def main():
settings = load_json_file(sly.TaskPaths.TASK_CONFIG_PATH)
sly.logger.info('Input settings:', extra={'config': settings})
if IOU not in settings:
raise RuntimeError(
'"{}" field is missing. Please set Intersection over Union threshold'
.format(IOU))
if CONFIDENCE_TAG_NAME not in settings:
raise RuntimeError(
f'{CONFIDENCE_TAG_NAME!r} field is missing. Please set the tag name to read prediction confidence from.'
)
confidence_tag_name = settings[CONFIDENCE_TAG_NAME]
confidence_threshold = settings.get(CONFIDENCE_THRESHOLD, 0.0)
metric = MAPMetric(settings[CLASSES_MAPPING],
settings[IOU],
confidence_tag_name=confidence_tag_name,
confidence_threshold=confidence_threshold)
applier = sly.MetricProjectsApplier(metric, settings)
# Input sanity checks.
check_class_mapping(applier.project_gt, applier.project_pred,
settings[CLASSES_MAPPING])
if not applier.project_pred.meta.tag_metas.has_key(confidence_tag_name):
raise RuntimeError(
f'Tag {confidence_tag_name!r} cannot be found in the project with predictions '
f'{applier.project_pred.name!r} does not have that tag. Make sure you specify the correct '
f'confidence tag name as a {CONFIDENCE_TAG_NAME!r} setting in the plugin config.'
)
applier.run_evaluation()
metric.log_total_metrics()
def main():
settings = load_json_file(sly.TaskPaths.SETTINGS_PATH)
sly.logger.info('Input settings:', extra={'config': settings})
metric = IoUMetric(settings[CLASSES_MAPPING])
applier = sly.MetricProjectsApplier(metric, settings)
check_class_mapping(applier.project_gt, applier.project_pred,
settings[CLASSES_MAPPING])
applier.run_evaluation()
metric.log_total_metrics()
def main():
settings = load_json_file(sly.TaskPaths.SETTINGS_PATH)
sly.logger.info('Input settings:', extra={'config': settings})
if IOU not in settings:
raise RuntimeError(
'"{}" field is missing. Please set Intersection over Union threshold'
.format(IOU))
metric = PrecisionRecallMetric(settings[CLASSES_MAPPING], settings[IOU])
applier = sly.MetricProjectsApplier(metric, settings)
check_class_mapping(applier.project_gt, applier.project_pred,
settings[CLASSES_MAPPING])
applier.run_evaluation()
metric.log_total_metrics()