def from_parameters(params: Parameters) -> ResourceRequest:
return SlurmResourceRequest(
partition=params.string("partition"),
num_cpus=params.optional_positive_integer("num_cpus"),
num_gpus=params.optional_integer("num_gpus"),
memory=MemoryAmount.parse(params.string("memory"))
if "memory" in params
else None,
job_time_in_minutes=params.optional_integer("job_time_in_minutes"),
)
def from_parameters(
params: Parameters,
*,
graph_logger: Optional[HypothesisLogger] = None
) -> "ObjectPursuitLearner": # type: ignore
log_word_hypotheses_dir = params.optional_creatable_directory(
"log_word_hypotheses_dir")
if log_word_hypotheses_dir:
logging.info("Hypotheses will be logged to %s",
log_word_hypotheses_dir)
rng = Random()
rng.seed(params.optional_integer("random_seed", default=0))
return ObjectPursuitLearner(
learning_factor=params.floating_point("learning_factor"),
graph_match_confirmation_threshold=params.floating_point(
"graph_match_confirmation_threshold"),
lexicon_entry_threshold=params.floating_point(
"lexicon_entry_threshold"),
smoothing_parameter=params.floating_point("smoothing_parameter"),
hypothesis_logger=graph_logger,
log_learned_item_hypotheses_to=log_word_hypotheses_dir,
rng=rng,
ontology=GAILA_PHASE_1_ONTOLOGY,
language_mode=params.enum("language_mode",
LanguageMode,
default=LanguageMode.ENGLISH),
)
def from_params(
params: Parameters,
*,
ontology: Optional[Ontology] = None,
graph_logger: Optional[GraphLogger] = None,
) -> "ProposeButVerifyObjectLearner":
rng = RandomChooser.for_seed(
params.optional_integer("random_seed", default=0))
return ProposeButVerifyObjectLearner(
graph_match_confirmation_threshold=params.floating_point(
"graph_match_confirmation_threshold", default=0.8),
graph_logger=graph_logger,
rng=rng,
ontology=ontology if ontology else GAILA_PHASE_1_ONTOLOGY,
language_mode=params.enum("language_mode",
LanguageMode,
default=LanguageMode.ENGLISH),
)
def build_object_learner_factory(
params: Parameters, beam_size: int,
language_mode: LanguageMode) -> TemplateLearner:
learner_type = params.string(
"learner_type",
valid_options=[
"subset", "pbv", "cross-situational", "pursuit", "recognizer"
],
default="subset",
)
ontology, objects, perception_gen = ONTOLOGY_STR_TO_ONTOLOGY[params.string(
"ontology",
valid_options=ONTOLOGY_STR_TO_ONTOLOGY.keys(),
default="phase2")]
if learner_type == "subset":
return SubsetObjectLearnerNew(ontology=ontology,
beam_size=beam_size,
language_mode=language_mode)
elif learner_type == "pbv":
chooser = RandomChooser.for_seed(
params.optional_integer("random_seed", default=0))
return ProposeButVerifyObjectLearner(
graph_match_confirmation_threshold=params.floating_point(
"graph_match_confirmation_threshold", default=0.8),
rng=chooser,
ontology=ontology,
language_mode=language_mode,
)
elif learner_type == "cross-situational":
return CrossSituationalObjectLearner(
graph_match_confirmation_threshold=params.floating_point(
"graph_match_confirmation_threshold"),
lexicon_entry_threshold=params.floating_point(
"lexicon_entry_threshold"),
smoothing_parameter=params.floating_point("smoothing_parameter"),
expected_number_of_meanings=len(
ontology.nodes_with_properties(THING)),
ontology=ontology,
language_mode=language_mode,
)
elif learner_type == "pursuit":
rng = random.Random()
rng.seed(params.integer("random_seed", default=0))
return PursuitObjectLearnerNew(
learning_factor=params.floating_point("learning_factor"),
graph_match_confirmation_threshold=params.floating_point(
"graph_match_confirmation_threshold"),
lexicon_entry_threshold=params.floating_point(
"lexicon_entry_threshold"),
rng=rng,
smoothing_parameter=params.floating_point("smoothing_parameter"),
ontology=ontology,
language_mode=language_mode,
)
elif learner_type == "recognizer":
object_recognizer = ObjectRecognizer.for_ontology_types(
objects,
determiners=ENGLISH_DETERMINERS,
ontology=ontology,
language_mode=language_mode,
perception_generator=perception_gen,
)
return ObjectRecognizerAsTemplateLearner(
object_recognizer=object_recognizer, language_mode=language_mode)
else:
raise RuntimeError("Object learner type invalid")
def curriculum_from_params(params: Parameters,
language_mode: LanguageMode = LanguageMode.ENGLISH):
str_to_train_test_curriculum: Mapping[str, Tuple[
CURRICULUM_BUILDER, Optional[CURRICULUM_BUILDER]]] = {
"m6-deniz": (make_m6_curriculum, None),
"each-object-by-itself": (
build_each_object_by_itself_curriculum_train,
build_each_object_by_itself_curriculum_test,
),
"pursuit": (
build_pursuit_curriculum,
build_each_object_by_itself_curriculum_test,
),
"m6-preposition": (build_m6_prepositions_curriculum, None),
"m9-objects": (build_gaila_phase1_object_curriculum, None),
"m9-attributes": (build_gaila_phase1_attribute_curriculum, None),
"chinese-classifiers": (build_classifier_curriculum, None),
"m9-relations": (build_gaila_phase1_relation_curriculum, None),
"m9-events": (build_gaila_phase1_verb_curriculum, None),
"m9-debug":
(build_debug_curriculum_train, build_debug_curriculum_test),
"m9-complete": (build_gaila_phase_1_curriculum, None),
"m13-imprecise-size": (make_imprecise_size_curriculum, None),
"m13-imprecise-temporal":
(make_imprecise_temporal_descriptions, None),
"m13-subtle-verb-distinction":
(make_subtle_verb_distinctions_curriculum, None),
"m13-object-restrictions":
(build_functionally_defined_objects_curriculum, None),
"m13-functionally-defined-objects": (
build_functionally_defined_objects_train_curriculum,
build_functionally_defined_objects_curriculum,
),
"m13-generics": (build_generics_curriculum, None),
"m13-complete": (build_gaila_m13_curriculum, None),
"m13-verbs-with-dynamic-prepositions": (
make_verb_with_dynamic_prepositions_curriculum,
None,
),
"m13-shuffled": (build_m13_shuffled_curriculum,
build_gaila_m13_curriculum),
"m13-relations": (make_prepositions_curriculum, None),
"actions-and-generics-curriculum":
(build_actions_and_generics_curriculum, None),
"m15-object-noise-experiments": (
build_object_learner_experiment_curriculum_train,
build_each_object_by_itself_curriculum_test,
),
"m18-integrated-learners-experiment": (
integrated_pursuit_learner_experiment_curriculum,
integrated_pursuit_learner_experiment_test,
),
}
curriculum_name = params.string("curriculum",
str_to_train_test_curriculum.keys())
language_generator = (
integrated_experiment_language_generator(language_mode)
if curriculum_name == "m18-integrated-learners-experiment" else
phase2_language_generator(language_mode))
if params.has_namespace("pursuit-curriculum-params"):
pursuit_curriculum_params = params.namespace(
"pursuit-curriculum-params")
else:
pursuit_curriculum_params = Parameters.empty()
use_path_instead_of_goal = params.boolean("use-path-instead-of-goal",
default=False)
(training_instance_groups,
test_instance_groups) = str_to_train_test_curriculum[curriculum_name]
num_samples = params.optional_positive_integer("num_samples")
# We need to be able to accept 0 as the number of noise objects but optional_integer doesn't currently
# support specifying a range of acceptable values: https://github.com/isi-vista/vistautils/issues/142
num_noise_objects = params.optional_integer("num_noise_objects")
if curriculum_name == "pursuit":
return (
training_instance_groups(
num_samples,
num_noise_objects,
language_generator,
pursuit_curriculum_params=pursuit_curriculum_params,
),
test_instance_groups(num_samples, num_noise_objects,
language_generator)
if test_instance_groups else [],
)
# optional argument to use path instead of goal
elif use_path_instead_of_goal and curriculum_name in [
"m13-complete",
"m13-shuffled",
"m13-verbs-with-dynamic-prepositions",
]:
return (
training_instance_groups(
num_samples,
num_noise_objects,
language_generator,
use_path_instead_of_goal,
),
test_instance_groups(num_samples, num_noise_objects,
language_generator)
if test_instance_groups else [],
)
elif curriculum_name in (
"m15-object-noise-experiments",
"m18-integrated-learners-experiment",
):
return (
training_instance_groups(
num_samples,
num_noise_objects,
language_generator,
params=params.namespace_or_empty("train_curriculum"),
),
test_instance_groups(
5,
0,
language_generator,
params=params.namespace_or_empty("test_curriculum"),
) if test_instance_groups else [],
)
return (
training_instance_groups(num_samples, num_noise_objects,
language_generator),
test_instance_groups(num_samples, num_noise_objects,
language_generator)
if test_instance_groups else [],
)
