def _make_under_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("table", TABLE)
figures = immutableset([figure_0, figure_1, figure_2])
grounds = immutableset([ground_0])
return phase1_instances(
"Preposition Training Under",
chain(*[
sampled(
_under_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 _extract_candidate_attributes(
whole_scene_perception_graph: PerceptionGraph,
object_with_attribute: ObjectSemanticNode,
) -> Sequence[PerceptionGraph]:
perception_digraph = whole_scene_perception_graph.copy_as_digraph()
# For now, we assume all attributes are based on properties.
properties = immutableset(
[
node
for _, node, label in perception_digraph.out_edges(
object_with_attribute, data="label"
)
if label == HAS_PROPERTY_LABEL
]
)
# Furthermore, we limit the search space to the even smaller set of hypotheses
# where we consider only single properties as possible attributes.
# Otherwise there are too many hypotheses for the pursuit learner to search through
# and it's unlikely to converge on the correct hypothesis
# in any reasonable amount of time or number of examples.
candidate_attribute_subgraph_node_sets = [
immutableset([object_with_attribute, property]) for property in properties
]
return immutableset(
[
whole_scene_perception_graph.subgraph_by_nodes(
candidate_attribute_subgraph_nodes
)
for candidate_attribute_subgraph_nodes in candidate_attribute_subgraph_node_sets
]
)
def test_disjoint_index(self) -> None:
overlapping_items = (
Foo(Span(0, 10)),
Foo(Span(5, 25)),
Foo(Span(20, 30)),
Bar(Span(20, 30)),
)
with self.assertRaisesRegex(
ValueError, "Some range keys are connected or overlapping"):
HasSpanIndex.index_disjoint(overlapping_items)
s1, s2, s3 = (Span(0, 3), Span(5, 25), Span(25, 30))
s2_within = Span(5, 10)
s4_contains = Span(5, 30)
fs1, fs2, fs3 = Foo(s1), Foo(s2), Foo(s3)
index = HasSpanIndex.index_disjoint((fs1, fs2, fs3))
self.assertIsNone(index.get_exactly_matching(s2_within))
self.assertEqual(fs3, index.get_exactly_matching(s3))
self.assertEqual(immutableset(), index.get_overlapping(Span(35, 40)))
self.assertEqual(immutableset([fs3]),
index.get_overlapping(Span(28, 35)))
self.assertEqual(immutableset([fs1, fs2]),
index.get_overlapping(Span(2, 7)))
self.assertEqual(immutableset(), index.get_contained(s2_within))
self.assertEqual(immutableset([fs2, fs3]),
index.get_contained(s4_contains))
self.assertIsNone(index.get_containing(s4_contains))
self.assertEqual(fs2, index.get_containing(s2_within))
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 _make_in_training(
num_samples: Optional[int],
noise_objects: Optional[int],
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree],
) -> Phase1InstanceGroup:
figure_0 = object_variable("water", WATER)
figure_1 = object_variable("juice", JUICE)
ground_0 = standard_object("box", BOX)
ground_1 = standard_object("cup", CUP)
figures = immutableset([figure_0, figure_1])
grounds = immutableset([ground_0, ground_1])
return phase1_instances(
"Preposition Training In",
chain(*[
sampled(
_in_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=True,
),
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
]),
language_generator=language_generator,
)
def forward(self): # pylint: disable=arguments-differ
self._update_subobject_positions()
collision_penalty = sum(
self.collision_penalty(box1, box2)
for (box1,
box2) in combinations(self.included_and_excluded_boxes, 2))
below_ground_penalty = sum(
self.below_ground_penalty(box)
for box in self.object_bounding_boxes)
weak_gravity_penalty = sum(
self.weak_gravity_penalty(
bounding_box,
immutableset(self.in_region_relations[object_perception]))
for object_perception, bounding_box in
self.object_perception_to_bounding_box.items()
if object_perception in self.in_region_relations)
in_region_penalty = sum(
self.in_region_penalty(
object_perception,
immutableset(self.in_region_relations[object_perception]),
) for object_perception in self.object_perception_to_bounding_box
if object_perception in self.in_region_relations)
# print(
# f"collision penalty: {collision_penalty}"
# f"\nout of bounds penalty: {below_ground_penalty}"
# f"\ngravity penalty: {weak_gravity_penalty}"
# f"\nin-region penalty: {in_region_penalty}"
# )
return (collision_penalty + below_ground_penalty +
weak_gravity_penalty + in_region_penalty)
def test_object_not_on_ground():
"""
Intended to test that one can specify an object is not on the ground
"""
table = situation_object(TABLE)
ground = situation_object(GROUND)
perception = _PERCEPTION_GENERATOR.generate_perception(
HighLevelSemanticsSituation(
ontology=GAILA_PHASE_1_ONTOLOGY,
salient_objects=immutableset([table]),
other_objects=immutableset([ground]),
always_relations=flatten_relations(negate(on(table, ground))),
),
chooser=RandomChooser.for_seed(0),
)
frame = perception.frames[0]
relations = frame.relations
table_perception = perception_with_handle(frame, "**table_0")
ground_perception = perception_with_handle(frame, "the ground")
assert not any(
relation.relation_type == IN_REGION
and relation.first_slot == table_perception
and isinstance(relation.second_slot, Region)
and relation.region.reference_object == ground_perception
and relation.region.distance == EXTERIOR_BUT_IN_CONTACT
for relation in relations
)
def from_job(job: Job,
output_files: Optional[Iterable[File]]) -> "DependencyNode":
return DependencyNode(
job=job,
output_files=immutableset(output_files)
if output_files else immutableset(),
)
def aligned_object_nodes(
num_arguments: int,
num_arguments_to_alignments_sets: Dict[
int, ImmutableSet[Tuple[SemanticNodeWithSpan, ...]]
],
language_concept_alignment: LanguageConceptAlignment,
) -> ImmutableSet[Tuple[SemanticNodeWithSpan, ...]]:
if num_arguments not in num_arguments_to_alignments_sets.keys():
# we haven't seen a request for this number of arguments before so we need to generate all the valid options
semantic_nodes_with_spans = immutableset(
SemanticNodeWithSpan(node=node, span=span)
for (
node,
span,
) in language_concept_alignment.node_to_language_span.items()
if isinstance(node, ObjectSemanticNode)
)
num_arguments_to_alignments_sets[num_arguments] = immutableset(
ordered_semantic_nodes
for ordered_semantic_nodes in itertools.product(
semantic_nodes_with_spans, repeat=num_arguments
)
if in_left_to_right_order(ordered_semantic_nodes)
)
return num_arguments_to_alignments_sets[num_arguments]
def templates_for_concept(
self, concept: Concept) -> ImmutableSet[SurfaceTemplate]:
if self._language_mode == LanguageMode.ENGLISH:
return self._concepts_to_templates[concept]
elif self._language_mode == LanguageMode.CHINESE:
if concept.debug_string == "you":
return immutableset([
SurfaceTemplate.for_object_name(
"ni3", language_mode=self._language_mode)
])
if concept.debug_string == "me":
return immutableset([
SurfaceTemplate.for_object_name(
"wo3", language_mode=self._language_mode)
])
mappings = (
GAILA_PHASE_1_CHINESE_LEXICON._ontology_node_to_word # pylint:disable=protected-access
)
for k, v in mappings.items():
if k.handle == concept.debug_string:
return immutableset([
SurfaceTemplate.for_object_name(
v.base_form, language_mode=self._language_mode)
])
# FunctionalObjectConcepts mean we have recognized an object but don't have
# Knowledge of what the lexicalization is. So we just return an empty set
if isinstance(concept, FunctionalObjectConcept):
return immutableset()
raise RuntimeError(f"Invalid concept {concept}")
def _candidate_templates(
self, language_perception_semantic_alignment:
LanguagePerceptionSemanticAlignment
) -> AbstractSet[SurfaceTemplateBoundToSemanticNodes]:
# We can only learn single words for objects at the moment.
# See https://github.com/isi-vista/adam/issues/793 .
# Attempt to align every unaligned token to some object in the scene.
language_alignment = (
language_perception_semantic_alignment.language_concept_alignment)
ret = immutableset(
SurfaceTemplateBoundToSemanticNodes(
SurfaceTemplate.for_object_name(
token, language_mode=self._language_mode),
slot_to_semantic_node={},
) for (tok_idx, token) in enumerate(
language_alignment.language.as_token_sequence())
if not language_alignment.token_index_is_aligned(tok_idx)
# ignore determiners
and token not in DETERMINERS)
return immutableset(
bound_surface_template for bound_surface_template in ret
# For now, we require templates to account for the entire utterance.
# See https://github.com/isi-vista/adam/issues/789
if covers_entire_utterance(bound_surface_template,
language_alignment,
ignore_determiners=True))
def required_action_description(
self, action_type: OntologyNode,
semantic_roles: Iterable[OntologyNode]) -> ActionDescription:
semantic_roles_set = immutableset(semantic_roles)
descriptions_for_action_type = self.action_to_description[action_type]
matching_descriptions = immutableset(
description for description in descriptions_for_action_type
if description.frame.semantic_roles == semantic_roles_set)
if matching_descriptions:
if len(matching_descriptions) == 1:
return only(matching_descriptions)
else:
raise RuntimeError(
f"Multiple action descriptions match action type "
f"{action_type} and roles {semantic_roles_set}")
else:
available_frames: Any = [
immutableset(description.frame.roles_to_variables.keys())
for description in descriptions_for_action_type
]
raise RuntimeError(
f"No action descriptions match action type "
f"{action_type} and roles {semantic_roles_set}. "
f"Known frames for {action_type} are "
f"{available_frames}")
def _make_on_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("chair", CHAIR)
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 On",
chain(*[
flatten([
sampled(
_on_template(
figure,
ground,
make_noise_objects(noise_objects),
is_training=True,
),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=num_samples if num_samples else 5,
) for figure in figures for ground in grounds
])
]),
language_generator=language_generator,
)
def candidate_relation_templates(
) -> Iterable[Tuple[AlignmentSlots, ...]]:
# This function returns templates fro the candidate relation templates
# terminology: (A)rgument - Noun, (F)ixedString - A collection or str tokens that can be a preposition or localiser/coverb, etc.
# Now, handle two arguments with one function string (e.g. a ball on a table)
for output in immutableset(
itertools.permutations(
[
AlignmentSlots.Argument,
AlignmentSlots.Argument,
AlignmentSlots.FixedString,
],
3,
)):
yield output
# Now, handle two arguments with two function strings (e.g. chyuou dzai zhouzi shang)
for output in immutableset(
itertools.permutations(
[
AlignmentSlots.Argument,
AlignmentSlots.Argument,
AlignmentSlots.FixedString,
AlignmentSlots.FixedString,
],
4,
)):
yield output
def test_empty_singleton(self):
empty1 = immutableset()
empty2 = immutableset()
self.assertIs(empty1, empty2)
empty3 = ImmutableSet.builder().build()
self.assertIs(empty1, empty3)
empty4 = immutableset([])
self.assertIs(empty1, empty4)
def test_symmetric_difference(self):
s1 = immutableset([1, 2, 3, 8])
s2 = immutableset([1, 2, 3, 4, 5, 6, 7])
s3 = immutableset([4, 5, 6, 7, 8])
self.assertEqual(s1, s2 ^ s3)
self.assertEqual(s1, s3 ^ s2)
self.assertEqual(s1, s2.symmetric_difference(s3))
self.assertEqual(s1, s3.symmetric_difference(s2))
def run_python_on_args(
self,
job_name: Locator,
python_module_or_path: Any,
set_args: str,
*,
depends_on,
resource_request: Optional[ResourceRequest] = None,
override_conda_config: Optional[CondaConfiguration] = None,
category: Optional[str] = None,
use_pypy: bool = False,
job_is_stageable: bool = False,
job_bypass_staging: bool = False,
pre_job_bash: str = "",
post_job_bash: str = "",
times_to_retry_job: int = 0,
container: Optional[Container] = None,
job_profiles: Iterable[PegasusProfile] = immutableset(),
input_file_paths: Union[Iterable[Union[Path, str]], Path,
str] = immutableset(),
output_file_paths: Union[Iterable[Union[Path, str]], Path,
str] = immutableset(),
) -> DependencyNode:
"""
Schedule a job to run the given *python_script* with the given *set_args*.
If this job requires other jobs to be executed first,
include them in *depends_on*.
This method returns a `DependencyNode` which can be used in *depends_on*
for future jobs.
`pre_job_bash` and `post_job_bash` are not provided as editable fields to append
and additional job into this python job. Scoring, Post-Processing, Etc should be
its own job. They are provided to allow for cases like 'export PYTHONPATH={path}'
where a job expects environment variables to be set.
"""
return self._run_python_job(
job_name,
python_module_or_path,
set_args,
depends_on=depends_on,
resource_request=resource_request,
override_conda_config=override_conda_config,
category=category,
use_pypy=use_pypy,
container=container,
pre_job_bash=pre_job_bash,
post_job_bash=post_job_bash,
job_is_stageable=job_is_stageable,
job_bypass_staging=job_bypass_staging,
times_to_retry_job=times_to_retry_job,
job_profiles=job_profiles,
treat_params_as_cmd_args=True,
input_file_paths=input_file_paths,
output_file_paths=output_file_paths,
)
class ActionDescription:
frame: ActionDescriptionFrame = attrib(
validator=instance_of(ActionDescriptionFrame), kw_only=True
)
# nested generic in optional seems to be confusing mypy
during: Optional[DuringAction[ActionDescriptionVariable]] = attrib( # type: ignore
validator=optional(instance_of(DuringAction)), default=None, kw_only=True
)
# conditions which hold both before and after the action
enduring_conditions: ImmutableSet[Relation[ActionDescriptionVariable]] = attrib(
converter=flatten_relations, default=immutableset(), kw_only=True
)
# Preconditions
preconditions: ImmutableSet[Relation[ActionDescriptionVariable]] = attrib(
converter=flatten_relations, default=immutableset(), kw_only=True
)
# Postconditions
postconditions: ImmutableSet[Relation[ActionDescriptionVariable]] = attrib(
converter=flatten_relations, default=immutableset(), kw_only=True
)
# Asserted properties of objects in action
asserted_properties: ImmutableSetMultiDict[
ActionDescriptionVariable, OntologyNode
] = attrib(
converter=_to_immutablesetmultidict, default=immutablesetmultidict(), kw_only=True
)
auxiliary_variables: ImmutableSet[ActionDescriptionVariable] = attrib(init=False)
"""
These are variables which do not occupy semantic roles
but are are still referred to by conditions, paths, etc.
An example would be the container for liquid for a "drink" action.
"""
def __attrs_post_init__(self) -> None:
for relation in chain(
self.enduring_conditions, self.preconditions, self.postconditions
):
if not isinstance(relation, Relation):
raise RuntimeError(
f"All conditions on an action description ought to be Relations "
f"but got {relation}"
)
@auxiliary_variables.default
def _init_auxiliary_variables(self):
auxiliary_variables: List[ActionDescriptionVariable] = []
if self.during:
self.during.accumulate_referenced_objects(auxiliary_variables)
for relation in chain(
self.enduring_conditions, self.preconditions, self.postconditions
):
relation.accumulate_referenced_objects(auxiliary_variables)
return immutableset(
variable
for variable in auxiliary_variables
if variable not in self.frame.variables_to_roles
)
def test_pursuit_preposition_on_learner(language_mode):
rng = random.Random()
rng.seed(0)
learner = PrepositionPursuitLearner(
learning_factor=0.5,
graph_match_confirmation_threshold=0.7,
lexicon_entry_threshold=0.7,
rng=rng,
smoothing_parameter=0.001,
ontology=GAILA_PHASE_1_ONTOLOGY,
object_recognizer=LANGUAGE_MODE_TO_OBJECT_RECOGNIZER[language_mode],
language_mode=language_mode,
) # type: ignore
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
language_generator = phase1_language_generator(language_mode)
on_train_curriculum = phase1_instances(
"Preposition Unit Train",
situations=sampled(
_on_template(ball, table, immutableset(), is_training=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=10,
),
language_generator=language_generator,
)
on_test_curriculum = phase1_instances(
"Preposition Unit Test",
situations=sampled(
_on_template(ball, table, immutableset(), is_training=False),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=1,
),
language_generator=language_generator,
)
for (
_,
linguistic_description,
perceptual_representation,
) in on_train_curriculum.instances():
# Get the object matches first - preposition learner can't learn without already recognized objects
learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_lingustics_description,
test_perceptual_representation,
) in on_test_curriculum.instances():
descriptions_from_learner = learner.describe(
test_perceptual_representation)
gold = test_lingustics_description.as_token_sequence()
assert descriptions_from_learner
assert [
desc.as_token_sequence() for desc in descriptions_from_learner
][0] == gold
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 test_empty(self):
empty = immutableset()
self.assertEqual(0, len(empty))
empty2 = immutableset([])
self.assertEqual(0, len(empty2))
self.assertEqual(empty, empty2)
empty3 = ImmutableSet.builder().build()
self.assertEqual(0, len(empty3))
self.assertEqual(empty, empty3)
def generate_language(
self, situation: SituationT,
chooser: SequenceChooser) -> ImmutableSet[LinguisticDescriptionT]:
wrapped_result = self._wrapped_generator.generate_language(
situation, chooser)
if wrapped_result:
return immutableset([wrapped_result[0]])
else:
return immutableset()
def test_immutable(self):
source = [1, 2, 3]
set1 = immutableset(source)
with self.assertRaises(AttributeError):
# noinspection PyUnresolvedReferences
set1.add(4)
# Update doesn't affect original
source.append(4)
self.assertNotEqual(immutableset(source), set1)
def test_pursuit_preposition_over_learner(language_mode, learner):
ball = standard_object("ball", BALL)
table = standard_object("table", TABLE)
language_generator = phase1_language_generator(language_mode)
over_train_curriculum = phase1_instances(
"Preposition Over Unit Train",
situations=sampled(
_over_template(ball,
table,
immutableset(),
is_training=True,
is_distal=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=10,
block_multiple_of_the_same_type=True,
),
language_generator=language_generator,
)
over_test_curriculum = phase1_instances(
"Preposition Over Unit Test",
situations=sampled(
_over_template(ball,
table,
immutableset(),
is_training=False,
is_distal=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=1,
block_multiple_of_the_same_type=True,
),
language_generator=language_generator,
)
processing_learner = learner(language_mode)
for (
_,
linguistic_description,
perceptual_representation,
) in over_train_curriculum.instances():
processing_learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_linguistic_description,
test_perceptual_representation,
) in over_test_curriculum.instances():
descriptions_from_learner = processing_learner.describe(
test_perceptual_representation)
gold = test_linguistic_description.as_token_sequence()
assert descriptions_from_learner
assert gold in [
desc.as_token_sequence() for desc in descriptions_from_learner
]
def test_pursuit_preposition_in_learner(language_mode):
rng = random.Random()
rng.seed(0)
learner = PrepositionPursuitLearner(
learning_factor=0.5,
graph_match_confirmation_threshold=0.7,
lexicon_entry_threshold=0.7,
rng=rng,
smoothing_parameter=0.001,
ontology=GAILA_PHASE_1_ONTOLOGY,
object_recognizer=LANGUAGE_MODE_TO_OBJECT_RECOGNIZER[language_mode],
language_mode=language_mode,
) # type: ignore
water = object_variable("water", WATER)
cup = standard_object("cup", CUP)
language_generator = phase1_language_generator(language_mode)
in_train_curriculum = phase1_instances(
"Preposition In Unit Train",
situations=sampled(
_in_template(water, cup, immutableset(), is_training=True),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=10,
),
language_generator=language_generator,
)
in_test_curriculum = phase1_instances(
"Preposition In Unit Test",
situations=sampled(
_in_template(water, cup, immutableset(), is_training=False),
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=1,
),
language_generator=language_generator,
)
for (
_,
linguistic_description,
perceptual_representation,
) in in_train_curriculum.instances():
learner.observe(
LearningExample(perceptual_representation, linguistic_description))
for (
_,
test_linguistic_description,
test_perceptual_representation,
) in in_test_curriculum.instances():
descriptions_from_learner = learner.describe(
test_perceptual_representation)
gold = test_linguistic_description.as_token_sequence()
assert descriptions_from_learner
assert [
desc.as_token_sequence() for desc in descriptions_from_learner
][0] == gold
def _read_keys_from_keys_file(zip_file: ZipFile) -> Optional[AbstractSet[str]]:
try:
keys_data = zip_file.read("__keys")
if keys_data:
return immutableset(keys_data.decode("utf-8").split("\n"))
else:
# If keys_data is empty, the "split" above will return [''], which is wrong.
return immutableset()
except KeyError:
return None
def make_noise_objects(
noise_objects: Optional[int],
banned_ontology_types: Iterable[OntologyNode] = immutableset(),
) -> Iterable[TemplateObjectVariable]:
return immutableset(
standard_object(
f"noise_object_{x}",
banned_properties=[IS_SPEAKER, IS_ADDRESSEE],
banned_ontology_types=banned_ontology_types,
) for x in range(noise_objects if noise_objects else 0))
def test_issuperset(self):
empty = immutableset()
s1 = immutableset([1, 2, 3, 3, 2, 1])
s2 = immutableset([1, 2, 3, 4])
self.assertFalse(s1.issuperset(s2))
self.assertTrue(s2.issuperset(s1))
self.assertFalse(empty.issuperset(s1))
# issuperset is >=, not >
self.assertTrue(empty.issuperset(empty))
self.assertTrue(s1.issuperset(empty))
def generate_language(
self, situation: SituationT,
chooser: SequenceChooser) -> ImmutableSet[LinguisticDescriptionT]:
wrapped_result = self._wrapped_generator.generate_language(
situation, chooser)
if wrapped_result:
# noinspection PyTypeChecker
return immutableset(
[self._sequence_chooser.choice(wrapped_result)])
else:
return immutableset()
class ObjectStructuralSchema(HasAxes, MaybeHasGeon):
r"""
A hierarchical representation of the internal structure of some type of object.
An `ObjectStructuralSchema` represents the general pattern of the structure of an object,
rather than the structure of any particular object
(e.g. people in general, rather than a particular person).
For example a person's body is made up of a head, torso, left arm, right arm, left leg, and
right leg. These sub-objects have various relations to one another
(e.g. the head is above and supported by the torso).
Declaring an `ObjectStructuralSchema` can be verbose;
see `Relation`\ s for additional tips on how to make this more compact.
"""
ontology_node: OntologyNode = attrib(validator=instance_of(OntologyNode))
"""
The `OntologyNode` this `ObjectStructuralSchema` represents the structure of.
"""
sub_objects: ImmutableSet["SubObject"] = attrib(converter=_to_immutableset,
default=immutableset())
r"""
The component parts which make up an object of the type *parent_object*.
These `SubObject`\ s themselves wrap `ObjectStructuralSchema`\ s
and can therefore themselves have complex internal structure.
"""
sub_object_relations: ImmutableSet[Relation["SubObject"]] = attrib(
converter=_to_immutableset, default=immutableset())
r"""
A set of `Relation`\ s which define how the `SubObject`\ s relate to one another.
"""
geon: Optional[Geon] = attrib(validator=optional(instance_of(Geon)),
default=None,
kw_only=True)
axes: Axes = attrib(validator=instance_of(Axes), kw_only=True)
@axes.default
def _init_axes(self) -> Axes:
if self.geon:
return self.geon.axes
else:
raise RuntimeError(
"If a geon is not give for a structural schema, "
"then axes must be explicitly specified")
def __attrs_post_init__(self) -> None:
for sub_object in self.sub_objects:
if sub_object.schema.axes == self.axes:
raise RuntimeError(
"Don't share axes between schemata. If the axes should be "
"similar, consider using .copy()")
