本文整理汇总了Python中object_detection.core.box_list_ops.refine_boxes_multi_class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.refine_boxes_multi_class方法的具体用法？Python box_list_ops.refine_boxes_multi_class怎么用？Python box_list_ops.refine_boxes_multi_class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.refine_boxes_multi_class方法的2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: test_refine_boxes_multi_class
# 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 refine_boxes_multi_class [as 别名]
def test_refine_boxes_multi_class(self):
pool = box_list.BoxList(
tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5],
[0.6, 0.6, 0.8, 0.8], [0.2, 0.2, 0.3, 0.3]], tf.float32))
pool.add_field('classes', tf.constant([0, 0, 1, 1]))
pool.add_field('scores', tf.constant([0.75, 0.25, 0.3, 0.2]))
refined_boxes = box_list_ops.refine_boxes_multi_class(pool, 3, 0.5, 10)
expected_boxes = [[0.1, 0.1, 0.425, 0.425], [0.6, 0.6, 0.8, 0.8],
[0.2, 0.2, 0.3, 0.3]]
expected_scores = [0.5, 0.3, 0.2]
with self.test_session() as sess:
boxes_out, scores_out, extra_field_out = sess.run(
[refined_boxes.get(), refined_boxes.get_field('scores'),
refined_boxes.get_field('classes')])
self.assertAllClose(expected_boxes, boxes_out)
self.assertAllClose(expected_scores, scores_out)
self.assertAllEqual(extra_field_out, [0, 1, 1]) 示例2: test_refine_boxes_multi_class
# 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 refine_boxes_multi_class [as 别名]
def test_refine_boxes_multi_class(self):
def graph_fn():
pool = box_list.BoxList(
tf.constant([[0.1, 0.1, 0.4, 0.4], [0.1, 0.1, 0.5, 0.5],
[0.6, 0.6, 0.8, 0.8], [0.2, 0.2, 0.3, 0.3]], tf.float32))
pool.add_field('classes', tf.constant([0, 0, 1, 1]))
pool.add_field('scores', tf.constant([0.75, 0.25, 0.3, 0.2]))
averaged_boxes = box_list_ops.refine_boxes_multi_class(pool, 3, 0.5, 10)
return (averaged_boxes.get(), averaged_boxes.get_field('scores'),
averaged_boxes.get_field('classes'))
boxes_out, scores_out, extra_field_out = self.execute_cpu(graph_fn, [])
expected_boxes = [[0.1, 0.1, 0.425, 0.425], [0.6, 0.6, 0.8, 0.8],
[0.2, 0.2, 0.3, 0.3]]
expected_scores = [0.5, 0.3, 0.2]
self.assertAllClose(expected_boxes, boxes_out)
self.assertAllClose(expected_scores, scores_out)
self.assertAllEqual(extra_field_out, [0, 1, 1])