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_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)
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)