def _add_annotation_constraints(self, job_info): constraints_data = self.metadata.get('annotation_constraints') if constraints_data is not None: for data in constraints_data: name = _get_required(data, 'name', 'annotation constraint') self._logger.info( 'Adding annotation constraint "{}"...'.format(name)) description = data.get('description', '') text = yaml_utils.dump(data) creation_time = time_utils.get_utc_now() creating_user = None creating_job = Job.objects.get(id=job_info.job_id) AnnotationConstraint.objects.create( name=name, description=description, text=text, creation_time=creation_time, creating_user=creating_user, creating_job=creating_job)
def _add_annotation_constraints(self, job_info): constraints_data = self.archive_data.get('annotation_constraints') if constraints_data is not None: for data in constraints_data: name = _get_required(data, 'name', 'annotation constraint') self._logger.info( 'Adding annotation constraint "{}"...'.format(name)) description = data.get('description', '') text = yaml_utils.dump(data) creation_time = time_utils.get_utc_now() creating_user = None creating_job = Job.objects.get(id=job_info.job_id) AnnotationConstraint.objects.create( name=name, description=description, text=text, creation_time=creation_time, creating_user=creating_user, creating_job=creating_job)
def save_classifier(model, settings, stats): clip_type = settings.clip_type path = classifier_utils.get_model_file_path(clip_type) path.parent.mkdir(exist_ok=True) model.save(str(path)) settings = create_classifier_settings(settings, stats) text = yaml_utils.dump(settings, default_flow_style=False) path = classifier_utils.get_settings_file_path(clip_type) path.write_text(text)
def _save_settings(self, dir_path): # Find classification threshold and add it to settings. self.settings.classification_threshold = find_classification_threshold( self.val_stats, self.settings.min_classifier_recall) # Add TensorFlow model input name to settings. self.settings.model_input_name = self.model_input_name # Save settings. settings = self.settings.__dict__ text = yaml_utils.dump(settings, default_flow_style=False) file_path = dir_path / classifier_utils.SETTINGS_FILE_NAME file_path.write_text(text)
def save_training_settings(settings, training_name): file_path = annotator_utils.get_training_settings_file_path(training_name) text = yaml_utils.dump(settings.__dict__, default_flow_style=False) file_path.write_text(text)
def test_dump_and_load(self): x = {'x': 1, 'y': [1, 2, 3], 'z': {'one': 1}} s = yaml_utils.dump(x) y = yaml_utils.load(s) self.assertEqual(x, y)
def test_dump_and_load_with_non_default_flow_style(self): x = {'x': 1, 'y': [1, 2, 3], 'z': {'one': 1}} s = yaml_utils.dump(x, default_flow_style=False) y = yaml_utils.load(s) self.assertEqual(x, y)