def _get_serialized_data(self, annotation):
if get_bool_env('FUTURE_SAVE_TASK_TO_STORAGE', default=False):
# export task with annotations
return ExportDataSerializer(annotation.task).data
else:
from io_storages.serializers import StorageAnnotationSerializer
# deprecated functionality - save only annotation
return StorageAnnotationSerializer(annotation).data
def _get_serialized_data(self, annotation):
if get_bool_env('FUTURE_SAVE_TASK_TO_STORAGE', default=False):
# export task with annotations
return ExportDataSerializer(annotation.task).data
else:
serializer_class = load_func(settings.STORAGE_ANNOTATION_SERIALIZER)
# deprecated functionality - save only annotation
return serializer_class(annotation).data
def train(self, project, use_ground_truth=False):
# get only tasks with annotations
tasks = project.tasks.annotate(
num_annotations=Count('annotations')).filter(num_annotations__gt=0)
# create serialized tasks with annotations: {"data": {...}, "annotations": [{...}], "predictions": [{...}]}
tasks_ser = ExportDataSerializer(tasks, many=True).data
logger.debug(
f'{len(tasks_ser)} tasks with annotations are sent to ML backend for training.'
)
request = {
'annotations': tasks_ser,
'project': self._create_project_uid(project),
'label_config': project.label_config,
'params': {
'login': project.task_data_login,
'password': project.task_data_password
}
}
return self._request('train', request, verbose=False)
def train(self, project, use_ground_truth=False):
# TODO Replace AnonymousUser with real user from request
user = AnonymousUser()
# Identify if feature flag is turned on
if flag_set(
'ff_back_dev_1417_start_training_mlbackend_webhooks_250122_long',
user):
request = {
'action':
'PROJECT_UPDATED',
'project':
load_func(settings.WEBHOOK_SERIALIZERS['project'])(
instance=project).data
}
return self._request('webhook',
request,
verbose=False,
timeout=TIMEOUT_PREDICT)
else:
# get only tasks with annotations
tasks = project.tasks.annotate(
num_annotations=Count('annotations')).filter(
num_annotations__gt=0)
# create serialized tasks with annotations: {"data": {...}, "annotations": [{...}], "predictions": [{...}]}
tasks_ser = ExportDataSerializer(tasks, many=True).data
logger.debug(
f'{len(tasks_ser)} tasks with annotations are sent to ML backend for training.'
)
request = {
'annotations': tasks_ser,
'project': self._create_project_uid(project),
'label_config': project.label_config,
'params': {
'login': project.task_data_login,
'password': project.task_data_password
}
}
return self._request('train',
request,
verbose=False,
timeout=TIMEOUT_PREDICT)