def build(region_similarity_calculator_config):
"""Builds region similarity calculator based on the configuration.
Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See
core/region_similarity_calculator.proto for details.
Args:
region_similarity_calculator_config: RegionSimilarityCalculator
configuration proto.
Returns:
region_similarity_calculator: RegionSimilarityCalculator object.
Raises:
ValueError: On unknown region similarity calculator.
"""
if not isinstance(
region_similarity_calculator_config,
region_similarity_calculator_pb2.RegionSimilarityCalculator):
raise ValueError(
'region_similarity_calculator_config not of type '
'region_similarity_calculator_pb2.RegionsSimilarityCalculator')
similarity_calculator = region_similarity_calculator_config.WhichOneof(
'region_similarity')
if similarity_calculator == 'iou_similarity':
return region_similarity_calculator.IouSimilarity()
if similarity_calculator == 'ioa_similarity':
return region_similarity_calculator.IoaSimilarity()
if similarity_calculator == 'neg_sq_dist_similarity':
return region_similarity_calculator.NegSqDistSimilarity()
raise ValueError('Unknown region similarity calculator.')
def build(region_similarity_calculator_config):
"""Builds region similarity calculator based on the configuration.
Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See
core/region_similarity_calculator.proto for details.
Args:
region_similarity_calculator_config: RegionSimilarityCalculator
configuration proto.
Returns:
region_similarity_calculator: RegionSimilarityCalculator object.
Raises:
ValueError: On unknown region similarity calculator.
"""
if not isinstance(
region_similarity_calculator_config,
region_similarity_calculator_pb2.RegionSimilarityCalculator):
raise ValueError(
'region_similarity_calculator_config not of type '
'region_similarity_calculator_pb2.RegionsSimilarityCalculator')
similarity_calculator = region_similarity_calculator_config.WhichOneof(
'region_similarity')
if similarity_calculator == 'iou_similarity':
return region_similarity_calculator.IouSimilarity()
if similarity_calculator == 'ioa_similarity':
return region_similarity_calculator.IoaSimilarity()
if similarity_calculator == 'ioa_similarity_inversed':
return region_similarity_calculator.IoaSimilarityInversed()
if similarity_calculator == 'ioa_similarity_combined':
return region_similarity_calculator.IoaSimilarityCombined(
region_similarity_calculator_config.ioa_similarity_combined.
anchor_related_weight)
if similarity_calculator == 'neg_sq_dist_similarity':
return region_similarity_calculator.NegSqDistSimilarity()
if similarity_calculator == 'thresholded_iou_similarity':
return region_similarity_calculator.ThresholdedIouSimilarity(
region_similarity_calculator_config.thresholded_iou_similarity.
iou_threshold)
if similarity_calculator == 'ioa_weighted_neg_sq_dist_similarity':
return region_similarity_calculator.IoaWeightedNegSqDistSimilarity()
if similarity_calculator == 'ioa_weighted_neg_sq_center_dist_similarity':
return region_similarity_calculator.IoaWeightedNegSqCenterDistSimilarity(
)
if similarity_calculator == 'relative_distance_similarity':
return region_similarity_calculator.RelativeDistanceSimilarity(
region_similarity_calculator_config.relative_distance_similarity.
positive_threshold, region_similarity_calculator_config.
relative_distance_similarity.negative_threshold)
raise ValueError('Unknown region similarity calculator.')
def __init__(self,
similarity_calc,
matcher,
box_coder,
positive_class_weight=1.0,
negative_class_weight=1.0,
unmatched_cls_target=None):
"""Construct Multibox Target Assigner.
Args:
similarity_calc: a RegionSimilarityCalculator
matcher: an object_detection.core.Matcher used to match groundtruth to
anchors.
box_coder: an object_detection.core.BoxCoder used to encode matching
groundtruth boxes with respect to anchors.
positive_class_weight: classification weight to be associated to positive
anchors (default: 1.0)
negative_class_weight: classification weight to be associated to negative
anchors (default: 1.0)
unmatched_cls_target: a float32 tensor with shape [d_1, d_2, ..., d_k]
which is consistent with the classification target for each
anchor (and can be empty for scalar targets). This shape must thus be
compatible with the groundtruth labels that are passed to the "assign"
function (which have shape [num_gt_boxes, d_1, d_2, ..., d_k]).
If set to None, unmatched_cls_target is set to be [0] for each anchor.
Raises:
ValueError: if similarity_calc is not a RegionSimilarityCalculator or
if matcher is not a Matcher or if box_coder is not a BoxCoder
"""
if not isinstance(similarity_calc,
sim_calc.RegionSimilarityCalculator):
raise ValueError(
'similarity_calc must be a RegionSimilarityCalculator')
if not isinstance(matcher, mat.Matcher):
raise ValueError('matcher must be a Matcher')
if not isinstance(box_coder, bcoder.BoxCoder):
raise ValueError('box_coder must be a BoxCoder')
self._similarity_calc = similarity_calc
self._similarity_calc_ioa = sim_calc.IoaSimilarity()
self._matcher = matcher
self._box_coder = box_coder
self._positive_class_weight = positive_class_weight
self._negative_class_weight = negative_class_weight
if unmatched_cls_target is None:
self._unmatched_cls_target = tf.constant([0], tf.float32)
else:
self._unmatched_cls_target = unmatched_cls_target
self._unmatched_conf_target = tf.constant([0], tf.float32)
def graph_fn():
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)
ioa_similarity_calculator = region_similarity_calculator.IoaSimilarity(
)
ioa_similarity_1 = ioa_similarity_calculator.compare(
boxes1, boxes2)
ioa_similarity_2 = ioa_similarity_calculator.compare(
boxes2, boxes1)
return ioa_similarity_1, ioa_similarity_2
def test_get_correct_pairwise_similarity_based_on_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_similarity_calculator = region_similarity_calculator.IoaSimilarity(
)
ioa_similarity_1 = ioa_similarity_calculator.compare(boxes1, boxes2)
ioa_similarity_2 = ioa_similarity_calculator.compare(boxes2, boxes1)
with self.test_session() as sess:
iou_output_1, iou_output_2 = sess.run(
[ioa_similarity_1, ioa_similarity_2])
self.assertAllClose(iou_output_1, exp_output_1)
self.assertAllClose(iou_output_2, exp_output_2)
def test_assign_ignore(self):
similarity_calc = region_similarity_calculator.IoaSimilarity()
matcher = argmax_matcher.ArgMaxMatcher(0.5)
box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
target_assigner = targetassigner.TargetAssigner(
similarity_calc, matcher, box_coder, unmatched_cls_target=None)
prior_means = tf.constant([[0.5, 0.5, 1.0, 0.8], [0, 0.5, .5, 1.0],
[0.0, 0.0, 0.5, 0.5]])
prior_stddevs = tf.constant(3 * [4 * [.1]])
priors = box_list.BoxList(prior_means)
priors.add_field('stddev', prior_stddevs)
box_corners = [[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 0.9, 0.9]]
boxes = box_list.BoxList(tf.constant(box_corners))
exp_cls_targets = [[1], [0], [1]]
exp_cls_weights = [1, 1, 0]
exp_reg_targets = [[0, 0, -1, 1], [0, 0, 0, 0], [0, 0, 0, 0]]
exp_reg_weights = [1, 0, 0]
exp_matching_anchors = [0, 2]
# ignore
ignore = tf.constant([True, False], dtype=tf.bool)
boxes.add_field(fields.BoxListFields.ignore, ignore)
result = target_assigner.assign(priors, boxes)
(cls_targets, cls_weights, reg_targets, reg_weights, match) = result
with self.test_session() as sess:
cls_targets_out, cls_weights_out, reg_targets_out, reg_weights_out, matching_anchors_out = \
sess.run([cls_targets, cls_weights, reg_targets, reg_weights, match.matched_column_indices()])
self.assertAllClose(cls_targets_out, exp_cls_targets)
self.assertAllClose(cls_weights_out, exp_cls_weights)
self.assertAllClose(reg_targets_out, exp_reg_targets)
self.assertAllClose(reg_weights_out, exp_reg_weights)
self.assertAllClose(matching_anchors_out, exp_matching_anchors)
self.assertEquals(cls_targets_out.dtype, np.float32)
self.assertEquals(cls_weights_out.dtype, np.float32)
self.assertEquals(reg_targets_out.dtype, np.float32)
self.assertEquals(reg_weights_out.dtype, np.float32)
def build(region_similarity_calculator_config):
if not isinstance(
region_similarity_calculator_config,
region_similarity_calculator_pb2.RegionSimilarityCalculator):
raise ValueError(
'region_similarity_calculator_config not of type '
'region_similarity_calculator_pb2.RegionsSimilarityCalculator')
similarity_calculator = region_similarity_calculator_config.WhichOneof(
'region_similarity')
if similarity_calculator == 'iou_similarity':
return region_similarity_calculator.IouSimilarity()
if similarity_calculator == 'ioa_similarity':
return region_similarity_calculator.IoaSimilarity()
if similarity_calculator == 'neg_sq_dist_similarity':
return region_similarity_calculator.NegSqDistSimilarity()
if similarity_calculator == 'thresholded_iou_similarity':
return region_similarity_calculator.ThresholdedIouSimilarity(
region_similarity_calculator_config.thresholded_iou_similarity.
iou_threshold)
raise ValueError('Unknown region similarity calculator.')
