def reload(self):
self.tasks = None
self.derived_input_schema = []
self.derived_output_schema = {
'from_name_to_name_type': set(),
'labels': defaultdict(set)
}
self._init()
self.label_config_full = config_comments_free(
open(self.config['label_config']).read())
self.label_config_line = config_line_stripped(self.label_config_full)
if self.analytics is None:
self.analytics = Analytics(
self.label_config_line,
self.config.get('collect_analytics', True), self.name)
else:
self.analytics.update_info(
self.label_config_line,
self.config.get('collect_analytics', True), self.name)
# configure project
self.project_obj = ProjectObj(label_config=self.label_config_line,
label_config_full=self.label_config_full)
# configure machine learning backend
if self.ml_backend is None:
ml_backend_params = self.config.get('ml_backend')
if ml_backend_params:
ml_backend = MLBackend.from_params(ml_backend_params)
self.project_obj.connect(ml_backend)
self.converter = Converter(self.label_config_full)
def load_label_config(self):
with open(self.label_config_path, encoding='utf8') as f:
self.label_config_full = config_comments_free(f.read())
self.label_config_line = config_line_stripped(self.label_config_full)
self.parsed_label_config = parse_config(self.label_config_line)
self.config_input_tags = self.get_config_input_tags(
self.label_config_line)
def reload_config(prompt_inputs=False, force=False):
global c
global label_config_line
global analytics
global ml_backend
global project
global config_path
# Read config from config.json & input arguments
c = json.load(open(config_path))
c['port'] = input_args.port if input_args.port else c['port']
c['label_config'] = input_args.label_config if input_args.label_config else c[
'label_config']
c['input_path'] = input_args.input_path if input_args.input_path else c[
'input_path']
c['output_dir'] = input_args.output_dir if input_args.output_dir else c[
'output_dir']
c['debug'] = input_args.debug if input_args.debug is not None else c[
'debug']
# If specified, prompt user in console about specific inputs
if prompt_inputs:
iprompt = LabelStudioConfigPrompt(c)
c['input_data'] = iprompt.ask_input_path()
c['output_dir'] = iprompt.ask_output_dir()
c['label_config'] = iprompt.ask_label_config()
# Initialize DBs
db.re_init(c)
label_config_full = config_comments_free(open(c['label_config']).read())
label_config_line = config_line_stripped(label_config_full)
if analytics is None:
analytics = Analytics(label_config_line,
c.get('collect_analytics', True))
else:
analytics.update_info(label_config_line,
c.get('collect_analytics', True))
# configure project
if project is None or force:
project = Project(label_config=label_config_line,
label_config_full=label_config_full)
# configure machine learning backend
if ml_backend is None or force:
ml_backend_params = c.get('ml_backend')
if ml_backend_params:
ml_backend = MLBackend.from_params(ml_backend_params)
project.connect(ml_backend)
return True
def load_label_config(self):
self.label_config_full = config_comments_free(
open(self.config["label_config"], encoding="utf8").read())
self.label_config_line = config_line_stripped(self.label_config_full)
self.parsed_label_config = parse_config(self.label_config_line)
self.input_data_tags = self.get_input_data_tags(self.label_config_line)
def load_label_config(self):
self.label_config_full = config_comments_free(
open(self.config['label_config']).read())
self.label_config_line = config_line_stripped(self.label_config_full)
self.input_data_tags = self.get_input_data_tags(self.label_config_line)
