Python matcher.Matcher方法代码示例

# 需要导入模块: from object_detection.core import matcher [as 别名]
# 或者: from object_detection.core.matcher import Matcher [as 别名]
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._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 

# 需要导入模块: from object_detection.core import matcher [as 别名]
# 或者: from object_detection.core.matcher import Matcher [as 别名]
def __init__(self, use_matmul_gather=False):
    """Constructs a Matcher.

    Args:
      use_matmul_gather: Force constructed match objects to use matrix
        multiplication based gather instead of standard tf.gather.
        (Default: False).
    """
    super(GreedyBipartiteMatcher, self).__init__(
        use_matmul_gather=use_matmul_gather) 

# 需要导入模块: from object_detection.core import matcher [as 别名]
# 或者: from object_detection.core.matcher import Matcher [as 别名]
def __init__(self,
               similarity_calc,
               matcher,
               box_coder,
               negative_class_weight=1.0,
               weight_regression_loss_by_score=False):
    """Construct Object Detection 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.
      negative_class_weight: classification weight to be associated to negative
        anchors (default: 1.0). The weight must be in [0., 1.].
      weight_regression_loss_by_score: Whether to weight the regression loss by
        ground truth box score.

    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._matcher = matcher
    self._box_coder = box_coder
    self._negative_class_weight = negative_class_weight
    self._weight_regression_loss_by_score = weight_regression_loss_by_score 

# 需要导入模块: from object_detection.core import matcher [as 别名]
# 或者: from object_detection.core.matcher import Matcher [as 别名]
def __init__(self, similarity_calc, matcher, box_coder,
               negative_class_weight=1.0, unmatched_cls_target=None):
    """Construct Object Detection 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.
      negative_class_weight: classification weight to be associated to negative
        anchors (default: 1.0). The weight must be in [0., 1.].
      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._matcher = matcher
    self._box_coder = box_coder
    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 

# 需要导入模块: from object_detection.core import matcher [as 别名]
# 或者: from object_detection.core.matcher import Matcher [as 别名]
def __init__(self, similarity_calc, matcher, box_coder,
               positive_class_weight=1.0, negative_class_weight=1.0,
               unmatched_cls_target=None):
    """Construct Object Detection 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._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 

# 需要导入模块: from object_detection.core import matcher [as 别名]
# 或者: from object_detection.core.matcher import Matcher [as 别名]
def __init__(self, similarity_calc, matcher, box_coder,
                 negative_class_weight=1.0, unmatched_cls_target=None):
        """Construct Object Detection 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.
          negative_class_weight: classification weight to be associated to negative
            anchors (default: 1.0). The weight must be in [0., 1.].
          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._matcher = matcher
        self._box_coder = box_coder
        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