zip_test.py

# Copyright 2017 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 `tf.data.Dataset.zip()`."""
import collections
from absl.testing import parameterized

import numpy as np

from tensorflow.python.data.experimental.ops import random_access
from tensorflow.python.data.kernel_tests import checkpoint_test_base
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.ops import options as options_lib
from tensorflow.python.framework import combinations
from tensorflow.python.framework import errors
from tensorflow.python.framework import tensor_shape
from tensorflow.python.platform import test

try:
  import attr  # pylint:disable=g-import-not-at-top
except ImportError:
  attr = None


def _dataset_factory(components):
  datasets = tuple([
      dataset_ops.Dataset.from_tensor_slices(component)
      for component in components
  ])
  return dataset_ops.Dataset.zip(datasets)


class ZipTest(test_base.DatasetTestBase, parameterized.TestCase):

  @combinations.generate(test_base.default_test_combinations())
  def testZipEqual(self):
    components = [
        np.tile(np.array([[1], [2], [3], [4]]), 20),
        np.tile(np.array([[12], [13], [14], [15]]), 22),
        np.array([37.0, 38.0, 39.0, 40.0])
    ]
    get_next = self.getNext(_dataset_factory(components))
    for i in range(4):
      results = self.evaluate(get_next())
      for component, result_component in zip(components, results):
        self.assertAllEqual(component[i], result_component)
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(get_next())
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(get_next())

  @combinations.generate(test_base.default_test_combinations())
  def testZipUnequal(self):
    components = [[1, 2, 3, 4], [1, 2, 3, 4, 5], [1.0, 2.0]]
    get_next = self.getNext(_dataset_factory(components))
    for i in range(2):
      results = self.evaluate(get_next())
      for component, result_component in zip(components, results):
        self.assertAllEqual(component[i], result_component)
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(get_next())
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(get_next())

  @combinations.generate(test_base.default_test_combinations())
  def testNested(self):

    components = [
        np.tile(np.array([[1], [2], [3], [4]]), 20),
        np.tile(np.array([[12], [13], [14], [15]]), 22),
        np.array([37.0, 38.0, 39.0, 40.0])
    ]
    datasets = [
        dataset_ops.Dataset.from_tensor_slices(component)
        for component in components
    ]
    dataset = dataset_ops.Dataset.zip((datasets[0], (datasets[1], datasets[2])))

    self.assertEqual(
        dataset_ops.get_legacy_output_shapes(dataset),
        (tensor_shape.TensorShape([20]),
         (tensor_shape.TensorShape([22]), tensor_shape.TensorShape([]))))

    get_next = self.getNext(dataset)
    for i in range(4):
      result1, (result2, result3) = self.evaluate(get_next())
      self.assertAllEqual(components[0][i], result1)
      self.assertAllEqual(components[1][i], result2)
      self.assertAllEqual(components[2][i], result3)
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(get_next())
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(get_next())

  @combinations.generate(test_base.default_test_combinations())
  def testNamedTuple(self):
    Foo = collections.namedtuple("Foo", ["x", "y"])
    x = Foo(x=dataset_ops.Dataset.range(3), y=dataset_ops.Dataset.range(3, 6))
    dataset = dataset_ops.Dataset.zip(x)
    expected = [Foo(x=0, y=3), Foo(x=1, y=4), Foo(x=2, y=5)]
    self.assertDatasetProduces(dataset, expected)

  @combinations.generate(test_base.default_test_combinations())
  def testAttrs(self):
    if attr is None:
      self.skipTest("attr module is not available.")

    @attr.s
    class Foo:
      x = attr.ib()
      y = attr.ib()

    x = Foo(x=dataset_ops.Dataset.range(3), y=dataset_ops.Dataset.range(3, 6))
    dataset = dataset_ops.Dataset.zip(x)
    expected = [Foo(x=0, y=3), Foo(x=1, y=4), Foo(x=2, y=5)]
    self.assertDatasetProduces(dataset, expected)

  @combinations.generate(test_base.default_test_combinations())
  def testName(self):
    x = dataset_ops.Dataset.from_tensors(4)
    y = dataset_ops.Dataset.from_tensors(2)
    dataset = dataset_ops.Dataset.zip((x, y), name="zip")
    self.assertDatasetProduces(dataset, [(4, 2)])

  @combinations.generate(
      combinations.times(test_base.default_test_combinations())
  )
  def testZipWithArgsAndDataset(self):
    with self.assertRaisesRegex(
        TypeError, r"Both `\*args` and `datasets` cannot be set."
    ):
      dataset_ops.Dataset.zip(
          dataset_ops.Dataset.range(1, 4),
          dataset_ops.Dataset.range(4, 7),
          datasets=(
              dataset_ops.Dataset.range(1, 4),
              dataset_ops.Dataset.range(4, 7),
          ),
      )

  @combinations.generate(
      combinations.times(test_base.default_test_combinations())
  )
  def testZipBasicWithNoInput(self):
    with self.assertRaisesRegex(
        TypeError, r"Must pass at least one dataset to `zip`."
    ):
      dataset_ops.Dataset.zip()

  @combinations.generate(
      combinations.times(test_base.default_test_combinations())
  )
  def InvalidZipInputList(self):
    with self.assertRaisesRegex(
        TypeError,
        r"Invalid input to `zip`. Inputs are expected to be (nested)"
        r" structures of `tf.data.Dataset` objects. Python `list` is"
        r" not supported and you should use `tuple` instead.",
    ):
      dataset_ops.Dataset.zip([1, 2, 3], [4, 5, 6])


class ZipCheckpointTest(
    checkpoint_test_base.CheckpointTestBase, parameterized.TestCase
):

  def _build_dataset(self, arr, options=None):
    components = [
        np.tile(np.array([[1], [2], [3], [4]]), 20),
        np.tile(np.array([[12], [13], [14], [15]]), 22),
        np.array(arr)
    ]
    datasets = [
        dataset_ops.Dataset.from_tensor_slices(component)
        for component in components
    ]
    dataset = dataset_ops.Dataset.zip((datasets[0], (datasets[1], datasets[2])))
    if options:
      dataset = dataset.with_options(options)
    return dataset

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          checkpoint_test_base.default_test_combinations(),
          combinations.combine(elements=[[37.0, 38.0, 39.0, 40.0], [1.0, 2.0]]),
          combinations.combine(symbolic_checkpoint=[False, True])))
  def test(self, verify_fn, elements, symbolic_checkpoint):
    options = options_lib.Options()
    options.experimental_symbolic_checkpoint = symbolic_checkpoint
    verify_fn(self, lambda: self._build_dataset(elements, options),
              len(elements))


class ZipRandomAccessTest(test_base.DatasetTestBase, parameterized.TestCase):

  @combinations.generate(
      combinations.times(test_base.default_test_combinations(),
                         combinations.combine(index=[-1, 3, 4])))
  def testInvalidIndex(self, index):
    dataset = dataset_ops.Dataset.zip(
        (dataset_ops.Dataset.range(1, 4), dataset_ops.Dataset.range(4, 7)))
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=index))

  @combinations.generate(
      combinations.times(test_base.default_test_combinations(),
                         combinations.combine(index=[-1, 0])))
  def testEmptyDataset(self, index):
    dataset = dataset_ops.Dataset.zip(
        datasets=(dataset_ops.Dataset.from_tensor_slices([]),
                  dataset_ops.Dataset.from_tensor_slices([])))
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=index))

  @combinations.generate(
      combinations.times(test_base.default_test_combinations()))
  def testZipBasic(self):
    dataset = dataset_ops.Dataset.zip(
        (dataset_ops.Dataset.range(1, 4), dataset_ops.Dataset.range(4, 7)))
    expected_dataset = [(1, 4), (2, 5), (3, 6)]
    for i in range(3):
      self.assertEqual(
          self.evaluate(random_access.at(dataset, index=i)),
          expected_dataset[i])

  @combinations.generate(
      combinations.times(test_base.default_test_combinations()))
  def testZipBasicWithoutTuple(self):
    dataset = dataset_ops.Dataset.zip(
        dataset_ops.Dataset.range(1, 4), dataset_ops.Dataset.range(4, 7)
    )
    expected_dataset = [(1, 4), (2, 5), (3, 6)]
    for i in range(3):
      self.assertEqual(
          self.evaluate(random_access.at(dataset, index=i)), expected_dataset[i]
      )

  @combinations.generate(
      combinations.times(test_base.default_test_combinations())
  )
  def testZipEqual(self):
    components = [
        np.tile(np.array([[1], [2], [3], [4]]), 20),
        np.tile(np.array([[12], [13], [14], [15]]), 22),
        np.array([37.0, 38.0, 39.0, 40.0])
    ]
    dataset = _dataset_factory(components)
    for i in range(4):
      results = self.evaluate(random_access.at(dataset, index=i))
      for component, result_component in zip(components, results):
        self.assertAllEqual(component[i], result_component)
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=4))

  @combinations.generate(test_base.default_test_combinations())
  def testZipUnequal(self):
    components = [[1, 2, 3, 4], [1, 2, 3, 4, 5], [1.0, 2.0]]
    dataset = _dataset_factory(components)
    for i in range(2):
      results = self.evaluate(random_access.at(dataset, index=i))
      for component, result_component in zip(components, results):
        self.assertAllEqual(component[i], result_component)
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=2))

  @combinations.generate(test_base.default_test_combinations())
  def testNested(self):
    components = [
        np.tile(np.array([[1], [2], [3], [4]]), 20),
        np.tile(np.array([[12], [13], [14], [15]]), 22),
        np.array([37.0, 38.0, 39.0, 40.0])
    ]
    datasets = [
        dataset_ops.Dataset.from_tensor_slices(component)
        for component in components
    ]
    dataset = dataset_ops.Dataset.zip((datasets[0], (datasets[1], datasets[2])))
    for i in range(4):
      result1, (result2,
                result3) = self.evaluate(random_access.at(dataset, index=i))
      self.assertAllEqual(components[0][i], result1)
      self.assertAllEqual(components[1][i], result2)
      self.assertAllEqual(components[2][i], result3)
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=4))

  @combinations.generate(test_base.default_test_combinations())
  def testNestedWithoutTuple(self):
    components = [
        np.tile(np.array([[1], [2], [3], [4]]), 20),
        np.tile(np.array([[12], [13], [14], [15]]), 22),
        np.array([37.0, 38.0, 39.0, 40.0]),
    ]
    datasets = [
        dataset_ops.Dataset.from_tensor_slices(component)
        for component in components
    ]
    dataset = dataset_ops.Dataset.zip(datasets[0], (datasets[1], datasets[2]))
    for i in range(4):
      result1, (result2, result3) = self.evaluate(
          random_access.at(dataset, index=i)
      )
      self.assertAllEqual(components[0][i], result1)
      self.assertAllEqual(components[1][i], result2)
      self.assertAllEqual(components[2][i], result3)
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=4))

  @combinations.generate(test_base.default_test_combinations())
  def testNamedTuple(self):
    Foo = collections.namedtuple("Foo", ["x", "y"])
    x = Foo(x=dataset_ops.Dataset.range(3), y=dataset_ops.Dataset.range(3, 6))
    dataset = dataset_ops.Dataset.zip(x)
    expected = [Foo(x=0, y=3), Foo(x=1, y=4), Foo(x=2, y=5)]
    for i in range(3):
      self.assertAllEqual(
          self.evaluate(random_access.at(dataset, index=i)), expected[i])
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=4))

  @combinations.generate(test_base.default_test_combinations())
  def testAttrs(self):
    if attr is None:
      self.skipTest("attr module is not available.")

    @attr.s
    class Foo:
      x = attr.ib()
      y = attr.ib()

    x = Foo(x=dataset_ops.Dataset.range(3), y=dataset_ops.Dataset.range(3, 6))
    dataset = dataset_ops.Dataset.zip(x)
    expected = [Foo(x=0, y=3), Foo(x=1, y=4), Foo(x=2, y=5)]
    for i in range(3):
      self.assertAllEqual(
          self.evaluate(random_access.at(dataset, index=i)), expected[i])
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=4))


if __name__ == "__main__":
  test.main()