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 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_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 all_possible_test(
template: Phase1SituationTemplate
) -> Iterable[HighLevelSemanticsSituation]:
"""
Shorcut for `all_possible` with the GAILA phase 1 ontology and a `RandomChooser` with seed 0.
"""
return all_possible(template,
chooser=RandomChooser.for_seed(0),
ontology=GAILA_PHASE_1_ONTOLOGY)
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 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_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 run_subset_learner_for_object(
nodes: Iterable[OntologyNode], *, learner,
language_generator: LanguageGenerator[HighLevelSemanticsSituation,
LinearizedDependencyTree]):
colored_obj_objects = [
object_variable("obj-with-color",
node,
added_properties=[color_variable("color")])
for node in nodes
]
obj_templates = [
Phase1SituationTemplate(
"colored-obj-object",
salient_object_variables=[colored_obj_object],
syntax_hints=[IGNORE_COLORS],
) for colored_obj_object in colored_obj_objects
]
obj_curriculum = phase1_instances(
"all obj situations",
flatten([
all_possible(
obj_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
) for obj_template in obj_templates
]),
language_generator=language_generator,
)
test_obj_curriculum = phase1_instances(
"obj test",
situations=sampled(
obj_templates[0],
chooser=PHASE1_TEST_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
max_to_sample=1,
),
language_generator=language_generator,
)
for training_stage in [obj_curriculum]:
for (
_,
linguistic_description,
perceptual_representation,
) in training_stage.instances():
learner.observe(
LearningExample(perceptual_representation,
linguistic_description))
for test_instance_group in [test_obj_curriculum]:
for (
_,
test_instance_language,
test_instance_perception,
) in test_instance_group.instances():
descriptions_from_learner = learner.describe(
test_instance_perception)
gold = test_instance_language.as_token_sequence()
assert gold in [
desc.as_token_sequence() for desc in descriptions_from_learner
]
def test_pursuit_object_learner_with_gaze(language_mode):
target_objects = [
BALL,
# PERSON,
# CHAIR,
# TABLE,
DOG,
# BIRD,
BOX,
]
language_generator = phase1_language_generator(language_mode)
target_test_templates = []
for obj in target_objects:
# Create train and test templates for the target objects
test_obj_object = object_variable("obj-with-color", obj)
test_template = Phase1SituationTemplate(
"colored-obj-object",
salient_object_variables=[test_obj_object],
syntax_hints=[IGNORE_COLORS],
gazed_objects=[test_obj_object],
)
target_test_templates.extend(
all_possible(
test_template,
chooser=PHASE1_CHOOSER_FACTORY(),
ontology=GAILA_PHASE_1_ONTOLOGY,
))
rng = random.Random()
rng.seed(0)
# We can use this to generate the actual pursuit curriculum
train_curriculum = make_simple_pursuit_curriculum(
target_objects=target_objects,
num_instances=30,
num_objects_in_instance=3,
num_noise_instances=0,
language_generator=language_generator,
add_gaze=True,
)
test_obj_curriculum = phase1_instances(
"obj test",
situations=target_test_templates,
language_generator=language_generator,
)
# All parameters should be in the range 0-1.
# Learning factor works better when kept < 0.5
# Graph matching threshold doesn't seem to matter that much, as often seems to be either a
# complete or a very small match.
# The lexicon threshold works better between 0.07-0.3, but we need to play around with it because we end up not
# lexicalize items sufficiently because of diminishing lexicon probability through training
rng = random.Random()
rng.seed(0)
learner = IntegratedTemplateLearner(object_learner=PursuitObjectLearnerNew(
learning_factor=0.05,
graph_match_confirmation_threshold=0.7,
lexicon_entry_threshold=0.7,
rng=rng,
smoothing_parameter=0.002,
ontology=GAILA_PHASE_1_ONTOLOGY,
language_mode=language_mode,
rank_gaze_higher=True,
))
for training_stage in [train_curriculum]:
for (
_,
linguistic_description,
perceptual_representation,
) in training_stage.instances():
learner.observe(
LearningExample(perceptual_representation,
linguistic_description))
for test_instance_group in [test_obj_curriculum]:
for (
_,
test_instance_language,
test_instance_perception,
) in test_instance_group.instances():
logging.info("lang: %s", test_instance_language)
descriptions_from_learner = learner.describe(
test_instance_perception)
gold = test_instance_language.as_token_sequence()
assert gold in [
desc.as_token_sequence() for desc in descriptions_from_learner
]
def test_copy_with_temporal_scope_pattern_content():
"""
Tests whether copy_with_temporal_scope converts patterns 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]))
perception_pattern = PerceptionGraphPattern.from_graph(
perception_graph).perception_graph_pattern
temporal_perception_graph = perception_graph.copy_with_temporal_scopes(
temporal_scopes=[TemporalScope.AFTER])
temporal_perception_pattern = perception_pattern.copy_with_temporal_scopes(
required_temporal_scopes=TemporalScope.AFTER)
# Exception while applying to dynamic pattern
with pytest.raises(RuntimeError):
temporal_perception_pattern.copy_with_temporal_scopes(
required_temporal_scopes=TemporalScope.AFTER)
for (source, target) in perception_pattern.copy_as_digraph().edges():
assert not isinstance(
perception_pattern.copy_as_digraph()[source][target]["predicate"],
HoldsAtTemporalScopePredicate,
)
for (source,
target) in temporal_perception_pattern.copy_as_digraph().edges():
# Check type, and then the content
predicate = temporal_perception_pattern.copy_as_digraph(
)[source][target]["predicate"]
# Test HoldsAtTemporalScope dot label, matches predicate
assert isinstance(predicate.dot_label(), str)
assert predicate.matches_predicate(
HoldsAtTemporalScopePredicate(predicate.wrapped_edge_predicate,
predicate.temporal_scopes))
assert not predicate.matches_predicate(
HoldsAtTemporalScopePredicate(predicate.wrapped_edge_predicate,
[TemporalScope.BEFORE]))
assert isinstance(predicate, HoldsAtTemporalScopePredicate)
assert (predicate.wrapped_edge_predicate == perception_pattern.
copy_as_digraph()[source][target]["predicate"])
assert len(predicate.temporal_scopes) == 1
assert only(predicate.temporal_scopes) == TemporalScope.AFTER
# Test normal matching behavior
temporal_matcher = temporal_perception_pattern.matcher(
temporal_perception_graph, match_mode=MatchMode.NON_OBJECT)
first(temporal_matcher.matches(use_lookahead_pruning=True))
# Test HoldsAtTemporalScopePredicate
for (source, target) in perception_graph.copy_as_digraph().edges():
label = "test edge label"
edge_predicate = AnyEdgePredicate()
temporal_predicate = HoldsAtTemporalScopePredicate(
edge_predicate, [TemporalScope.AFTER])
temporal_edge_label = TemporallyScopedEdgeLabel(
label, [TemporalScope.AFTER])
assert temporal_predicate(source, temporal_edge_label, target)
# Non temporal edge exception
with pytest.raises(RuntimeError):
temporal_predicate(source, label, target)
def test_allowed_matches_with_bad_partial_match():
"""
Tests whether PatternMarching's allowed_matches functionality works as intended when a bad
partial match is specified.
"""
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_without_learner(
PerceptionGraph.from_frame(perceptual_representation.frames[0]))
pattern1: PerceptionGraphPattern = PerceptionGraphPattern.from_graph(
perception.subgraph_by_nodes({
cast(PerceptionGraphNode, node)
for node in perception._graph.nodes # pylint: disable=protected-access
if getattr(node, "debug_handle", None) == "box_0"
})).perception_graph_pattern
pattern2: PerceptionGraphPattern = PerceptionGraphPattern.from_graph(
perception.subgraph_by_nodes({
cast(PerceptionGraphNode, node)
for node in perception._graph.nodes # pylint: disable=protected-access
if getattr(node, "debug_handle", None) in {"box_0", "the ground"}
})).perception_graph_pattern
pattern1_box: AnyObjectPerception = cast(
AnyObjectPerception,
only(node for node in pattern1._graph # pylint: disable=protected-access
if getattr(node, "debug_handle", None) == "box_0"),
)
pattern2_box: AnyObjectPerception = cast(
AnyObjectPerception,
only(node for node in pattern2._graph # pylint: disable=protected-access
if getattr(node, "debug_handle", None) == "box_0"),
)
pattern2_ground: AnyObjectPerception = cast(
AnyObjectPerception,
only(node for node in pattern2._graph # pylint: disable=protected-access
if getattr(node, "debug_handle", None) == "the ground"),
)
matcher = PatternMatching(
pattern=pattern1,
graph_to_match_against=pattern2,
matching_pattern_against_pattern=True,
match_mode=MatchMode.OBJECT,
allowed_matches=immutablesetmultidict([(pattern1_box, pattern2_box)]),
)
with pytest.raises(RuntimeError):
first(
matcher.matches(
initial_partial_match={pattern1_box: pattern2_ground},
use_lookahead_pruning=True,
),
None,
)
def test_syntactically_infeasible_partial_match():
"""
Tests whether syntactic feasibility works 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]
# Original perception graph
perception = graph_without_learner(
PerceptionGraph.from_frame(perceptual_representation.frames[0]))
# Create an altered perception graph we remove the color node
altered_perception_digraph = perception.copy_as_digraph()
nodes = []
for node in perception.copy_as_digraph().nodes:
# If we find a color node, we add an extra edge to it
if isinstance(node, tuple) and isinstance(node[0], RgbColorPerception):
nodes.append(node)
# change edge information
for node in nodes:
random_node = r.choice(list(altered_perception_digraph.nodes))
altered_perception_digraph.add_edge(node, random_node, label=PART_OF)
random_node_2 = r.choice(list(altered_perception_digraph.nodes))
altered_perception_digraph.add_edge(random_node_2, node, label=PART_OF)
altered_perception_perception_graph = PerceptionGraph(
altered_perception_digraph)
altered_perception_pattern = PerceptionGraphPattern.from_graph(
altered_perception_perception_graph).perception_graph_pattern
# Start the matching process, get a partial match
first_matcher = altered_perception_pattern.matcher(
altered_perception_perception_graph, match_mode=MatchMode.OBJECT)
partial_match: PerceptionGraphPatternMatch = first(
first_matcher.matches(use_lookahead_pruning=True), None)
partial_mapping = partial_match.pattern_node_to_matched_graph_node
# Try to extend the partial mapping, we expect a semantic infeasibility runtime error
second_matcher = altered_perception_pattern.matcher(
perception, match_mode=MatchMode.OBJECT)
# The partial mapping (obtained from first matcher with original perception graph)
# syntactically doesn't match the one in the altered version (second matcher with altered graph)
with pytest.raises(RuntimeError):
first(
second_matcher.matches(initial_partial_match=partial_mapping,
use_lookahead_pruning=True),
None,
)
def test_semantically_infeasible_partial_match():
"""
Tests whether semantic feasibility works as intended
"""
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 = graph_without_learner(
PerceptionGraph.from_frame(perceptual_representation.frames[0]))
whole_perception_pattern = PerceptionGraphPattern.from_graph(
perception).perception_graph_pattern
# Create an altered perception graph we remove the color node
altered_perception_digraph = perception.copy_as_digraph()
nodes_to_remove = []
edges = []
different_nodes = []
for node in perception.copy_as_digraph().nodes:
# If we find a color node, we make it black
if isinstance(node, tuple) and isinstance(node[0], RgbColorPerception):
new_node = (RgbColorPerception(0, 0, 0), node[1])
# Get edge information
for edge in perception.copy_as_digraph().edges(data=True):
if edge[0] == node:
edges.append((new_node, edge[1], edge[2]))
if edge[1] == node:
edges.append((edge[0], new_node, edge[2]))
nodes_to_remove.append(node)
different_nodes.append(new_node)
# remove original node
altered_perception_digraph.remove_nodes_from(nodes_to_remove)
# add new nodes
for node in different_nodes:
altered_perception_digraph.add_node(node)
# add edge information
for edge in edges:
altered_perception_digraph.add_edge(edge[0], edge[1])
for k, v in edge[2].items():
altered_perception_digraph[edge[0]][edge[1]][k] = v
altered_perception_pattern = PerceptionGraphPattern.from_graph(
PerceptionGraph(altered_perception_digraph)).perception_graph_pattern
partial_digraph = altered_perception_pattern.copy_as_digraph()
partial_digraph.remove_nodes_from([
node for node in partial_digraph.nodes
if isinstance(node, IsColorNodePredicate)
])
# Start the matching process, get a partial match
matcher = whole_perception_pattern.matcher(perception,
match_mode=MatchMode.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, we expect a semantic infeasibility runtime error
second_matcher = whole_perception_pattern.matcher(
PerceptionGraph(altered_perception_digraph),
match_mode=MatchMode.OBJECT)
# The partial mapping (obtained from first matcher with original perception graph)
# semantically doesn't match the one in the altered version (second matcher with altered graph)
with pytest.raises(RuntimeError):
first(
second_matcher.matches(initial_partial_match=partial_mapping,
use_lookahead_pruning=True),
None,
)
def test_last_failed_pattern_node():
"""
Tests whether `MatchFailure` can find the correct node.
"""
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)
for (_, _, perceptual_representation) in train_curriculum.instances():
# Original perception graph
perception = graph_without_learner(
PerceptionGraph.from_frame(perceptual_representation.frames[0]))
# Original perception pattern
whole_perception_pattern = PerceptionGraphPattern.from_graph(
perception).perception_graph_pattern
# Create an altered perception graph we replace the color node
altered_perception_digraph = perception.copy_as_digraph()
nodes_to_remove = []
edges = []
different_nodes = []
for node in perception.copy_as_digraph().nodes:
# If we find a color node, we make it black
if isinstance(node, tuple) and isinstance(node[0],
RgbColorPerception):
new_node = (RgbColorPerception(0, 0, 0), 42)
# Get edge information
for edge in perception.copy_as_digraph().edges(data=True):
if edge[0] == node:
edges.append((new_node, edge[1], edge[2]))
if edge[1] == node:
edges.append((edge[0], new_node, edge[2]))
nodes_to_remove.append(node)
different_nodes.append(new_node)
# add new nodes
for node in different_nodes:
altered_perception_digraph.add_node(node)
# add edge information
for edge in edges:
altered_perception_digraph.add_edge(edge[0], edge[1])
for k, v in edge[2].items():
altered_perception_digraph[edge[0]][edge[1]][k] = v
# remove original node
altered_perception_digraph.remove_nodes_from(nodes_to_remove)
# Start the matching process
matcher = whole_perception_pattern.matcher(
PerceptionGraph(altered_perception_digraph),
match_mode=MatchMode.NON_OBJECT)
match_or_failure = matcher.first_match_or_failure_info()
assert isinstance(match_or_failure, PatternMatching.MatchFailure)
assert isinstance(match_or_failure.last_failed_pattern_node,
IsColorNodePredicate)