def main(params: Parameters):
curriculum_repository_path = params.creatable_directory(
CURRICULUM_REPOSITORY_PATH_PARAMETER
)
language_mode = params.enum(
LANGUAGE_MODE_PARAMETER, LanguageMode, default=LanguageMode.ENGLISH
)
train_curriculum, test_curriculum = curriculum_from_params(
params, language_mode=language_mode
)
strict_curriculum = ExperimentCurriculum(
evaluate_curriculum(train_curriculum), evaluate_curriculum(test_curriculum)
)
write_experiment_curriculum(
curriculum_repository_path,
params,
language_mode,
strict_curriculum,
ignored_parameters=immutableset(
IGNORED_PARAMETERS.union(
{CURRICULUM_REPOSITORY_PATH_PARAMETER, LANGUAGE_MODE_PARAMETER}
)
),
)
if __name__ == "__main__":
parameters_only_entry_point(main)
})
run_python_on_parameters(
experiment_name,
log_experiment_script,
experiment_params,
depends_on=[],
)
write_workflow_description()
EXPERIMENT_NAME_FORMAT = (
"{num_instances:d}_instances-{num_noise_instances:d}_noise_instances-{num_objects_in_instance:d}_"
"objects_in_instance-{prob_given:.3f}_given-{prob_not_given:.3f}_not_given-{add_gaze}_gaze"
)
FIXED_PARAMETERS = {
"curriculum": "pursuit",
"learner": "pursuit-gaze",
"pursuit": {
"learning_factor": 0.05,
"graph_match_confirmation_threshold": 0.7,
"lexicon_entry_threshold": 0.7,
"smoothing_parameter": 0.001,
},
}
if __name__ == "__main__":
parameters_only_entry_point(gaze_ablation_runner_entry_point)
viz,
)
except RuntimeError as err:
print(f"uncaught exception: {err}")
else:
# render phase 1 scenes:
root_output_directory = params.optional_creatable_directory(
"screenshot_directory")
assert root_output_directory is not None
if not os.path.isdir(root_output_directory):
os.mkdir(root_output_directory)
for idx, instance_group in enumerate(
build_curriculum(None, None,
GAILA_PHASE_1_LANGUAGE_GENERATOR)):
# do any filtering here
if instance_group.name() in EXCLUDED_CURRICULA:
continue
directory_name = f"{idx:03}-{instance_group.name()}"
if not os.path.isdir(root_output_directory / directory_name):
os.mkdir(root_output_directory /
directory_name) # type: ignore
# then call some function from make_scenes.py to run the curriculum
make_scenes(params, [instance_group],
root_output_directory / directory_name, viz)
if __name__ == "__main__":
parameters_only_entry_point(main, usage_message=USAGE_MESSAGE)
depends_on=[build_container],
job_profiles=[job_profile],
resource_request=saga31_request,
container=python36,
input_file_paths=[input_file],
output_file_paths=[add_y_output_file_nas],
)
sort_job = run_python_on_parameters(
job_locator / "sort",
sort_nums_in_file,
{"input_file": add_y_output_file_nas, "output_file": sorted_output_file_nas},
depends_on=[add_y_job],
container=python36,
job_profiles=[job_profile],
resource_request=saga31_request,
input_file_paths=add_y_output_file_nas,
output_file_paths=sorted_output_file_nas,
)
_ = stop_docker_as_service(
mongo4_4, depends_on=[start_mongo, sort_job], resource_request=saga31_request
)
# Generate the Pegasus DAX file & a Submit Script
write_workflow_description(tmp_path)
if __name__ == "__main__":
parameters_only_entry_point(example_workflow)
)
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 [],
)
if __name__ == "__main__":
parameters_only_entry_point(log_experiment_entry_point)
CURRICULUM_NAME_FORMAT = (
"noise@{noise}-shuffled@{shuffled}-attributes@{attributes}-relations@{relations}"
)
CURRICULUM_PARAMS = {
"block_multiple_of_same_type": True,
"include_targets_in_noise": False,
"min_noise_objects": 1,
"max_noise_objects": 10,
"min_noise_relations": 0,
"max_noise_relations": 5,
"random_seed": 0,
"chooser_seed": 0,
}
FIXED_PARAMETERS = {
"curriculum": "m18-integrated-learners-experiment",
"learner": "integrated-learner-params",
"action_learner": {
"learner_type": "none"
},
"plural_learner": {
"learner_type": "none"
},
"include_functional_learner": False,
"include_generics_learner": False,
}
if __name__ == "__main__":
parameters_only_entry_point(integrated_experiment_entry_point)
FILE_NAME_STRING = (
"pursuit-ablating-gaze-{num_instances:d}_instances-{num_noise_instances:d}_noise_instances-{num_objects_in_instance:d}_"
"objects_in_instance-{prob_given:.3f}_given-{prob_not_given:.3f}_not_given-{add_gaze}_gaze"
)
PARAM_FILE_STRING = """_includes:
- "../../root.params"
- "m13.params"
experiment: '{experiment}'
curriculum: pursuit
learner: pursuit-gaze
accuracy_to_txt : True
pursuit:
learning_factor: 0.05
graph_match_confirmation_threshold: 0.7
lexicon_entry_threshold: 0.7
smoothing_parameter: .001
pursuit-curriculum-params:
num_instances: {num_instances:d}
num_noise_instances: {num_noise_instances:d}
num_objects_in_instance: {num_objects_in_instance:d}
add_gaze : {add_gaze}
prob_given : {prob_given:.3f}
prob_not_given : {prob_not_given:.3f}"""
if __name__ == "__main__":
parameters_only_entry_point(create_gaze_ablation_entry_point)
FIXED_PARAMETERS = {
"curriculum": "m15-object-noise-experiments",
"learner": "integrated-learner-params",
"post_observer": {
"include_acc_observer": False,
"include_pr_observer": True,
"log_pr": True,
},
"attribute_learner": {
"learner_type": "none"
},
"relation_learner": {
"learner_type": "none"
},
"action_learner": {
"learner_type": "none"
},
"plural_learner": {
"learner_type": "none"
},
"include_functional_learner": False,
"include_generics_learner": False,
"test_observer": {
"accuracy_to_txt": True
},
}
if __name__ == "__main__":
parameters_only_entry_point(object_language_ablation_runner_entry_point)
