def test_evaluate_obj_det(self):
    if (self.data_dir is None):
        unittest.TestCase.skipTest(self, 'DLPY_DATA_DIR is not set in the environment variables')
    (caslib, path, tmp_caslib) = caslibify(self.s, path=(self.data_dir + 'evaluate_obj_det_det.sashdat'), task='load')
    self.s.table.loadtable(caslib=caslib, casout={'name': 'evaluate_obj_det_det', 'replace': True}, path=path)
    self.s.table.loadtable(caslib=caslib, casout={'name': 'evaluate_obj_det_gt', 'replace': True}, path='evaluate_obj_det_gt.sashdat')
    yolo_anchors = (5.9838598901098905, 3.4326923076923075, 2.184993862520458, 1.9841448445171848, 1.0261752136752136, 1.2277777777777779)
    yolo_model = Tiny_YoloV2(self.s, grid_number=17, scale=(1.0 / 255), n_classes=1, height=544, width=544, predictions_per_grid=3, anchors=yolo_anchors, max_boxes=100, coord_type='yolo', max_label_per_image=100, class_scale=1.0, coord_scale=2.0, prediction_not_a_object_scale=1, object_scale=5, detection_threshold=0.05, iou_threshold=0.2)
    metrics = yolo_model.evaluate_object_detection(ground_truth='evaluate_obj_det_gt', coord_type='yolo', detection_data='evaluate_obj_det_det', iou_thresholds=0.5)
    if ((caslib is not None) and tmp_caslib):
        self.s.retrieve('table.dropcaslib', message_level='error', caslib=caslib)
def test_evaluate_obj_det(self):
    if (self.data_dir is None):
        unittest.TestCase.skipTest(self, 'DLPY_DATA_DIR is not set in the environment variables')
    (caslib, path, tmp_caslib) = caslibify(self.s, path=(self.data_dir + 'evaluate_obj_det_det.sashdat'), task='load')
    self.s.table.loadtable(caslib=caslib, casout={'name': 'evaluate_obj_det_det', 'replace': True}, path=path)
    self.s.table.loadtable(caslib=caslib, casout={'name': 'evaluate_obj_det_gt', 'replace': True}, path='evaluate_obj_det_gt.sashdat')
    yolo_anchors = (5.9838598901098905, 3.4326923076923075, 2.184993862520458, 1.9841448445171848, 1.0261752136752136, 1.2277777777777779)
    yolo_model = Tiny_YoloV2(self.s, grid_number=17, scale=(1.0 / 255), n_classes=1, height=544, width=544, predictions_per_grid=3, anchors=yolo_anchors, max_boxes=100, coord_type='yolo', max_label_per_image=100, class_scale=1.0, coord_scale=2.0, prediction_not_a_object_scale=1, object_scale=5, detection_threshold=0.05, iou_threshold=0.2)
    metrics = yolo_model.evaluate_object_detection(ground_truth='evaluate_obj_det_gt', coord_type='yolo', detection_data='evaluate_obj_det_det', iou_thresholds=0.5)
    if ((caslib is not None) and tmp_caslib):
        self.s.retrieve('table.dropcaslib', message_level='error', caslib=caslib)