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)
def build(box_coder_config):
"""Builds a box coder object based on the box coder config.
Args:
box_coder_config: A box_coder.proto object containing the config for the
desired box coder.
Returns:
BoxCoder based on the config.
Raises:
ValueError: On empty box coder proto.
"""
if not isinstance(box_coder_config, box_coder_pb2.BoxCoder):
raise ValueError(
'box_coder_config not of type box_coder_pb2.BoxCoder.')
if box_coder_config.WhichOneof(
'box_coder_oneof') == 'faster_rcnn_box_coder':
return faster_rcnn_box_coder.FasterRcnnBoxCoder(scale_factors=[
box_coder_config.faster_rcnn_box_coder.y_scale,
box_coder_config.faster_rcnn_box_coder.x_scale,
box_coder_config.faster_rcnn_box_coder.height_scale,
box_coder_config.faster_rcnn_box_coder.width_scale
])
if box_coder_config.WhichOneof('box_coder_oneof') == 'keypoint_box_coder':
return keypoint_box_coder.KeypointBoxCoder(
box_coder_config.keypoint_box_coder.num_keypoints,
scale_factors=[
box_coder_config.keypoint_box_coder.y_scale,
box_coder_config.keypoint_box_coder.x_scale,
box_coder_config.keypoint_box_coder.height_scale,
box_coder_config.keypoint_box_coder.width_scale
])
if (box_coder_config.WhichOneof('box_coder_oneof') ==
'mean_stddev_box_coder'):
return mean_stddev_box_coder.MeanStddevBoxCoder(
stddev=box_coder_config.mean_stddev_box_coder.stddev)
if box_coder_config.WhichOneof('box_coder_oneof') == 'square_box_coder':
return square_box_coder.SquareBoxCoder(scale_factors=[
box_coder_config.square_box_coder.y_scale,
box_coder_config.square_box_coder.x_scale,
box_coder_config.square_box_coder.length_scale
])
raise ValueError('Empty box coder.')
def graph_fn(anchor_means, groundtruth_box_corners,
groundtruth_keypoints):
similarity_calc = region_similarity_calculator.IouSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(matched_threshold=0.5,
unmatched_threshold=0.5)
box_coder = keypoint_box_coder.KeypointBoxCoder(
num_keypoints=6, scale_factors=[10.0, 10.0, 5.0, 5.0])
target_assigner = targetassigner.TargetAssigner(
similarity_calc, matcher, box_coder)
anchors_boxlist = box_list.BoxList(anchor_means)
groundtruth_boxlist = box_list.BoxList(groundtruth_box_corners)
groundtruth_boxlist.add_field(fields.BoxListFields.keypoints,
groundtruth_keypoints)
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_get_correct_relative_codes_after_encoding(self):
boxes = [[10., 10., 20., 15.], [0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]], [[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.], [0.1, 0.0, 0.7, 0.9]]
expected_rel_codes = [[
-0.5, -0.416666, -0.405465, -0.182321, -0.5, -0.5, -0.833333, 0.
],
[
-0.083333, -0.222222, -0.693147, -1.098612,
0.166667, -0.166667, -0.333333, -0.055556
]]
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(num_keypoints)
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., 10., 20., 15.], [0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15., 12.], [10., 15.]], [[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15., 12., 30., 18.], [0.1, 0.0, 0.7, 0.9]]
scale_factors = [2, 3, 4, 5]
expected_rel_codes = [[
-1., -1.25, -1.62186, -0.911608, -1.0, -1.5, -1.666667, 0.
],
[
-0.166667, -0.666667, -2.772588, -5.493062,
0.333333, -0.5, -0.666667, -0.166667
]]
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(
num_keypoints, 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)