def test_no_padding(self):
"""Test padding using string values."""
data = tf.constant([['1', '1', '1'], ['2', '2', '2']])
axis = 1
expected_result = data
padded_result = text.pad_along_dimension(
data=data, axis=axis, left_pad=None, right_pad=None)
self.assertAllEqual(expected_result, padded_result)
def test_float_partial_no_padding(self):
"""Test padding using float values."""
data = tf.constant([[1.0, 1.0, 1.0]])
axis = 1
right_pad_value = [3.5, 3.5, 3.5]
expected_result = tf.constant([[1.0, 1.0, 1.0, 3.5, 3.5, 3.5]])
padded_result = text.pad_along_dimension(
data=data, axis=axis, right_pad=right_pad_value)
self.assertAllEqual(expected_result, padded_result)
def test_no_right_padding(self):
"""Test that not specifying a right pad means no right padding."""
data = tf.constant([[1, 1, 1], [2, 2, 1], [3, 3, 1]])
axis = 1
left_pad_value = [0]
expected_result = tf.constant([[0, 1, 1, 1], [0, 2, 2, 1], [0, 3, 3, 1]])
padded_result = text.pad_along_dimension(
data=data, axis=axis, left_pad=left_pad_value)
self.assertAllEqual(expected_result, padded_result)
def test_invalid_axis(self):
data = tf.constant([[1, 1, 1], [2, 2, 1], [3, 3, 1]])
axis = -4
left_pad_value = [0, 0]
right_pad_value = [9, 9, 9]
error_msg = 'axis must be between -k <= axis <= -1 OR 0 <= axis < k'
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError, error_msg):
_ = text.pad_along_dimension(
data=data,
axis=axis,
left_pad=left_pad_value,
right_pad=right_pad_value)
error_msg = 'axis must be an int'
with self.assertRaisesRegexp(TypeError, error_msg):
_ = text.pad_along_dimension(
data=data,
axis=tf.constant(0),
left_pad=left_pad_value,
right_pad=right_pad_value)
def test_string_partial_no_padding(self):
"""Test padding using string values but without one padding value."""
data = tf.constant([['1', '1', '1'], ['2', '2', '2']])
axis = 1
left_pad_value = ['0', '0']
expected_result = tf.constant([['0', '0', '1', '1', '1'],
['0', '0', '2', '2', '2']])
padded_result = text.pad_along_dimension(
data=data, axis=axis, left_pad=left_pad_value)
self.assertAllEqual(expected_result, padded_result)
def test_pads_along_negative_outer_dimension(self):
"""Test padding along the outer dimension with a negative axis integer."""
data = tf.constant([[1, 1, 1], [2, 2, 1], [3, 3, 1]])
axis = -2
left_pad_value = [[0, 0, 0]]
right_pad_value = [[9, 9, 9]]
expected_result = tf.constant([[0, 0, 0], [1, 1, 1], [2, 2, 1], [3, 3, 1],
[9, 9, 9]])
padded_result = text.pad_along_dimension(
data=data,
axis=axis,
left_pad=left_pad_value,
right_pad=right_pad_value)
self.assertAllEqual(expected_result, padded_result)
def test_pads_along_positive_inner_dimension(self):
"""Test padding along the inner dimension with a positive axis integer."""
data = tf.constant([[1, 1, 1], [2, 2, 1], [3, 3, 1]])
axis = 1
left_pad_value = [0]
right_pad_value = [9]
expected_result = tf.constant([[0, 1, 1, 1, 9], [0, 2, 2, 1, 9],
[0, 3, 3, 1, 9]])
padded_result = text.pad_along_dimension(
data=data,
axis=axis,
left_pad=left_pad_value,
right_pad=right_pad_value)
self.assertAllEqual(expected_result, padded_result)
def testRaggedPadDimension(self,
descr,
data,
axis,
expected,
left_pad=None,
right_pad=None,
ragged_rank=None):
data = self._convert_ragged(data, ragged_rank)
positive_axis = axis if axis >= 0 else axis + data.shape.ndims
assert positive_axis >= 0
left_pad = self._convert_ragged(left_pad, data.ragged_rank - positive_axis)
right_pad = self._convert_ragged(right_pad,
data.ragged_rank - positive_axis)
padded = text.pad_along_dimension(data, axis, left_pad, right_pad)
self.assertRaggedEqual(padded, expected)
def test_padding_tensor_of_unknown_shape(self):
"""Test padding a tensor whose shape is not known at graph building time."""
data = tf.placeholder_with_default(
tf.constant([[1, 1, 1], [2, 2, 1], [3, 3, 1]]), shape=None)
axis = 1
left_pad_value = [0]
right_pad_value = [9]
expected_result = tf.constant([[0, 1, 1, 1, 9], [0, 2, 2, 1, 9],
[0, 3, 3, 1, 9]])
padded_result = text.pad_along_dimension(
data=data,
axis=axis,
left_pad=left_pad_value,
right_pad=right_pad_value)
self.assertAllEqual(expected_result, padded_result)