def test_yolov2_tiny_2(self):
from dlpy.applications import Tiny_YoloV2
anchors=[]
anchors.append(1)
anchors.append(1)
anchors.append(1)
anchors.append(1)
model = Tiny_YoloV2(self.s, anchors, predictions_per_grid=2, max_label_per_image=3, max_boxes=1)
model.print_summary()
def test_yolov2_tiny(self):
from dlpy.applications import Tiny_YoloV2
anchors = []
anchors.append(1)
anchors.append(1)
anchors.append(1)
anchors.append(1)
model = Tiny_YoloV2(self.s, anchors, predictions_per_grid=2)
model.print_summary()
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 = 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)