本文整理汇总了Python中object_detection.utils.ops.retain_groundtruth方法的典型用法代码示例。如果您正苦于以下问题：Python ops.retain_groundtruth方法的具体用法？Python ops.retain_groundtruth怎么用？Python ops.retain_groundtruth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.utils.ops的用法示例。
在下文中一共展示了ops.retain_groundtruth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_filter_with_missing_fields
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import retain_groundtruth [as 别名]
def test_filter_with_missing_fields(self):
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
input_classes = tf.placeholder(tf.int32, shape=(None,))
input_tensors = {
fields.InputDataFields.groundtruth_boxes: input_boxes,
fields.InputDataFields.groundtruth_classes: input_classes
}
valid_indices = tf.placeholder(tf.int32, shape=(None,))
feed_dict = {
input_boxes:
np.array([[0.2, 0.4, 0.1, 0.8], [0.2, 0.4, 1.0, 0.8]], dtype=np.float),
input_classes:
np.array([1, 2], dtype=np.int32),
valid_indices:
np.array([0], dtype=np.int32)
}
expected_tensors = {
fields.InputDataFields.groundtruth_boxes:
[[0.2, 0.4, 0.1, 0.8]],
fields.InputDataFields.groundtruth_classes:
[1]
}
output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
with self.test_session() as sess:
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
for key in [fields.InputDataFields.groundtruth_boxes]:
self.assertAllClose(expected_tensors[key], output_tensors[key])
for key in [fields.InputDataFields.groundtruth_classes]:
self.assertAllEqual(expected_tensors[key], output_tensors[key]) 示例2: test_filter_with_empty_groundtruth_boxes
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import retain_groundtruth [as 别名]
def test_filter_with_empty_groundtruth_boxes(self):
input_boxes = tf.placeholder(tf.float32, shape=(None, 4))
input_classes = tf.placeholder(tf.int32, shape=(None,))
input_is_crowd = tf.placeholder(tf.bool, shape=(None,))
input_area = tf.placeholder(tf.float32, shape=(None,))
input_difficult = tf.placeholder(tf.float32, shape=(None,))
valid_indices = tf.placeholder(tf.int32, shape=(None,))
input_tensors = {
fields.InputDataFields.groundtruth_boxes: input_boxes,
fields.InputDataFields.groundtruth_classes: input_classes,
fields.InputDataFields.groundtruth_is_crowd: input_is_crowd,
fields.InputDataFields.groundtruth_area: input_area,
fields.InputDataFields.groundtruth_difficult: input_difficult
}
output_tensors = ops.retain_groundtruth(input_tensors, valid_indices)
feed_dict = {
input_boxes:
np.array([], dtype=np.float).reshape(0, 4),
input_classes:
np.array([], dtype=np.int32),
input_is_crowd:
np.array([], dtype=np.bool),
input_area:
np.array([], dtype=np.float32),
input_difficult:
np.array([], dtype=np.float32),
valid_indices:
np.array([], dtype=np.int32)
}
with self.test_session() as sess:
output_tensors = sess.run(output_tensors, feed_dict=feed_dict)
for key in input_tensors:
if key == fields.InputDataFields.groundtruth_boxes:
self.assertAllEqual([0, 4], output_tensors[key].shape)
else:
self.assertAllEqual([0], output_tensors[key].shape)