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Python ops.normalize_to_target方法代码示例

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, 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') 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:11,代码来源:ops_test.py

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) 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:10,代码来源:ops_test.py

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) 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:9,代码来源:ops_test.py

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()) 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:10,代码来源:ops_test.py

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) 
开发者ID:ringringyi,项目名称:DOTA_models,代码行数:15,代码来源:ops_test.py


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