def box_iou(self, true_box, pred_box):
# based on the type of loss, compute the iou loss for a box
# compute_<name> indicated the type of iou to use
if self.iou_type == 'giou':
_, iou = box_ops.compute_giou(true_box, pred_box)
elif self.iou_type == 'ciou':
_, iou = box_ops.compute_ciou(true_box, pred_box)
else:
iou = box_ops.compute_iou(true_box, pred_box)
return iou
def box_loss(self, true_box, pred_box, darknet=False):
"""Call iou function and use it to compute the loss for the box maps."""
if self._loss_type == 'giou':
iou, liou = box_ops.compute_giou(true_box, pred_box)
elif self._loss_type == 'ciou':
iou, liou = box_ops.compute_ciou(true_box, pred_box, darknet=darknet)
else:
liou = iou = box_ops.compute_iou(true_box, pred_box)
loss_box = 1 - liou
return iou, liou, loss_box
def test_ious(self, num_boxes):
boxes = tf.convert_to_tensor(np.random.rand(num_boxes, 4))
expected_shape = np.array([
num_boxes,
])
expected_iou = np.ones([
num_boxes,
])
iou = box_ops.compute_iou(boxes, boxes)
_, giou = box_ops.compute_giou(boxes, boxes)
_, ciou = box_ops.compute_ciou(boxes, boxes)
_, diou = box_ops.compute_diou(boxes, boxes)
self.assertAllEqual(tf.shape(iou).numpy(), expected_shape)
self.assertArrayNear(iou, expected_iou, 0.001)
self.assertArrayNear(giou, expected_iou, 0.001)
self.assertArrayNear(ciou, expected_iou, 0.001)
self.assertArrayNear(diou, expected_iou, 0.001)
