229 lines
8.1 KiB
Python

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for ragged_util."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops.ragged import ragged_util
from tensorflow.python.platform import googletest
# Example 3d tensor for test cases. Has shape [4, 2, 3].
TENSOR_3D = [[[('%d%d%d' % (i, j, k)).encode('utf-8')
for k in range(3)]
for j in range(2)]
for i in range(4)]
# Example 4d tensor for test cases. Has shape [4, 2, 3, 5].
TENSOR_4D = [[[[('%d%d%d%d' % (i, j, k, l)).encode('utf-8')
for l in range(5)]
for k in range(3)]
for j in range(2)]
for i in range(4)]
@test_util.run_all_in_graph_and_eager_modes
class RaggedUtilTest(test_util.TensorFlowTestCase,
parameterized.TestCase):
@parameterized.parameters([
# Docstring examples
dict(
data=['a', 'b', 'c'],
repeats=[3, 0, 2],
axis=0,
expected=[b'a', b'a', b'a', b'c', b'c']),
dict(
data=[[1, 2], [3, 4]],
repeats=[2, 3],
axis=0,
expected=[[1, 2], [1, 2], [3, 4], [3, 4], [3, 4]]),
dict(
data=[[1, 2], [3, 4]],
repeats=[2, 3],
axis=1,
expected=[[1, 1, 2, 2, 2], [3, 3, 4, 4, 4]]),
# Scalar repeats value
dict(
data=['a', 'b', 'c'],
repeats=2,
axis=0,
expected=[b'a', b'a', b'b', b'b', b'c', b'c']),
dict(
data=[[1, 2], [3, 4]],
repeats=2,
axis=0,
expected=[[1, 2], [1, 2], [3, 4], [3, 4]]),
dict(
data=[[1, 2], [3, 4]],
repeats=2,
axis=1,
expected=[[1, 1, 2, 2], [3, 3, 4, 4]]),
# data & repeats are broadcast to have at least one dimension,
# so these are all equivalent:
dict(data=3, repeats=4, axis=0, expected=[3, 3, 3, 3]),
dict(data=[3], repeats=4, axis=0, expected=[3, 3, 3, 3]),
dict(data=3, repeats=[4], axis=0, expected=[3, 3, 3, 3]),
dict(data=[3], repeats=[4], axis=0, expected=[3, 3, 3, 3]),
# Empty tensor
dict(data=[], repeats=[], axis=0, expected=[]),
])
def testRepeat(self, data, repeats, expected, axis=None):
result = ragged_util.repeat(data, repeats, axis)
self.assertAllEqual(result, expected)
@parameterized.parameters([
dict(mode=mode, **args)
for mode in ['constant', 'dynamic', 'unknown_shape']
for args in [
# data & repeats are broadcast to have at least one dimension,
# so these are all equivalent:
dict(data=3, repeats=4, axis=0),
dict(data=[3], repeats=4, axis=0),
dict(data=3, repeats=[4], axis=0),
dict(data=[3], repeats=[4], axis=0),
# 1-dimensional data tensor.
dict(data=[], repeats=5, axis=0),
dict(data=[1, 2, 3], repeats=5, axis=0),
dict(data=[1, 2, 3], repeats=[3, 0, 2], axis=0),
dict(data=[1, 2, 3], repeats=[3, 0, 2], axis=-1),
dict(data=[b'a', b'b', b'c'], repeats=[3, 0, 2], axis=0),
# 2-dimensional data tensor.
dict(data=[[1, 2, 3], [4, 5, 6]], repeats=3, axis=0),
dict(data=[[1, 2, 3], [4, 5, 6]], repeats=3, axis=1),
dict(data=[[1, 2, 3], [4, 5, 6]], repeats=[3, 5], axis=0),
dict(data=[[1, 2, 3], [4, 5, 6]], repeats=[3, 5, 7], axis=1),
# 3-dimensional data tensor: shape=[4, 2, 3].
dict(data=TENSOR_3D, repeats=2, axis=0),
dict(data=TENSOR_3D, repeats=2, axis=1),
dict(data=TENSOR_3D, repeats=2, axis=2),
dict(data=TENSOR_3D, repeats=[2, 0, 4, 1], axis=0),
dict(data=TENSOR_3D, repeats=[3, 2], axis=1),
dict(data=TENSOR_3D, repeats=[1, 3, 1], axis=2),
# 4-dimensional data tensor: shape=[4, 2, 3, 5].
dict(data=TENSOR_4D, repeats=2, axis=0),
dict(data=TENSOR_4D, repeats=2, axis=1),
dict(data=TENSOR_4D, repeats=2, axis=2),
dict(data=TENSOR_4D, repeats=2, axis=3),
dict(data=TENSOR_4D, repeats=[2, 0, 4, 1], axis=0),
dict(data=TENSOR_4D, repeats=[3, 2], axis=1),
dict(data=TENSOR_4D, repeats=[1, 3, 1], axis=2),
dict(data=TENSOR_4D, repeats=[1, 3, 0, 0, 2], axis=3),
]
])
def testValuesMatchesNumpy(self, mode, data, repeats, axis):
# Exception: we can't handle negative axis if data.ndims is unknown.
if axis < 0 and mode == 'unknown_shape':
return
expected = np.repeat(data, repeats, axis)
if mode == 'constant':
data = constant_op.constant(data)
repeats = constant_op.constant(repeats)
elif mode == 'dynamic':
data = constant_op.constant(data)
repeats = constant_op.constant(repeats)
data = array_ops.placeholder_with_default(data, data.shape)
repeats = array_ops.placeholder_with_default(repeats, repeats.shape)
elif mode == 'unknown_shape':
data = array_ops.placeholder_with_default(data, None)
repeats = array_ops.placeholder_with_default(repeats, None)
result = ragged_util.repeat(data, repeats, axis)
self.assertAllEqual(result, expected)
@parameterized.parameters([
dict(
descr='axis >= rank(data)',
mode='dynamic',
data=[1, 2, 3],
repeats=[3, 0, 2],
axis=1,
error='axis=1 out of bounds: expected -1<=axis<1'),
dict(
descr='axis < -rank(data)',
mode='dynamic',
data=[1, 2, 3],
repeats=[3, 0, 2],
axis=-2,
error='axis=-2 out of bounds: expected -1<=axis<1'),
dict(
descr='len(repeats) != data.shape[axis]',
mode='dynamic',
data=[[1, 2, 3], [4, 5, 6]],
repeats=[2, 3],
axis=1,
error='Dimensions 3 and 2 are not compatible'),
dict(
descr='rank(repeats) > 1',
mode='dynamic',
data=[[1, 2, 3], [4, 5, 6]],
repeats=[[3], [5]],
axis=1,
error=r'Shape \(2, 1\) must have rank at most 1'),
dict(
descr='non-integer axis',
mode='constant',
data=[1, 2, 3],
repeats=2,
axis='foo',
exception=TypeError,
error='axis must be an int'),
])
def testError(self,
descr,
mode,
data,
repeats,
axis,
exception=ValueError,
error=None):
# Make sure that this is also an error case for numpy.
with self.assertRaises(exception):
np.repeat(data, repeats, axis)
if mode == 'constant':
data = constant_op.constant(data)
repeats = constant_op.constant(repeats)
elif mode == 'dynamic':
data = constant_op.constant(data)
repeats = constant_op.constant(repeats)
data = array_ops.placeholder_with_default(data, data.shape)
repeats = array_ops.placeholder_with_default(repeats, repeats.shape)
elif mode == 'unknown_shape':
data = array_ops.placeholder_with_default(data, None)
repeats = array_ops.placeholder_with_default(repeats, None)
with self.assertRaisesRegexp(exception, error):
ragged_util.repeat(data, repeats, axis)
if __name__ == '__main__':
googletest.main()