Python region_similarity_calculator.NegSqDistSimilarity方法代码示例

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=1.0)
    unmatched_class_label = tf.constant([[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]])
    priors = box_list.BoxList(prior_means)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [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], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(
          priors,
          boxes,
          groundtruth_labels,
          unmatched_class_label=unmatched_class_label) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
    prior_stddevs = tf.constant([[1.0, 1.0, 1.0, 1.0]])
    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],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))
    groundtruth_labels = tf.constant([[[0, 1], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder(stddev=1.0)
    unmatched_cls_target = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
    priors = box_list.BoxList(prior_means)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [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], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
def test_raises_error_on_invalid_groundtruth_labels(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([[0, 0], [0, 0], [0, 0]], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5]])
    prior_stddevs = tf.constant([[1.0, 1.0, 1.0, 1.0]])
    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],
                   [.75, 0, .95, .27]]
    boxes = box_list.BoxList(tf.constant(box_corners))

    groundtruth_labels = tf.constant([[[0, 1], [1, 0]]], tf.float32)

    with self.assertRaises(ValueError):
      target_assigner.assign(priors, boxes, groundtruth_labels,
                             num_valid_rows=3) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
def test_assign_multiclass_unequal_class_weights(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0, negative_class_weight=0.5,
        unmatched_cls_target=unmatched_cls_target)

    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]])
    prior_stddevs = tf.constant(4 * [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],
                   [.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)

    exp_cls_weights = [1, 1, .5, 1]
    result = target_assigner.assign(priors, boxes, groundtruth_labels,
                                    num_valid_rows=3)
    (_, cls_weights, _, _, _) = result
    with self.test_session() as sess:
      cls_weights_out = sess.run(cls_weights)
      self.assertAllClose(cls_weights_out, exp_cls_weights) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
def _get_agnostic_target_assigner(self):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=None) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
def _get_multi_class_target_assigner(self, num_classes):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant([1] + num_classes * [0], tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
def _get_multi_dimensional_target_assigner(self, target_dimensions):
    similarity_calc = region_similarity_calculator.NegSqDistSimilarity()
    matcher = bipartite_matcher.GreedyBipartiteMatcher()
    box_coder = mean_stddev_box_coder.MeanStddevBoxCoder()
    unmatched_cls_target = tf.constant(np.zeros(target_dimensions),
                                       tf.float32)
    return targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        positive_class_weight=1.0,
        negative_class_weight=1.0,
        unmatched_cls_target=unmatched_cls_target) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
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) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
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.') 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
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) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
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()
  if similarity_calculator == 'thresholded_iou_similarity':
    return region_similarity_calculator.ThresholdedIouSimilarity(
        region_similarity_calculator_config.thresholded_iou_similarity.threshold
    )

  raise ValueError('Unknown region similarity calculator.') 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
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_cls_target = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    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]])
    prior_stddevs = tf.constant(4 * [4 * [.1]])
    priors = box_list.BoxList(prior_means)
    priors.add_field('stddev', prior_stddevs)

    box_corners = [[0.0, 0.0, 0.5, 0.5],
                   [0.0, 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,
                             num_valid_rows=3) 

# 需要导入模块: from object_detection.core import region_similarity_calculator [as 别名]
# 或者: from object_detection.core.region_similarity_calculator import NegSqDistSimilarity [as 别名]
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_cls_target = tf.constant([1, 0, 0, 0, 0, 0, 0], tf.float32)
    target_assigner = targetassigner.TargetAssigner(
        similarity_calc, matcher, box_coder,
        unmatched_cls_target=unmatched_cls_target)

    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,
                             num_valid_rows=3)