Python box_coder.BoxCoder方法代码示例

# 需要导入模块: from object_detection.core import box_coder [as 别名]
# 或者: from object_detection.core.box_coder import BoxCoder [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 box_coder [as 别名]
# 或者: from object_detection.core.box_coder import BoxCoder [as 别名]
def get_box_coder(self):
    """Get BoxCoder of this TargetAssigner.

    Returns:
      BoxCoder: BoxCoder object.
    """
    return self._box_coder


# TODO: This method pulls in all the implementation dependencies into core.
# Therefore its best to have this factory method outside of core. 

# 需要导入模块: from object_detection.core import box_coder [as 别名]
# 或者: from object_detection.core.box_coder import BoxCoder [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 box_coder [as 别名]
# 或者: from object_detection.core.box_coder import BoxCoder [as 别名]
def get_box_coder(self):
    """Get BoxCoder of this TargetAssigner.

    Returns:
      BoxCoder object.
    """
    return self._box_coder


# TODO(rathodv): This method pulls in all the implementation dependencies into
# core. Therefore its best to have this factory method outside of core. 

# 需要导入模块: from object_detection.core import box_coder [as 别名]
# 或者: from object_detection.core.box_coder import BoxCoder [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 box_coder [as 别名]
# 或者: from object_detection.core.box_coder import BoxCoder [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 box_coder [as 别名]
# 或者: from object_detection.core.box_coder import BoxCoder [as 别名]
def get_box_coder(self):
    """Get BoxCoder of this TargetAssigner.

    Returns:
      BoxCoder: BoxCoder object.
    """
    return self._box_coder


# TODO: This method pulls in all the implementation dependencies into
# core. Therefore its best to have this factory method outside of core. 

# 需要导入模块: from object_detection.core import box_coder [as 别名]
# 或者: from object_detection.core.box_coder import BoxCoder [as 别名]
def get_box_coder(self):
    """Get BoxCoder of this TargetAssigner.

    Returns:
      BoxCoder object.
    """
    return self._box_coder


# TODO: This method pulls in all the implementation dependencies into
# core. Therefore its best to have this factory method outside of core.