本文整理汇总了Python中object_detection.core.box_list_ops.matched_iou方法的典型用法代码示例。如果您正苦于以下问题:Python box_list_ops.matched_iou方法的具体用法?Python box_list_ops.matched_iou怎么用?Python box_list_ops.matched_iou使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类object_detection.core.box_list_ops
的用法示例。
在下文中一共展示了box_list_ops.matched_iou方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _compute_loss
# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import matched_iou [as 别名]
def _compute_loss(self, prediction_tensor, target_tensor, weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors, 4]
representing the decoded predicted boxes
target_tensor: A float tensor of shape [batch_size, num_anchors, 4]
representing the decoded target boxes
weights: a float tensor of shape [batch_size, num_anchors]
Returns:
loss: a (scalar) tensor representing the value of the loss function
"""
predicted_boxes = box_list.BoxList(tf.reshape(prediction_tensor, [-1, 4]))
target_boxes = box_list.BoxList(tf.reshape(target_tensor, [-1, 4]))
per_anchor_iou_loss = 1.0 - box_list_ops.matched_iou(predicted_boxes,
target_boxes)
return tf.reduce_sum(tf.reshape(weights, [-1]) * per_anchor_iou_loss)
示例2: _compute_loss
# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import matched_iou [as 别名]
def _compute_loss(self, prediction_tensor, target_tensor, weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors, 4]
representing the decoded predicted boxes
target_tensor: A float tensor of shape [batch_size, num_anchors, 4]
representing the decoded target boxes
weights: a float tensor of shape [batch_size, num_anchors]
Returns:
loss: a float tensor of shape [batch_size, num_anchors] tensor
representing the value of the loss function.
"""
predicted_boxes = box_list.BoxList(tf.reshape(prediction_tensor, [-1, 4]))
target_boxes = box_list.BoxList(tf.reshape(target_tensor, [-1, 4]))
per_anchor_iou_loss = 1.0 - box_list_ops.matched_iou(predicted_boxes,
target_boxes)
return tf.reshape(weights, [-1]) * per_anchor_iou_loss
示例3: _compute_loss
# 需要导入模块: from object_detection.core import box_list_ops [as 别名]
# 或者: from object_detection.core.box_list_ops import matched_iou [as 别名]
def _compute_loss(self, prediction_tensor, target_tensor, weights):
"""Compute loss function.
Args:
prediction_tensor: A float tensor of shape [batch_size, num_anchors, 4]
representing the decoded predicted boxes
target_tensor: A float tensor of shape [batch_size, num_anchors, 4]
representing the decoded target boxes
weights: a float tensor of shape [batch_size, num_anchors]
Returns:
loss: a float tensor of shape [batch_size, num_anchors] tensor
representing the value of the loss function.
"""
predicted_boxes = box_list.BoxList(tf.reshape(prediction_tensor, [-1, 4]))
target_boxes = box_list.BoxList(tf.reshape(target_tensor, [-1, 4]))
per_anchor_iou_loss = 1.0 - box_list_ops.matched_iou(predicted_boxes,
target_boxes)
return tf.reshape(weights, [-1]) * per_anchor_iou_loss