def test_iouworks_on_empty_inputs(self):
corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]])
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
boxes_empty = box_list.BoxList(tf.zeros((0, 4)))
iou_empty_1 = box_list_ops.iou(boxes1, boxes_empty)
iou_empty_2 = box_list_ops.iou(boxes_empty, boxes2)
iou_empty_3 = box_list_ops.iou(boxes_empty, boxes_empty)
with self.test_session() as sess:
iou_output_1, iou_output_2, iou_output_3 = sess.run(
[iou_empty_1, iou_empty_2, iou_empty_3])
self.assertAllEqual(iou_output_1.shape, (2, 0))
self.assertAllEqual(iou_output_2.shape, (0, 3))
self.assertAllEqual(iou_output_3.shape, (0, 0))
def get_box_loss(pred, gt, num_classes):
lamd_coord = 5.0
xmin = pred[:, 0] - pred[:, 2] / 2
xmax = pred[:, 0] + pred[:, 2] / 2
ymin = pred[:, 1] - pred[:, 3] / 2
ymax = pred[:, 1] + pred[:, 3] / 2
boxes1 = tf.transpose(tf.stack([ymin, xmin, ymax, xmax]))
xmin = gt[0] - gt[2] / 2
xmax = gt[0] + gt[2] / 2
ymin = gt[1] - gt[3] / 2
ymax = gt[1] + gt[3] / 2
boxes2 = tf.transpose(tf.stack([ymin, xmin, ymax, xmax]))
boxes2 = tf.expand_dims(boxes2, 0)
boxes_obj1 = box_list.BoxList(boxes1)
boxes_obj2 = box_list.BoxList(boxes2)
iou_score = box_list_ops.iou(boxes_obj2, boxes_obj1)
boxes_obj1.add_field('scores', iou_score[0])
boxes_obj1.add_field('classes', pred[:, 5:])
boxes_obj1.add_field('objness', pred[:, 4])
boxes_obj1 = box_list_ops.sort_by_field(boxes_obj1, 'scores')
matched_info = boxes_obj1.get_center_coordinates_and_sizes()[0]
pred_cen = matched_info[0:2]
pred_size = tf.sqrt(matched_info[2:4])
gt_center = tf.stack([gt[1], gt[0]])
gt_size = tf.stack([gt[3], gt[2]])
geo_loss = tf.losses.mean_squared_error(
gt_center, pred_cen) + tf.losses.mean_squared_error(
gt_size, pred_size)
geo_loss = lamd_coord * geo_loss
pred_confi = boxes_obj1.get_field('objness')[0] - 1
confi_loss = pred_confi * pred_confi
predi_class = boxes_obj1.get_field('classes')[0]
box_class = gt[4:]
class_loss = tf.losses.mean_squared_error(box_class, predi_class)
return confi_loss + geo_loss + class_loss
def test_iou(self):
corners1 = tf.constant([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]])
corners2 = tf.constant([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0],
[0.0, 0.0, 20.0, 20.0]])
exp_output = [[2.0 / 16.0, 0, 6.0 / 400.0], [1.0 / 16.0, 0.0, 5.0 / 400.0]]
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
iou = box_list_ops.iou(boxes1, boxes2)
with self.test_session() as sess:
iou_output = sess.run(iou)
self.assertAllClose(iou_output, exp_output)
def _compare(self, boxlist1, boxlist2):
"""Compute pairwise IOU similarity between the two BoxLists.
Args:
boxlist1: BoxList holding N boxes.
boxlist2: BoxList holding M boxes.
Returns:
A tensor with shape [N, M] representing pairwise iou scores.
"""
return box_list_ops.iou(boxlist1, boxlist2)
def _compare(self, boxlist1, boxlist2):
"""Compute pairwise IOU similarity between the two BoxLists and score.
Args:
boxlist1: BoxList holding N boxes. Must have a score field.
boxlist2: BoxList holding M boxes.
Returns:
A tensor with shape [N, M] representing scores threholded by pairwise
iou scores.
"""
ious = box_list_ops.iou(boxlist1, boxlist2)
scores = boxlist1.get_field(fields.BoxListFields.scores)
scores = tf.expand_dims(scores, axis=1)
row_replicated_scores = tf.tile(scores, [1, tf.shape(ious)[-1]])
thresholded_ious = tf.where(ious > self._iou_threshold,
row_replicated_scores, tf.zeros_like(ious))
return thresholded_ious
def _compare(self, boxlist1, boxlist2):
"""Compute pairwise IOU similarity between the two BoxLists ro RBoxlists.
Args:
boxlist1: BoxList or RBoxlist holding N boxes.
boxlist2: BoxList or RBoxlist holding M boxes.
Returns:
A tensor with shape [N, M] representing pairwise iou scores.
"""
if isinstance(boxlist1, rbox_list.RBoxList) and isinstance(
boxlist2, rbox_list.RBoxList):
return rbox_list_ops.iou(boxlist1, boxlist2)
elif isinstance(boxlist1, box_list.BoxList) and isinstance(
boxlist2, box_list.BoxList):
return box_list_ops.iou(boxlist1, boxlist2)
else:
raise ValueError(
'boxlist type must be same and BoxList or RBoxsList.')
