def _extract_perception_graph(
self, perception: PerceptualRepresentation[DevelopmentalPrimitivePerceptionFrame]
) -> PerceptionGraph:
if perception.is_dynamic():
return PerceptionGraph.from_dynamic_perceptual_representation(perception)
else:
return PerceptionGraph.from_frame(perception.frames[0])
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_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 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 test_subset_learner_subobject():
mom = SituationObject.instantiate_ontology_node(
ontology_node=MOM, ontology=GAILA_PHASE_1_ONTOLOGY)
head = SituationObject.instantiate_ontology_node(
ontology_node=HEAD, ontology=GAILA_PHASE_1_ONTOLOGY)
hand = SituationObject.instantiate_ontology_node(
ontology_node=HAND, ontology=GAILA_PHASE_1_ONTOLOGY)
ball = SituationObject.instantiate_ontology_node(
ontology_node=BALL, ontology=GAILA_PHASE_1_ONTOLOGY)
house = SituationObject.instantiate_ontology_node(
ontology_node=HOUSE, ontology=GAILA_PHASE_1_ONTOLOGY)
ground = SituationObject.instantiate_ontology_node(
ontology_node=GROUND, ontology=GAILA_PHASE_1_ONTOLOGY)
mom_situation = HighLevelSemanticsSituation(
ontology=GAILA_PHASE_1_ONTOLOGY, salient_objects=immutableset([mom]))
floating_head_situation = HighLevelSemanticsSituation(
ontology=GAILA_PHASE_1_ONTOLOGY,
salient_objects=immutableset([head]),
other_objects=immutableset([ground]),
always_relations=flatten_relations(negate(on(head, ground))),
)
# Need to include some extra situations so that the learner will prune its semantics for 'a'
# away and not recognize it as an object.
floating_hand_situation = HighLevelSemanticsSituation(
ontology=GAILA_PHASE_1_ONTOLOGY,
salient_objects=immutableset([hand]),
other_objects=immutableset([ground]),
always_relations=flatten_relations(negate(on(hand, ground))),
)
floating_ball_situation = HighLevelSemanticsSituation(
ontology=GAILA_PHASE_1_ONTOLOGY,
salient_objects=immutableset([ball]),
other_objects=immutableset([ground]),
always_relations=flatten_relations(negate(on(ball, ground))),
)
floating_house_situation = HighLevelSemanticsSituation(
ontology=GAILA_PHASE_1_ONTOLOGY,
salient_objects=immutableset([house]),
other_objects=immutableset([ground]),
always_relations=flatten_relations(negate(on(house, ground))),
)
object_learner = SubsetObjectLearnerNew(ontology=GAILA_PHASE_1_ONTOLOGY,
beam_size=5,
language_mode=LanguageMode.ENGLISH)
for situation in [
mom_situation,
floating_head_situation,
floating_hand_situation,
floating_ball_situation,
floating_house_situation,
]:
perceptual_representation = GAILA_PHASE_1_PERCEPTION_GENERATOR.generate_perception(
situation, chooser=RandomChooser.for_seed(0))
for linguistic_description in GAILA_PHASE_1_LANGUAGE_GENERATOR.generate_language(
situation, chooser=RandomChooser.for_seed(0)):
perception_graph = PerceptionGraph.from_frame(
perceptual_representation.frames[0])
object_learner.learn_from(
LanguagePerceptionSemanticAlignment(
language_concept_alignment=LanguageConceptAlignment.
create_unaligned(language=linguistic_description),
perception_semantic_alignment=PerceptionSemanticAlignment(
perception_graph=perception_graph, semantic_nodes=[]),
))
mom_perceptual_representation = GAILA_PHASE_1_PERCEPTION_GENERATOR.generate_perception(
mom_situation, chooser=RandomChooser.for_seed(0))
perception_graph = PerceptionGraph.from_frame(
mom_perceptual_representation.frames[0])
enriched = object_learner.enrich_during_description(
PerceptionSemanticAlignment.create_unaligned(perception_graph))
semantic_node_types_and_debug_strings = {
(type(semantic_node), semantic_node.concept.debug_string)
for semantic_node in enriched.semantic_nodes
}
assert (ObjectSemanticNode, "Mom") in semantic_node_types_and_debug_strings
assert (ObjectSemanticNode,
"head") in semantic_node_types_and_debug_strings
assert (ObjectSemanticNode,
"hand") in semantic_node_types_and_debug_strings
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)
def do_object_on_table_test(
object_type_to_match: OntologyNode,
object_schema: ObjectStructuralSchema,
negative_object_ontology_node: OntologyNode,
):
"""
Tests the `PerceptionGraphMatcher` can match simple objects.
"""
# we create four situations:
# a object_to_match above or under a table with color red or blue
color = color_variable("color")
object_to_match = object_variable(
debug_handle=object_type_to_match.handle,
root_node=object_type_to_match,
added_properties=[color],
)
table = standard_object("table_0", TABLE)
object_on_table_template = Phase1SituationTemplate(
"object_to_match-on-table",
salient_object_variables=[object_to_match, table],
asserted_always_relations=[
bigger_than(table, object_to_match),
on(object_to_match, table),
],
)
object_under_table_template = Phase1SituationTemplate(
"object_to_match-under-table",
salient_object_variables=[object_to_match, table],
asserted_always_relations=[
bigger_than(table, object_to_match),
above(table, object_to_match),
],
)
# We test that a perceptual pattern for "object_to_match" matches in all four cases.
object_to_match_pattern = PerceptionGraphPattern.from_schema(
object_schema, perception_generator=GAILA_PHASE_1_PERCEPTION_GENERATOR)
situations_with_object_to_match = chain(
all_possible_test(object_on_table_template),
all_possible_test(object_under_table_template),
)
for (_,
situation_with_object) in enumerate(situations_with_object_to_match):
perception = GAILA_PHASE_1_PERCEPTION_GENERATOR.generate_perception(
situation_with_object, chooser=RandomChooser.for_seed(0))
perception_graph = PerceptionGraph.from_frame(perception.frames[0])
# perception_graph.render_to_file(f"object_to_match {idx}", out_dir / f"object_to_match
# -{idx}.pdf")
# object_to_match_pattern.render_to_file(f"object_to_match pattern", out_dir /
# "object_to_match_pattern.pdf")
matcher = object_to_match_pattern.matcher(perception_graph,
match_mode=MatchMode.OBJECT)
# debug_matching = matcher.debug_matching(
# use_lookahead_pruning=False, render_match_to=Path("/Users/gabbard/tmp")
# )
result = any(matcher.matches(use_lookahead_pruning=False))
if not result:
return False
# Now let's create the same situations, but substitute a negative_object for a object_to_match.
negative_object = object_variable(
debug_handle=negative_object_ontology_node.handle,
root_node=negative_object_ontology_node,
added_properties=[color],
)
negative_object_on_table_template = Phase1SituationTemplate(
"negative_object-on-table",
salient_object_variables=[negative_object, table],
asserted_always_relations=[
bigger_than(table, negative_object),
on(negative_object, table),
],
)
negative_object_under_table_template = Phase1SituationTemplate(
"negative_object-under-table",
salient_object_variables=[negative_object, table],
asserted_always_relations=[
bigger_than(table, negative_object),
above(table, negative_object),
],
)
situations_with_negative_object = chain(
all_possible_test(negative_object_on_table_template),
all_possible_test(negative_object_under_table_template),
)
# The pattern should now fail to match.
for situation_with_negative_object in situations_with_negative_object:
perception = GAILA_PHASE_1_PERCEPTION_GENERATOR.generate_perception(
situation_with_negative_object, chooser=RandomChooser.for_seed(0))
perception_graph = PerceptionGraph.from_frame(perception.frames[0])
if any(
object_to_match_pattern.matcher(
perception_graph, match_mode=MatchMode.OBJECT).matches(
use_lookahead_pruning=True)):
return False
return True
def _extract_perception_graph(
self, perception: PerceptualRepresentation[
DevelopmentalPrimitivePerceptionFrame]
) -> PerceptionGraph:
return PerceptionGraph.from_frame(perception.frames[0])