser.for_seed(0),
max_to_sample=1,
))
situation_with_out_learner = tuple(
sampled(
template_with_out_learner,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=RandomChooser.for_seed(0),
max_to_sample=1,
))
situation_with_addressee = tuple(
sampled(
template_with_addressee,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=RandomChooser.for_seed(0),
max_to_sample=1,
))
for object_ in situation_with_learner[0].all_objects:
if object_.ontology_node == LEARNER:
assert IS_ADDRESSEE in object_.properties
break
assert situation_with_learner[0].axis_info
assert situation_with_learner[0].axis_info.addressee
assert len(situation_with_out_learner[0].all_objects) == 2
for object_ in situation_with_out_learner[0].all_objects:
if object_.ontology_node == LEARNER:
assert IS_ADDRESSEE in object_.properties
break
assert situation_with_out_learner[0].axis_info
assert situation_with_out_learner[0].axis_info.addressee
for object_ in situation_with_addressee[0].all_objects:
if object_.ontology_node == LEARNER:
assert False
assert situation_with_addressee[0].axis_info
assert situation_with_addressee[0].axis_info.addressee