本文整理匯總了Python中object_detection.utils.ops.normalize_to_target方法的典型用法代碼示例。如果您正苦於以下問題：Python ops.normalize_to_target方法的具體用法？Python ops.normalize_to_target怎麽用？Python ops.normalize_to_target使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類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個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_create_normalize_to_target
# 需要導入模塊: from object_detection.utils import ops [as 別名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 別名]
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
# 需要導入模塊: from object_detection.utils import ops [as 別名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 別名]
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
# 需要導入模塊: from object_detection.utils import ops [as 別名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 別名]
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
# 需要導入模塊: from object_detection.utils import ops [as 別名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 別名]
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
# 需要導入模塊: from object_detection.utils import ops [as 別名]
# 或者: from object_detection.utils.ops import normalize_to_target [as 別名]
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)