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)
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)
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)
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)
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())
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())
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')
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')
def test_multiple_target_norm_values(self):
inputs = tf.constant([[[[3, 4], [7, 24]],
[[5, -12], [-1, 0]]]], tf.float32)
target_norm_value = [10.0, 20.0]
dim = 3
expected_output = [[[[30/5.0, 80/5.0], [70/25.0, 480/25.0]],
[[50/13.0, -240/13.0], [-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)
def test_multiple_target_norm_values(self):
inputs = tf.constant([[[[3, 4], [7, 24]],
[[5, -12], [-1, 0]]]], tf.float32)
target_norm_value = [10.0, 20.0]
dim = 3
expected_output = [[[[30/5.0, 80/5.0], [70/25.0, 480/25.0]],
[[50/13.0, -240/13.0], [-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)
