def test_yolo_class_masking(setup_y_true_tensor, setup_true_box_tensor):
# 1. setup y_true placeholder
# 2. setup y_true feed value
y_true, y_true_value = setup_y_true_tensor
true_box_class, true_box_class_value = setup_true_box_tensor
# 3. create coord_mask operation
yolo_mask = _Mask(nb_class=4,
coord_scale=1.0,
class_scale=1.0,
object_scale=5.0,
no_object_scale=1.0)
class_mask_op = yolo_mask.create_class_mask(y_true, true_box_class)
# 4. run loss_op in session
class_mask_value = run_op(class_mask_op,
feed_dict={
y_true: y_true_value,
true_box_class: true_box_class_value
})
# coordinate mask value : (N, grid, grid, nb_box)
# object �� �ִ� (grid_x, grid_y, anchor_idx) ���� 1, �������� 0
expected_class_mask = np.zeros((1, 9, 9, 5))
expected_class_mask[0, 4, 3, 2] = 1.0
expected_class_mask[0, 4, 4, 2] = 1.0
assert np.allclose(class_mask_value, expected_class_mask)
def test_yolo_conf_masking(setup_y_true_tensor, setup_true_boxes_tensor, setup_y_pred_tensor):
y_true, y_true_value = setup_y_true_tensor
true_boxes, true_boxes_value = setup_true_boxes_tensor
y_pred, y_pred_value = setup_y_pred_tensor
yolo_mask = _Mask(nb_class=4, coord_scale=1.0, class_scale=1.0, object_scale=5.0, no_object_scale=1.0)
conf_mask_op = yolo_mask.create_conf_mask(y_true, y_pred, batch_size=1)
conf_mask_value = run_op(conf_mask_op, feed_dict={y_true:y_true_value,
true_boxes: true_boxes_value,
y_pred:y_pred_value})
expected_conf_mask_value = np.ones((1,9,9,5))
expected_conf_mask_value[0,4,3,2] = 5
expected_conf_mask_value[0,4,4,2] = 5
assert np.allclose(conf_mask_value, expected_conf_mask_value)
def test_yolo_conf_masking(setup_y_true_tensor, setup_true_boxes_tensor, setup_y_pred_tensor):
y_true, y_true_value = setup_y_true_tensor
true_boxes, true_boxes_value = setup_true_boxes_tensor
y_pred, y_pred_value = setup_y_pred_tensor
yolo_mask = _Mask(nb_class=4, coord_scale=1.0, class_scale=1.0, object_scale=5.0, no_object_scale=1.0)
conf_mask_op = yolo_mask.create_conf_mask(y_true, y_pred, batch_size=1)
conf_mask_value = run_op(conf_mask_op, feed_dict={y_true:y_true_value,
true_boxes: true_boxes_value,
y_pred:y_pred_value})
expected_conf_mask_value = np.ones((1,9,9,5))
expected_conf_mask_value[0,4,3,2] = 5
expected_conf_mask_value[0,4,4,2] = 5
assert np.allclose(conf_mask_value, expected_conf_mask_value)
def test_yolo_coord_masking(setup_y_true_tensor):
# 1. setup y_true placeholder
# 2. setup y_true feed value
y_true, y_true_value = setup_y_true_tensor
# 3. create coord_mask operation
yolo_mask = _Mask(nb_class=4, coord_scale=1.0, class_scale=1.0, object_scale=5.0, no_object_scale=1.0)
coord_mask_op = yolo_mask.create_coord_mask(y_true)
# 4. run loss_op in session
coord_mask_value = run_op(coord_mask_op, feed_dict={y_true: y_true_value})
# coordinate mask value : (N, grid, grid, nb_box, 1)
# object �� �ִ� (grid_x, grid_y, anchor_idx) ���� 1, �������� 0
expected_coord_mask = np.zeros((1,9,9,5,1))
expected_coord_mask[0, 4, 3, 2, :] = 1.0
expected_coord_mask[0, 4, 4, 2, :] = 1.0
assert np.allclose(coord_mask_value, expected_coord_mask)