range_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.range()`."""
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 dtypes
from tensorflow.python.framework import errors
from tensorflow.python.platform import test


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

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(output_type=[
              dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
          ])))
  def testStop(self, output_type):
    stop = 5
    dataset = dataset_ops.Dataset.range(stop, output_type=output_type)
    expected_output = np.arange(stop, dtype=output_type.as_numpy_dtype)
    self.assertDatasetProduces(dataset, expected_output=expected_output)
    self.assertEqual(output_type, dataset_ops.get_legacy_output_types(dataset))

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(output_type=[
              dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
          ])))
  def testStartStop(self, output_type):
    start, stop = 2, 5
    dataset = dataset_ops.Dataset.range(start, stop, output_type=output_type)
    expected_output = np.arange(start, stop, dtype=output_type.as_numpy_dtype)
    self.assertDatasetProduces(dataset, expected_output=expected_output)
    self.assertEqual(output_type, dataset_ops.get_legacy_output_types(dataset))

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(output_type=[
              dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
          ])))
  def testStartStopStep(self, output_type):
    start, stop, step = 2, 10, 2
    dataset = dataset_ops.Dataset.range(
        start, stop, step, output_type=output_type)
    expected_output = np.arange(
        start, stop, step, dtype=output_type.as_numpy_dtype)
    self.assertDatasetProduces(dataset, expected_output=expected_output)
    self.assertEqual(output_type, dataset_ops.get_legacy_output_types(dataset))

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(output_type=[
              dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
          ])))
  def testZeroStep(self, output_type):
    start, stop, step = 2, 10, 0
    with self.assertRaises(errors.InvalidArgumentError):
      dataset = dataset_ops.Dataset.range(
          start, stop, step, output_type=output_type)
      self.evaluate(dataset._variant_tensor)

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(output_type=[
              dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
          ])))
  def testNegativeStep(self, output_type):
    start, stop, step = 2, 10, -1
    dataset = dataset_ops.Dataset.range(
        start, stop, step, output_type=output_type)
    expected_output = np.arange(
        start, stop, step, dtype=output_type.as_numpy_dtype)
    self.assertDatasetProduces(dataset, expected_output=expected_output)
    self.assertEqual(output_type, dataset_ops.get_legacy_output_types(dataset))

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(output_type=[
              dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
          ])))
  def testStopLessThanStart(self, output_type):
    start, stop = 10, 2
    dataset = dataset_ops.Dataset.range(start, stop, output_type=output_type)
    expected_output = np.arange(start, stop, dtype=output_type.as_numpy_dtype)
    self.assertDatasetProduces(dataset, expected_output=expected_output)
    self.assertEqual(output_type, dataset_ops.get_legacy_output_types(dataset))

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(output_type=[
              dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
          ])))
  def testStopLessThanStartWithPositiveStep(self, output_type):
    start, stop, step = 10, 2, 2
    dataset = dataset_ops.Dataset.range(
        start, stop, step, output_type=output_type)
    expected_output = np.arange(
        start, stop, step, dtype=output_type.as_numpy_dtype)
    self.assertDatasetProduces(dataset, expected_output=expected_output)
    self.assertEqual(output_type, dataset_ops.get_legacy_output_types(dataset))

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(output_type=[
              dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
          ])))
  def testStopLessThanStartWithNegativeStep(self, output_type):
    start, stop, step = 10, 2, -1
    dataset = dataset_ops.Dataset.range(
        start, stop, step, output_type=output_type)
    expected_output = np.arange(
        start, stop, step, dtype=output_type.as_numpy_dtype)
    self.assertDatasetProduces(dataset, expected_output=expected_output)
    self.assertEqual(output_type, dataset_ops.get_legacy_output_types(dataset))

  @combinations.generate(test_base.default_test_combinations())
  def testName(self):
    dataset = dataset_ops.Dataset.range(5, name="range")
    self.assertDatasetProduces(dataset, list(range(5)))


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

  def _build_range_dataset(self, start, stop, options=None):
    dataset = dataset_ops.Dataset.range(start, stop)
    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(symbolic_checkpoint=[False, True])))
  def test(self, verify_fn, symbolic_checkpoint):
    start = 2
    stop = 10
    options = options_lib.Options()
    options.experimental_symbolic_checkpoint = symbolic_checkpoint
    verify_fn(self, lambda: self._build_range_dataset(start, stop, options),
              stop - start)


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

  @combinations.generate(
      combinations.times(test_base.default_test_combinations(),
                         combinations.combine(index=[-1, 2, 3])))
  def testInvalidIndex(self, index):
    dataset = dataset_ops.Dataset.range(2)
    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.range(0)
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=index))

  @combinations.generate(
      combinations.times(test_base.default_test_combinations()))
  def testBasic(self):
    dataset = dataset_ops.Dataset.range(10)
    for i in range(10):
      self.assertEqual(self.evaluate(random_access.at(dataset, index=i)), i)

  @combinations.generate(
      combinations.times(
          test_base.default_test_combinations(),
          combinations.combine(
              start=[-1, 0, 5],
              stop=[-5, 0, 10],
              step=[-3, 1, 5],
              output_type=[
                  dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64
              ])))
  def testMultipleCombinations(self, start, stop, step, output_type):
    dataset = dataset_ops.Dataset.range(
        start, stop, step, output_type=output_type)
    expected_output = np.arange(
        start, stop, step, dtype=output_type.as_numpy_dtype)
    len_dataset = self.evaluate(dataset.cardinality())
    for i in range(len_dataset):
      self.assertEqual(
          self.evaluate(random_access.at(dataset, index=i)), expected_output[i])
    with self.assertRaises(errors.OutOfRangeError):
      self.evaluate(random_access.at(dataset, index=len_dataset))


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