def __init__(self, optimal_model, ongoing_trials=None, remote=False):
self.optimal_model = optimal_model
self.ongoing_trials = ongoing_trials
self.remote = remote
self.num_available_devices = torch.cuda.device_count()
self.home_path = optimal_model.data['home_path']
self.dataset_name = optimal_model.data['dataset_name']
self.service_name = 'trainer' if self.ongoing_trials is None else 'trial'
self.package_name = 'zazuml'
if self.remote:
dataset_obj = get_dataset_obj(optimal_model.dataloop)
self.dataset_id = dataset_obj.id
with open('global_configs.json', 'r') as fp:
global_project_name = json.load(fp)['project']
self.project = dl.projects.get(project_name=global_project_name)
logger.info('service: ' + self.service_name)
self.service = self.project.services.get(service_name=self.service_name)
else:
self.local_trial_connector = LocalTrialConnector(self.service_name)
# TODO: dont convert here
if self.optimal_model.name == 'yolov3':
if self.optimal_model.data['annotation_type'] == 'coco':
self._convert_coco_to_yolo_format()
self.optimal_model.data['annotation_type'] = 'yolo'
def __init__(self, optimal_model, ongoing_trials=None, remote=False):
self.optimal_model = optimal_model
self.ongoing_trials = ongoing_trials
self.remote = remote
self.num_available_devices = torch.cuda.device_count()
self.home_path = optimal_model.data['home_path']
self.dataset_name = optimal_model.data['dataset_name']
self.package_name = 'zazuml'
if self.remote:
dataset_obj = get_dataset_obj(optimal_model.dataloop)
self.project = dl.projects.get(project_id=dataset_obj.projects[0])
self.dataset_id = dataset_obj.id
try:
self.train_query = optimal_model.dataloop['train_query']
except:
self.train_query = dl.Filters().prepare()['filter']
try:
# TODO: TRAIN QUERY IS STILL BEING COPPIED
try:
self.val_query = deepcopy(self.train_query)
except:
self.val_query = dl.Filters().prepare()
self.val_query['filter']['$and'][0][
'dir'] = optimal_model.dataloop['test_dir']
except:
try:
self.val_query = optimal_model.dataloop['val_query']
except:
self.val_query = dl.Filters().prepare()['filter']
with open('global_configs.json', 'r') as fp:
global_project_name = json.load(fp)['project']
self.global_project = dl.projects.get(
project_name=global_project_name)
# TODO: dont convert here
if self.optimal_model.name == 'yolov3':
if self.optimal_model.data['annotation_type'] == 'coco':
self._convert_coco_to_yolo_format()
self.optimal_model.data['annotation_type'] = 'yolo'