def classify_studies(studies):
# TODO: seems like a lot of nested loops here...revisit and optimize
for modality, study_paths in studies.items():
# Check to see if the case is a CT scan by seeing if the dicom modality is 'CT'
# or the DICOMDIR has multiple slices
# TODO: come up with a better solution for identifying CT scans
if modality == 'CT' or modality != 'XR':
for orthanc_id in study_paths:
study_db.save_study_type(orthanc_id, 'CT')
messaging_service.send_notification(f'Study {orthanc_id} ready', 'study_ready')
continue
# evaluate the study using classifier model
classifier_model = classifier_db.get_classifier_model(modality)
# check to see if there is currently a classifier set for the given modality
# if not just get the default one from the db
if classifier_model is None:
classifier_model = eval_db.get_default_model()
# run studies through the classifier model
eval_service.evaluate(classifier_model, study_paths, str(uuid.uuid4()))
def fail_dicom_eval(eval_id):
traceback.print_exc()
error_message = f'evaluation for study {eval_id} failed'
logger_service.log_error(error_message, traceback.format_exc())
# update eval status to FAILED
eval_db.fail_eval(eval_id)
messaging_service.send_notification(error_message, 'eval_failed')
def on_classifier_result(ch, method, properties, body):
print(f'received classifier result {body}')
message = json.loads(body)
orthanc_id = message['id']
result = message['output']
classifier_service.save_classification(orthanc_id, result)
messaging_service.send_notification(f'Study {orthanc_id} ready', 'new_result')
def fail_evals(model_id, eval_ids):
traceback.print_exc()
error_message = f'evaluation using model {model_id} failed'
logger_service.log_error(error_message, traceback.format_exc())
for eval_id in eval_ids:
eval_db.fail_eval(eval_id)
messaging_service.send_notification(error_message, 'eval_failed')
def fail_experiment(experiment):
"""
"""
messaging_service.send_notification(
f'Failed experiment {experiment["name"]}', 'experiment_failed')
experiment_db.set_experiment_failed(experiment['id'])
traceback.print_exc()
experiment_id = experiment['id']
logger_service.log_error(f'experiment {experiment_id} failed',
traceback.format_exc())
def on_eval_result(ch, method, properties, body):
print(f'received eval result {body}')
message = json.loads(body)
eval_id = message['id']
result = message['output']
type = message['type']
if type == 'FAIL':
eval_service.fail_dicom_eval(eval_id)
return
# write result to db
eval_service.write_eval_results(result, eval_id)
# send notification to frontend
messaging_service.send_notification(f'Finished evaluation {eval_id}', 'new_result')
def run_experiments(batch_size: int):
"""
Monitors db for active experiments and runs them
"""
print('running experiments')
# get experiments
experiments = experiment_service.get_running_experiments()
# run experiments
for experiment in experiments:
messaging_service.send_notification(f'Started experiment {experiment["name"]}',
'experiment_started')
# restart failed evaluations
eval_service.restart_failed_by_exp(experiment['id'])
# get experiment studies
studies = experiment_service.get_experiment_studies(experiment['id'])
# get model
model = model_service.get_model(experiment['modelId'])
batch = studies[:batch_size]
experiment_service.run_experiment(batch, dict(model), dict(experiment))
def start_study_evaluations(studies: List[object], model_id: int) -> List[int]:
"""
inserts entries into the study_evaluation table and sets them to 'RUNNING'
Args:
studies (List[object]): a list of the primary keys of study db entries to evaluate
model (int): the id of the model to use in evalution
Returns:
List[int]: a list of ids of the db entries that were inserted
"""
logger_service.log(f'starting study evaluations for {studies}')
for study in studies:
messaging_service.send_notification(
f"Started evaluation of study {study['orthancStudyId']}",
'eval_started')
# create string that contains the insert values for the studies
# kind of janky TBH
values = [
f'(\'{study["id"]}\', null, \'RUNNING\', {model_id})'
for study in studies
]
if len(studies) == 0:
return []
# join the insert arrays by , so that it can be used to insert multiple
reduced = ','.join(list(values))
# query and fetch all results
sql = f'''
INSERT INTO study_evaluation ("studyId", "modelOutput", status, "modelId")
VALUES {reduced}
RETURNING id;
'''
query_result = query_and_fetchall(sql)
return [evaluation['id'] for evaluation in query_result]
def finish_experiment(experiment):
"""
"""
experiment_db.set_experiment_complete(experiment['id'])
messaging_service.send_notification(
f'Completed experiment {experiment["name"]}', 'experiment_finished')
def fail_model(model_id):
traceback.print_exc()
error_message = f'evaluation using model {model_id} failed'
logger_service.log_error(error_message, traceback.format_exc())
messaging_service.send_notification(error_message, 'eval_failed')