本文整理汇总了Python中object_detection.utils.ops.normalize_to_target方法的典型用法代码示例。如果您正苦于以下问题：Python ops.normalize_to_target方法的具体用法？Python ops.normalize_to_target怎么用？Python ops.normalize_to_target使用的例子？那么恭喜您, 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.normalize_to_target方法的5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_create_normalize_to_target
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 别名]
def test_create_normalize_to_target(self):
inputs = tf.random_uniform([5, 10, 12, 3])
target_norm_value = 4.0
dim = 3
with self.test_session():
output = ops.normalize_to_target(inputs, target_norm_value, dim)
self.assertEqual(output.op.name, 'NormalizeToTarget/mul')
var_name = tf.contrib.framework.get_variables()[0].name
self.assertEqual(var_name, 'NormalizeToTarget/weights:0') 示例2: test_invalid_dim
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 别名]
def test_invalid_dim(self):
inputs = tf.random_uniform([5, 10, 12, 3])
target_norm_value = 4.0
dim = 10
with self.assertRaisesRegexp(
ValueError,
'dim must be non-negative but smaller than the input rank.'):
ops.normalize_to_target(inputs, target_norm_value, dim) 示例3: test_invalid_target_norm_values
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 别名]
def test_invalid_target_norm_values(self):
inputs = tf.random_uniform([5, 10, 12, 3])
target_norm_value = [4.0, 4.0]
dim = 3
with self.assertRaisesRegexp(
ValueError, 'target_norm_value must be a float or a list of floats'):
ops.normalize_to_target(inputs, target_norm_value, dim) 示例4: test_correct_output_shape
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 别名]
def test_correct_output_shape(self):
inputs = tf.random_uniform([5, 10, 12, 3])
target_norm_value = 4.0
dim = 3
with self.test_session():
output = ops.normalize_to_target(inputs, target_norm_value, dim)
self.assertEqual(output.get_shape().as_list(),
inputs.get_shape().as_list()) 示例5: test_correct_initial_output_values
# 需要导入模块: from object_detection.utils import ops [as 别名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 别名]
def test_correct_initial_output_values(self):
inputs = tf.constant([[[[3, 4], [7, 24]],
[[5, -12], [-1, 0]]]], tf.float32)
target_norm_value = 10.0
dim = 3
expected_output = [[[[30/5.0, 40/5.0], [70/25.0, 240/25.0]],
[[50/13.0, -120/13.0], [-10, 0]]]]
with self.test_session() as sess:
normalized_inputs = ops.normalize_to_target(inputs, target_norm_value,
dim)
sess.run(tf.global_variables_initializer())
output = normalized_inputs.eval()
self.assertAllClose(output, expected_output)