def test_object_recognition_with_drink_perception():
"""
Regression test to confirm we can perform object recognition on a pickled and unpickled "drink"
perception. If we do this using the normal pickling interface we get an error. This test checks
that we don't run into such an error when we instead pickle and unpickle the perception using
the AdamPickler and AdamUnpickler.
See https://github.com/isi-vista/adam/issues/958.
"""
language_mode = LanguageMode.ENGLISH
template = drink_test_template()
curriculum = phase1_instances(
"train",
sampled(
template,
max_to_sample=3,
ontology=GAILA_PHASE_1_ONTOLOGY,
chooser=PHASE1_CHOOSER_FACTORY(),
block_multiple_of_the_same_type=True,
),
language_generator=phase1_language_generator(language_mode),
)
object_recognizer = LANGUAGE_MODE_TO_TEMPLATE_LEARNER_OBJECT_RECOGNIZER[
language_mode]
learner = IntegratedTemplateLearner(object_learner=object_recognizer)
for (_, linguistic_description,
perceptual_representation) in curriculum.instances():
new_perceptual_representation = _pickle_and_unpickle_object(
perceptual_representation)
learner.observe(
LearningExample(new_perceptual_representation,
linguistic_description))
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
]