def testHardMiningWithSingleLossType(self):
location_losses = tf.constant([[100, 90, 80, 0], [0, 1, 2, 3]],
tf.float32)
cls_losses = tf.constant([[0, 10, 50, 110], [9, 6, 3, 0]], tf.float32)
box_corners = tf.constant([[0.1, 0.1, 0.9, 0.9], [0.1, 0.1, 0.9, 0.9],
[0.1, 0.1, 0.9, 0.9], [0.1, 0.1, 0.9, 0.9]],
tf.float32)
decoded_boxlist_list = []
decoded_boxlist_list.append(box_list.BoxList(box_corners))
decoded_boxlist_list.append(box_list.BoxList(box_corners))
# Uses only location loss to select hard examples
loss_op = losses.HardExampleMiner(num_hard_examples=1,
iou_threshold=0.0,
loss_type='loc',
cls_loss_weight=1,
loc_loss_weight=1)
(loc_loss, cls_loss) = loss_op(location_losses, cls_losses,
decoded_boxlist_list)
exp_loc_loss = 100 + 3
exp_cls_loss = 0 + 0
with self.test_session() as sess:
loc_loss_output = sess.run(loc_loss)
self.assertAllClose(loc_loss_output, exp_loc_loss)
cls_loss_output = sess.run(cls_loss)
self.assertAllClose(cls_loss_output, exp_cls_loss)
def test_raises_error_on_incompatible_groundtruth_boxes_and_labels(self):
similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
matcher = bipartite_matcher.GreedyBipartiteMatcher()
box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
unmatched_class_label = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
target_assigner = targetassigner.TargetAssigner(
similarity_calc, matcher, box_coder)
prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5],
[0.5, 0.5, 1.0, 0.8],
[0, 0.5, .5, 1.0],
[.75, 0, 1.0, .25]])
priors = box_list.BoxList(prior_means)
box_corners = [[0.0, 0.0, 0.5, 0.5],
[0.0, 0.0, 0.5, 0.8],
[0.5, 0.5, 0.9, 0.9],
[.75, 0, .95, .27]]
boxes = box_list.BoxList(tf.constant(box_corners))
groundtruth_labels = tf.constant([[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0]], tf.float32)
with self.assertRaisesRegexp(ValueError, 'Unequal shapes'):
target_assigner.assign(
priors,
boxes,
groundtruth_labels,
unmatched_class_label=unmatched_class_label,
num_valid_rows=3)
def test_prune_non_overlapping_boxes(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)
minoverlap = 0.5
exp_output_1 = boxes1
exp_output_2 = box_list.BoxList(tf.constant(0.0, shape=[0, 4]))
output_1, keep_indices_1 = box_list_ops.prune_non_overlapping_boxes(
boxes1, boxes2, min_overlap=minoverlap)
output_2, keep_indices_2 = box_list_ops.prune_non_overlapping_boxes(
boxes2, boxes1, min_overlap=minoverlap)
with self.test_session() as sess:
(output_1_, keep_indices_1_, output_2_, keep_indices_2_,
exp_output_1_, exp_output_2_) = sess.run([
output_1.get(), keep_indices_1,
output_2.get(), keep_indices_2,
exp_output_1.get(),
exp_output_2.get()
])
self.assertAllClose(output_1_, exp_output_1_)
self.assertAllClose(output_2_, exp_output_2_)
self.assertAllEqual(keep_indices_1_, [0, 1])
self.assertAllEqual(keep_indices_2_, [])
def test_create_target_assigner(self):
"""Tests that named constructor gives working target assigners.
TODO(rathodv): Make this test more general.
"""
corners = [[0.0, 0.0, 1.0, 1.0]]
groundtruth = box_list.BoxList(tf.constant(corners))
priors = box_list.BoxList(tf.constant(corners))
multibox_ta = (targetassigner
.create_target_assigner('Multibox', stage='proposal'))
multibox_ta.assign(priors, groundtruth)
# No tests on output, as that may vary arbitrarily as new target assigners
# are added. As long as it is constructed correctly and runs without errors,
# tests on the individual assigners cover correctness of the assignments.
anchors = box_list.BoxList(tf.constant(corners))
faster_rcnn_proposals_ta = (targetassigner
.create_target_assigner('FasterRCNN',
stage='proposal'))
faster_rcnn_proposals_ta.assign(anchors, groundtruth)
fast_rcnn_ta = (targetassigner
.create_target_assigner('FastRCNN'))
fast_rcnn_ta.assign(anchors, groundtruth)
faster_rcnn_detection_ta = (targetassigner
.create_target_assigner('FasterRCNN',
stage='detection'))
faster_rcnn_detection_ta.assign(anchors, groundtruth)
with self.assertRaises(ValueError):
targetassigner.create_target_assigner('InvalidDetector',
stage='invalid_stage')
def test_concatenate_with_missing_fields(self):
corners1 = tf.constant([[0, 0, 0, 0], [1, 2, 3, 4]], tf.float32)
scores1 = tf.constant([1.0, 2.1])
corners2 = tf.constant([[0, 3, 1, 6], [2, 4, 3, 8]], tf.float32)
boxlist1 = box_list.BoxList(corners1)
boxlist1.add_field('scores', scores1)
boxlist2 = box_list.BoxList(corners2)
with self.assertRaises(ValueError):
box_list_ops.concatenate([boxlist1, boxlist2])
def test_matched_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]])
exp_output = [2.0 / 16.0, 0]
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
iou = box_list_ops.matched_iou(boxes1, boxes2)
with self.test_session() as sess:
iou_output = sess.run(iou)
self.assertAllClose(iou_output, exp_output)
def test_intersection(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, 0.0, 6.0], [1.0, 0.0, 5.0]]
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
intersect = box_list_ops.intersection(boxes1, boxes2)
with self.test_session() as sess:
intersect_output = sess.run(intersect)
self.assertAllClose(intersect_output, exp_output)
def test_box_list_invalid_inputs(self):
data0 = tf.constant([[[0, 0, 1, 1], [3, 4, 5, 5]]], tf.float32)
data1 = tf.constant([[0, 0, 1], [1, 1, 2], [3, 4, 5]], tf.float32)
data2 = tf.constant([[0, 0, 1], [1, 1, 2], [3, 4, 5]], tf.int32)
with self.assertRaises(ValueError):
_ = box_list.BoxList(data0)
with self.assertRaises(ValueError):
_ = box_list.BoxList(data1)
with self.assertRaises(ValueError):
_ = box_list.BoxList(data2)
def test_very_small_Width_nan_after_encoding(self):
boxes = [[10.0, 10.0, 10.0000001, 20.0]]
anchors = [[15.0, 12.0, 30.0, 18.0]]
expected_rel_codes = [[-0.833333, 0., -21.128731, 0.510826]]
boxes = box_list.BoxList(tf.constant(boxes))
anchors = box_list.BoxList(tf.constant(anchors))
coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
def test_box_voting_fails_with_negative_scores(self):
candidates = box_list.BoxList(
tf.constant([[0.1, 0.1, 0.4, 0.4]], tf.float32))
pool = box_list.BoxList(tf.constant([[0.1, 0.1, 0.4, 0.4]],
tf.float32))
pool.add_field('scores', tf.constant([-0.2]))
averaged_boxes = box_list_ops.box_voting(candidates, pool)
with self.test_session() as sess:
with self.assertRaisesOpError('Scores must be non negative'):
sess.run([averaged_boxes.get()])
def test_pairwise_distances(self):
corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 2.0]])
corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0], [-4.0, 0.0, 0.0, 3.0],
[0.0, 0.0, 0.0, 0.0]])
exp_output = [[26, 25, 0], [18, 27, 6]]
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
dist_matrix = box_list_ops.sq_dist(boxes1, boxes2)
with self.test_session() as sess:
dist_output = sess.run(dist_matrix)
self.assertAllClose(dist_output, exp_output)
def test_gather_with_invalid_inputs(self):
corners = tf.constant(
[4 * [0.0], 4 * [1.0], 4 * [2.0], 4 * [3.0], 4 * [4.0]])
indices_float32 = tf.constant([0, 2, 4], tf.float32)
boxes = box_list.BoxList(corners)
with self.assertRaises(ValueError):
_ = box_list_ops.gather(boxes, indices_float32)
indices_2d = tf.constant([[0, 2, 4]], tf.int32)
boxes = box_list.BoxList(corners)
with self.assertRaises(ValueError):
_ = box_list_ops.gather(boxes, indices_2d)
def test_correct_relative_codes_with_small_width(self):
boxes = [[10.0, 10.0, 10.0000001, 20.0]]
anchors = [[15.0, 12.0, 30.0, 18.0]]
scale_factors = None
expected_rel_codes = [[-1.317616, 0., -20.670586]]
boxes = box_list.BoxList(tf.constant(boxes))
anchors = box_list.BoxList(tf.constant(anchors))
coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
(rel_codes_out, ) = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [[-0.5, -0.416666, -0.405465, -0.182321],
[-0.083333, -0.222222, -0.693147, -1.098612]]
boxes = box_list.BoxList(tf.constant(boxes))
anchors = box_list.BoxList(tf.constant(anchors))
coder = faster_rcnn_box_coder.FasterRcnnBoxCoder()
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2):
box_list1 = box_list.BoxList(groundtruth_boxlist1)
box_list2 = box_list.BoxList(groundtruth_boxlist2)
gt_box_batch = [box_list1, box_list2]
gt_class_targets = [None, None]
anchors_boxlist = box_list.BoxList(anchor_means)
agnostic_target_assigner = self._get_target_assigner()
(cls_targets, cls_weights, reg_targets, reg_weights,
_) = targetassigner.batch_assign_targets(
agnostic_target_assigner, anchors_boxlist, gt_box_batch,
gt_class_targets)
return (cls_targets, cls_weights, reg_targets, reg_weights)
def testGetCorrectRelativeCodesAfterEncoding(self):
box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]]
boxes = box_list.BoxList(tf.constant(box_corners))
expected_rel_codes = [[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]]
prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]])
priors = box_list.BoxList(prior_means)
coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
rel_codes = coder.encode(boxes, priors)
with self.test_session() as sess:
rel_codes_out = sess.run(rel_codes)
self.assertAllClose(rel_codes_out, expected_rel_codes)
def test_get_correct_pairwise_similarity_based_on_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_similarity_calculator = region_similarity_calculator.IouSimilarity()
iou_similarity = iou_similarity_calculator.compare(boxes1, boxes2)
with self.test_session() as sess:
iou_output = sess.run(iou_similarity)
self.assertAllClose(iou_output, exp_output)
def graph_fn(anchor_means, groundtruth_box_corners):
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
unmatched_threshold=0.3)
box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=0.1)
target_assigner = targetassigner.TargetAssigner(
similarity_calc, matcher, box_coder)
anchors_boxlist = box_list.BoxList(anchor_means)
groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
result = target_assigner.assign(
anchors_boxlist, groundtruth_boxlist, unmatched_class_label=None)
(cls_targets, cls_weights, reg_targets, reg_weights, _) = result
return (cls_targets, cls_weights, reg_targets, reg_weights)
def test_correct_relative_codes_with_non_default_scale(self):
boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4]
expected_rel_codes = [[-1.581139, -0.790569, -1.175573],
[-0.136083, -0.816497, -3.583519]]
boxes = box_list.BoxList(tf.constant(boxes))
anchors = box_list.BoxList(tf.constant(anchors))
coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
(rel_codes_out, ) = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
def test_box_voting_fails_when_unmatched(self):
candidates = box_list.BoxList(
tf.constant([[0.1, 0.1, 0.4, 0.4]], tf.float32))
pool = box_list.BoxList(tf.constant([[0.6, 0.6, 0.8, 0.8]],
tf.float32))
pool.add_field('scores', tf.constant([0.2]))
averaged_boxes = box_list_ops.box_voting(candidates, pool)
with self.test_session() as sess:
with self.assertRaisesOpError(
'Each box in selected_boxes must match '
'with at least one box in pool_boxes.'):
sess.run([averaged_boxes.get()])
def test_change_coordinate_frame(self):
corners = tf.constant([[0.25, 0.5, 0.75, 0.75], [0.5, 0.0, 1.0, 1.0]])
window = tf.constant([0.25, 0.25, 0.75, 0.75])
boxes = box_list.BoxList(corners)
expected_corners = tf.constant([[0, 0.5, 1.0, 1.0],
[0.5, -0.5, 1.5, 1.5]])
expected_boxes = box_list.BoxList(expected_corners)
output = box_list_ops.change_coordinate_frame(boxes, window)
with self.test_session() as sess:
output_, expected_boxes_ = sess.run(
[output.get(), expected_boxes.get()])
self.assertAllClose(output_, expected_boxes_)
def graph_fn(anchor_means, groundtruth_box_corners, gt_class_targets):
groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
gt_box_batch = [groundtruth_boxlist]
gt_class_targets_batch = [gt_class_targets]
anchors_boxlist = box_list.BoxList(anchor_means)
multiclass_target_assigner = self._get_target_assigner()
num_classes = 3
unmatched_class_label = tf.constant([1] + num_classes * [0], tf.float32)
(cls_targets, cls_weights, reg_targets, reg_weights,
_) = targetassigner.batch_assign_targets(
multiclass_target_assigner, anchors_boxlist,
gt_box_batch, gt_class_targets_batch, unmatched_class_label)
return (cls_targets, cls_weights, reg_targets, reg_weights)
def test_get_correct_pairwise_similarity_based_on_squared_distances(self):
corners1 = tf.constant([[0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 0.0, 2.0]])
corners2 = tf.constant([[3.0, 4.0, 1.0, 0.0],
[-4.0, 0.0, 0.0, 3.0],
[0.0, 0.0, 0.0, 0.0]])
exp_output = [[-26, -25, 0], [-18, -27, -6]]
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
dist_similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
dist_similarity = dist_similarity_calc.compare(boxes1, boxes2)
with self.test_session() as sess:
dist_output = sess.run(dist_similarity)
self.assertAllClose(dist_output, exp_output)
def test_correct_relative_codes_with_default_scale(self):
boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
scale_factors = None
expected_rel_codes = [[-0.790569, -0.263523, -0.293893],
[-0.068041, -0.272166, -0.89588]]
boxes = box_list.BoxList(tf.constant(boxes))
anchors = box_list.BoxList(tf.constant(anchors))
coder = square_box_coder.SquareBoxCoder(scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
(rel_codes_out, ) = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
def test_get_correct_relative_codes_after_encoding_with_scaling(self):
boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4, 5]
expected_rel_codes = [[-1., -1.25, -1.62186, -0.911608],
[-0.166667, -0.666667, -2.772588, -5.493062]]
boxes = box_list.BoxList(tf.constant(boxes))
anchors = box_list.BoxList(tf.constant(anchors))
coder = faster_rcnn_box_coder.FasterRcnnBoxCoder(
scale_factors=scale_factors)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
def test_ioa(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_1 = [[2.0 / 12.0, 0, 6.0 / 400.0],
[1.0 / 12.0, 0.0, 5.0 / 400.0]]
exp_output_2 = [[2.0 / 6.0, 1.0 / 5.0], [0, 0], [6.0 / 6.0, 5.0 / 5.0]]
boxes1 = box_list.BoxList(corners1)
boxes2 = box_list.BoxList(corners2)
ioa_1 = box_list_ops.ioa(boxes1, boxes2)
ioa_2 = box_list_ops.ioa(boxes2, boxes1)
with self.test_session() as sess:
ioa_output_1, ioa_output_2 = sess.run([ioa_1, ioa_2])
self.assertAllClose(ioa_output_1, exp_output_1)
self.assertAllClose(ioa_output_2, exp_output_2)
def test_copy_extra_fields(self):
corners = tf.constant([[0, 0, 1, 1], [0, 0.1, 1, 1.1]], tf.float32)
boxes = box_list.BoxList(corners)
tensor1 = np.array([[1], [4]])
tensor2 = np.array([[1, 1], [2, 2]])
boxes.add_field('tensor1', tf.constant(tensor1))
boxes.add_field('tensor2', tf.constant(tensor2))
new_boxes = box_list.BoxList(
tf.constant([[0, 0, 10, 10], [1, 3, 5, 5]], tf.float32))
new_boxes = box_list_ops._copy_extra_fields(new_boxes, boxes)
with self.test_session() as sess:
self.assertAllClose(tensor1,
sess.run(new_boxes.get_field('tensor1')))
self.assertAllClose(tensor2,
sess.run(new_boxes.get_field('tensor2')))
def graph_fn(anchor_means, groundtruth_boxlist1, groundtruth_boxlist2,
class_targets1, class_targets2):
box_list1 = box_list.BoxList(groundtruth_boxlist1)
box_list2 = box_list.BoxList(groundtruth_boxlist2)
gt_box_batch = [box_list1, box_list2]
gt_class_targets = [class_targets1, class_targets2]
anchors_boxlist = box_list.BoxList(anchor_means)
multiclass_target_assigner = self._get_target_assigner()
target_dimensions = (2, 3)
unmatched_class_label = tf.constant(np.zeros(target_dimensions),
tf.float32)
(cls_targets, cls_weights, reg_targets, reg_weights,
_) = targetassigner.batch_assign_targets(
multiclass_target_assigner, anchors_boxlist, gt_box_batch,
gt_class_targets, unmatched_class_label)
return (cls_targets, cls_weights, reg_targets, reg_weights)
def _decode(self, rel_codes, anchors):
"""Decodes relative codes to boxes.
Args:
rel_codes: a tensor representing N anchor-encoded boxes.
anchors: BoxList of anchors.
Returns:
boxes: BoxList holding N bounding boxes.
"""
ycenter_a, xcenter_a, ha, wa = anchors.get_center_coordinates_and_sizes(
)
la = tf.sqrt(ha * wa)
ty, tx, tl = tf.unstack(tf.transpose(rel_codes))
if self._scale_factors:
ty /= self._scale_factors[0]
tx /= self._scale_factors[1]
tl /= self._scale_factors[2]
l = tf.exp(tl) * la
ycenter = ty * la + ycenter_a
xcenter = tx * la + xcenter_a
ymin = ycenter - l / 2.
xmin = xcenter - l / 2.
ymax = ycenter + l / 2.
xmax = xcenter + l / 2.
return box_list.BoxList(
tf.transpose(tf.stack([ymin, xmin, ymax, xmax])))
def test_very_small_width_nan_after_encoding(self):
boxes = [[10., 10., 10.0000001, 20.]]
keypoints = [[[10., 10.], [10.0000001, 20.]]]
anchors = [[15., 12., 30., 18.]]
expected_rel_codes = [[
-0.833333, 0., -21.128731, 0.510826, -0.833333, -0.833333,
-0.833333, 0.833333
]]
boxes = box_list.BoxList(tf.constant(boxes))
boxes.add_field(fields.BoxListFields.keypoints, tf.constant(keypoints))
anchors = box_list.BoxList(tf.constant(anchors))
coder = keypoint_box_coder.KeypointBoxCoder(2)
rel_codes = coder.encode(boxes, anchors)
with self.test_session() as sess:
rel_codes_out, = sess.run([rel_codes])
self.assertAllClose(rel_codes_out, expected_rel_codes)
