code: # 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 the experimental input pipeline ops."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import threading
  
  import numpy as np
  
  from tensorflow.contrib.data.python.ops import batching
  from tensorflow.contrib.data.python.ops import dataset_ops
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.platform import test
  
  
  class DatasetConstructorTest(test.TestCase):
  
    def testTensorDataset(self):
      """Test an dataset that represents a single tuple of tensors."""
      components = (np.array(1), np.array([1, 2, 3]), np.array(37.0))
  
      iterator = (dataset_ops.Dataset.from_tensors(components)
                  .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = iterator.get_next()
  
      self.assertEqual([c.shape for c in components],
                       [t.shape for t in get_next])
  
      with self.test_session() as sess:
        sess.run(init_op)
        results = sess.run(get_next)
        for component, result_component in zip(components, results):
          self.assertAllEqual(component, result_component)
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testTensorSliceDataset(self):
      """Test an dataset that represents the slices from a tuple of tensors."""
      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])
      )
  
      iterator = (dataset_ops.Dataset.from_tensor_slices(components)
                  .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = iterator.get_next()
  
      self.assertEqual([c.shape[1:] for c in components],
                       [t.shape for t in get_next])
  
      with self.test_session() as sess:
        sess.run(init_op)
        for i in range(4):
          results = sess.run(get_next)
          for component, result_component in zip(components, results):
            self.assertAllEqual(component[i], result_component)
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testTensorSliceDatasetWithDict(self):
      components = {"foo": [1, 2, 3], "bar": [[4.0], [5.0], [6.0]]}
      iterator = (dataset_ops.Dataset.from_tensor_slices(components)
                  .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = iterator.get_next()
  
      self.assertEqual(dtypes.int32, iterator.output_types["foo"])
      self.assertEqual(dtypes.float32, iterator.output_types["bar"])
      self.assertEqual((), iterator.output_shapes["foo"])
      self.assertEqual((1,), iterator.output_shapes["bar"])
  
      with self.test_session() as sess:
        sess.run(init_op)
        for i in range(3):
          results = sess.run(get_next)
          self.assertEqual(components["foo"][i], results["foo"])
          self.assertEqual(components["bar"][i], results["bar"])
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testSparseTensorSliceDataset(self):
      """Test a dataset based on slices of a `tf.SparseTensor`."""
      st = array_ops.sparse_placeholder(dtypes.float64)
      iterator = (dataset_ops.Dataset.from_sparse_tensor_slices(st)
                  .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = sparse_tensor.SparseTensor(*iterator.get_next())
  
      with self.test_session() as sess:
        slices = [[1., 2., 3.], [1.], [1.], [1., 2.], [], [1., 2.], [], [], []]
  
        # Test with sparse tensor in the appropriate order.
        indices = np.array(
            [[i, j] for i in range(len(slices)) for j in range(len(slices[i]))])
        values = np.array([val for s in slices for val in s])
        dense_shape = np.array([len(slices), max(len(s) for s in slices) + 1])
        sparse_feed = sparse_tensor.SparseTensorValue(indices, values,
                                                      dense_shape)
        sess.run(init_op, feed_dict={st: sparse_feed})
        for i, s in enumerate(slices):
          results = sess.run(get_next)
          self.assertAllEqual(s, results.values)
          expected_indices = np.array(
              [[j] for j in range(len(slices[i]))]).reshape([-1, 1])
          self.assertAllEqual(expected_indices, results.indices)
          self.assertAllEqual(dense_shape[1:], results.dense_shape)
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
        # Test with sparse tensor in the reverse order, which is not
        # currently supported.
        reverse_order_indices = indices[::-1, :]
        reverse_order_values = values[::-1]
        sparse_feed = sparse_tensor.SparseTensorValue(
            reverse_order_indices, reverse_order_values, dense_shape)
        with self.assertRaises(errors.UnimplementedError):
          sess.run(init_op, feed_dict={st: sparse_feed})
  
        # Test with an empty sparse tensor.
        empty_indices = np.empty((0, 4), dtype=np.int64)
        empty_values = np.empty((0,), dtype=np.float64)
        empty_dense_shape = [0, 4, 37, 9]
        sparse_feed = sparse_tensor.SparseTensorValue(empty_indices, empty_values,
                                                      empty_dense_shape)
        sess.run(init_op, feed_dict={st: sparse_feed})
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    # pylint: disable=g-long-lambda,unnecessary-lambda
    def testNestedStructure(self):
      components = (np.array([1, 2, 3]), (np.array([4., 5.]), np.array([6., 7.])),
                    np.array([8, 9, 10]))
  
      dataset = dataset_ops.Dataset.from_tensors(components)
      self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
                         dtypes.int64), dataset.output_types)
      self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
  
      dataset = dataset.shuffle(10, 10)
      self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
                         dtypes.int64), dataset.output_types)
      self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
  
      dataset = dataset.repeat(-1)
      self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
                         dtypes.int64), dataset.output_types)
      self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
  
      dataset = dataset.filter(lambda x, y, z: True)
      self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
                         dtypes.int64), dataset.output_types)
      self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
  
      dataset = dataset.take(5)
      self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
                         dtypes.int64), dataset.output_types)
      self.assertEquals(([3], ([2], [2]), [3]), dataset.output_shapes)
  
      dataset = dataset.map(lambda x, y, z: ((x, z), (y[0], y[1])))
      self.assertEquals(((dtypes.int64, dtypes.int64),
                         (dtypes.float64, dtypes.float64)), dataset.output_types)
      self.assertEquals((([3], [3]), ([2], [2])), dataset.output_shapes)
  
      dataset = dataset.flat_map(
          lambda x, y: dataset_ops.Dataset.from_tensors(((x[0], x[1]),
                                                         (y[0], y[1])))
      )
      self.assertEquals(((dtypes.int64, dtypes.int64),
                         (dtypes.float64, dtypes.float64)), dataset.output_types)
      self.assertEquals((([3], [3]), ([2], [2])), dataset.output_shapes)
  
      dataset = dataset.batch(32)
      self.assertEquals(((dtypes.int64, dtypes.int64),
                         (dtypes.float64, dtypes.float64)), dataset.output_types)
      self.assertEquals((([None, 3], [None, 3]), ([None, 2], [None, 2])),
                        nest.pack_sequence_as(dataset.output_shapes, [
                            s.as_list()
                            for s in nest.flatten(dataset.output_shapes)
                        ]))
  
      iterator = dataset.make_one_shot_iterator()
      (w, x), (y, z) = iterator.get_next()
      self.assertEquals(dtypes.int64, w.dtype)
      self.assertEquals(dtypes.int64, x.dtype)
      self.assertEquals(dtypes.float64, y.dtype)
      self.assertEquals(dtypes.float64, z.dtype)
      self.assertEquals([None, 3], w.shape.as_list())
      self.assertEquals([None, 3], x.shape.as_list())
      self.assertEquals([None, 2], y.shape.as_list())
      self.assertEquals([None, 2], z.shape.as_list())
  
      iterator = dataset.make_initializable_iterator()
      (w, x), (y, z) = iterator.get_next()
      self.assertEquals(dtypes.int64, w.dtype)
      self.assertEquals(dtypes.int64, x.dtype)
      self.assertEquals(dtypes.float64, y.dtype)
      self.assertEquals(dtypes.float64, z.dtype)
      self.assertEquals([None, 3], w.shape.as_list())
      self.assertEquals([None, 3], x.shape.as_list())
      self.assertEquals([None, 2], y.shape.as_list())
      self.assertEquals([None, 2], z.shape.as_list())
  
      # Define a separate set of components with matching leading
      # dimension for the from-slices constructor.
      components_for_slices = (np.array([1, 2, 3]), (np.array(
          [4., 5., 6.]), np.array([7., 8., 9.])), np.array([10, 11, 12]))
  
      dataset = dataset_ops.Dataset.from_tensor_slices(components_for_slices)
      self.assertEquals((dtypes.int64, (dtypes.float64, dtypes.float64),
                         dtypes.int64), dataset.output_types)
      self.assertEquals(([], ([], []), []), dataset.output_shapes)
  
    def testNestedDict(self):
      components = {"a": {"aa": 1, "ab": [2.0, 2.0]}, "b": [3, 3, 3]}
      dataset = dataset_ops.Dataset.from_tensors(components)
      self.assertEquals(dtypes.int32, dataset.output_types["a"]["aa"])
      self.assertEquals(dtypes.float32, dataset.output_types["a"]["ab"])
      self.assertEquals(dtypes.int32, dataset.output_types["b"])
      self.assertEquals([], dataset.output_shapes["a"]["aa"])
      self.assertEquals([2], dataset.output_shapes["a"]["ab"])
      self.assertEquals([3], dataset.output_shapes["b"])
  
    def testNonSequenceNestedStructure(self):
      components = np.array([1, 2, 3])
  
      dataset = dataset_ops.Dataset.from_tensors(components)
      self.assertEquals(dtypes.int64, dataset.output_types)
      self.assertEquals([3], dataset.output_shapes)
  
      dataset = dataset.filter(
          lambda x: math_ops.reduce_all(math_ops.equal(x, components)))
      self.assertEquals(dtypes.int64, dataset.output_types)
      self.assertEquals([3], dataset.output_shapes)
  
      dataset = dataset.map(lambda x: array_ops.stack([x, x]))
      self.assertEquals(dtypes.int64, dataset.output_types)
      self.assertEquals([2, 3], dataset.output_shapes)
  
      dataset = dataset.flat_map(
          lambda x: dataset_ops.Dataset.from_tensor_slices(x))
      self.assertEquals(dtypes.int64, dataset.output_types)
      self.assertEquals([3], dataset.output_shapes)
  
      iterator = dataset.make_one_shot_iterator()
      get_next = iterator.get_next()
      self.assertEquals(dtypes.int64, get_next.dtype)
      self.assertEquals([3], get_next.shape)
  
    def _testFromGenerator(self, generator, elem_sequence, num_repeats):
      iterator = (
          dataset_ops.Dataset.from_generator(generator, output_types=dtypes.int64)
          .repeat(num_repeats)
          .prefetch(5)
          .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        for _ in range(2):  # Run twice to test reinitialization.
          sess.run(init_op)
          for _ in range(num_repeats):
            for elem in elem_sequence:
              self.assertAllEqual(elem, sess.run(get_next))
          with self.assertRaises(errors.OutOfRangeError):
            sess.run(get_next)
  
    def _testFromGeneratorOneShot(self, generator, elem_sequence, num_repeats):
      iterator = (
          dataset_ops.Dataset.from_generator(generator, output_types=dtypes.int64)
          .repeat(num_repeats)
          .prefetch(5)
          .make_one_shot_iterator())
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        for _ in range(num_repeats):
          for elem in elem_sequence:
            self.assertAllEqual(elem, sess.run(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testFromGeneratorUsingFunction(self):
      def generator():
        for i in range(1, 100):
          yield [i] * i
      elem_sequence = list(generator())
      self._testFromGenerator(generator, elem_sequence, 1)
      self._testFromGenerator(generator, elem_sequence, 5)
      self._testFromGeneratorOneShot(generator, elem_sequence, 1)
      self._testFromGeneratorOneShot(generator, elem_sequence, 5)
  
    def testFromGeneratorUsingList(self):
      generator = lambda: [[i] * i for i in range(1, 100)]
      elem_sequence = list(generator())
      self._testFromGenerator(generator, elem_sequence, 1)
      self._testFromGenerator(generator, elem_sequence, 5)
  
    def testFromGeneratorUsingNdarray(self):
      generator = lambda: np.arange(100, dtype=np.int64)
      elem_sequence = list(generator())
      self._testFromGenerator(generator, elem_sequence, 1)
      self._testFromGenerator(generator, elem_sequence, 5)
  
    def testFromGeneratorUsingGeneratorExpression(self):
      # NOTE(mrry): Generator *expressions* are not repeatable (or in
      # general reusable), because they eagerly evaluate the `for`
      # expression as `iter(range(1, 100))` and discard the means of
      # reconstructing `range(1, 100)`. Wrapping the generator
      # expression in a `lambda` makes it repeatable.
      generator = lambda: ([i] * i for i in range(1, 100))
      elem_sequence = list(generator())
      self._testFromGenerator(generator, elem_sequence, 1)
      self._testFromGenerator(generator, elem_sequence, 5)
  
    def testFromMultipleConcurrentGenerators(self):
      num_inner_repeats = 5
      num_outer_repeats = 100
  
      def generator():
        for i in range(1, 10):
          yield ([i] * i, [i, i ** 2, i ** 3])
      input_list = list(generator())
  
      # The interleave transformation is essentially a flat map that
      # draws from multiple input datasets concurrently (in a cyclic
      # fashion). By placing `Datsaet.from_generator()` inside an
      # interleave, we test its behavior when multiple iterators are
      # active at the same time; by additionally prefetching inside the
      # interleave, we create the possibility of parallel (modulo GIL)
      # invocations to several iterators created by the same dataset.
      def interleave_fn(_):
        return (dataset_ops.Dataset.from_generator(
            generator, output_types=(dtypes.int64, dtypes.int64),
            output_shapes=([None], [3]))
                .repeat(num_inner_repeats).prefetch(5))
  
      iterator = (
          dataset_ops.Dataset.range(num_outer_repeats)
          .interleave(interleave_fn, cycle_length=10,
                      block_length=len(input_list))
          .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        sess.run(init_op)
        for _ in range(num_inner_repeats * num_outer_repeats):
          for elem in input_list:
            val0, val1 = sess.run(get_next)
            self.assertAllEqual(elem[0], val0)
            self.assertAllEqual(elem[1], val1)
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testFromGeneratorsRunningInParallel(self):
      num_parallel_iterators = 3
  
      # Define shared state that multiple iterator instances will access to
      # demonstrate their concurrent activity.
      lock = threading.Lock()
      condition = threading.Condition(lock)
      next_ticket = [0]  # GUARDED_BY(lock)
  
      def generator():
        # NOTE(mrry): We yield one element before the barrier, because
        # the current implementation of `Dataset.interleave()` must
        # fetch one element from each incoming dataset to start the
        # prefetching.
        yield 0
  
        # Define a barrier that `num_parallel_iterators` iterators must enter
        # before any can proceed. Demonstrates that multiple iterators may be
        # active at the same time.
        condition.acquire()
        ticket = next_ticket[0]
        next_ticket[0] += 1
        if ticket == num_parallel_iterators - 1:
          # The last iterator to join the barrier notifies the others.
          condition.notify_all()
        else:
          # Wait until the last iterator enters the barrier.
          while next_ticket[0] < num_parallel_iterators:
            condition.wait()
        condition.release()
  
        yield 1
  
      # As in `testFromMultipleConcurrentGenerators()`, we use a combination of
      # `Dataset.interleave()` and `Dataset.prefetch()` to cause multiple
      # iterators to be active concurrently.
      def interleave_fn(_):
        return dataset_ops.Dataset.from_generator(
            generator, output_types=dtypes.int64, output_shapes=[]).prefetch(2)
  
      iterator = (
          dataset_ops.Dataset.range(num_parallel_iterators)
          .interleave(
              interleave_fn, cycle_length=num_parallel_iterators, block_length=1)
          .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        sess.run(init_op)
        for elem in [0, 1]:
          for _ in range(num_parallel_iterators):
            self.assertAllEqual(elem, sess.run(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testFromGeneratorTypeError(self):
      def generator():
        yield np.array([1, 2, 3], dtype=np.int64)
        yield np.array([4, 5, 6], dtype=np.int64)
        yield "ERROR"
        yield np.array([7, 8, 9], dtype=np.int64)
  
      iterator = (dataset_ops.Dataset.from_generator(
          generator, output_types=dtypes.int64, output_shapes=[3])
                  .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        sess.run(init_op)
        self.assertAllEqual([1, 2, 3], sess.run(get_next))
        self.assertAllEqual([4, 5, 6], sess.run(get_next))
        with self.assertRaisesOpError(r"element of type .*int64.* was expected"):
          sess.run(get_next)
        self.assertAllEqual([7, 8, 9], sess.run(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testFromGeneratorShapeError(self):
      def generator():
        yield np.array([1, 2, 3], dtype=np.int64)
        yield np.array([4, 5, 6], dtype=np.int64)
        yield np.array([7, 8, 9, 10], dtype=np.int64)
        yield np.array([11, 12, 13], dtype=np.int64)
  
      iterator = (dataset_ops.Dataset.from_generator(
          generator, output_types=dtypes.int64, output_shapes=[3])
                  .make_initializable_iterator())
      init_op = iterator.initializer
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        sess.run(init_op)
        self.assertAllEqual([1, 2, 3], sess.run(get_next))
        self.assertAllEqual([4, 5, 6], sess.run(get_next))
        with self.assertRaisesOpError(r"element of shape \(3,\) was expected"):
          sess.run(get_next)
        self.assertAllEqual([11, 12, 13], sess.run(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testSplitPipelineFailsWithPlacementError(self):
      with session.Session(
          target="",
          config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
  
        dataset = dataset_ops.Dataset.from_tensors(0)
  
        # Define a pipeline that attempts to use variables on two
        # different devices.
        #
        # Initialize the variables before creating to iterator, to avoid the
        # placement algorithm overriding the DT_RESOURCE colocation constraints.
        with ops.device("/cpu:0"):
          var_0 = resource_variable_ops.ResourceVariable(initial_value=0)
          dataset = dataset.map(lambda x: x + var_0.read_value())
        sess.run(var_0.initializer)
  
        with ops.device("/cpu:1"):
          var_1 = resource_variable_ops.ResourceVariable(initial_value=0)
          dataset = dataset.map(lambda x: x + var_1.read_value())
        sess.run(var_1.initializer)
  
        iterator = dataset.make_initializable_iterator()
  
        with self.assertRaisesRegexp(
            errors.InvalidArgumentError,
            "Trying to access resource located in device"):
          sess.run(iterator.initializer)
  
    def testRestructureDataset(self):
      components = (array_ops.placeholder(dtypes.int32),
                    (array_ops.placeholder(dtypes.int32, shape=[None]),
                     array_ops.placeholder(dtypes.int32, shape=[20, 30])))
      dataset = dataset_ops.Dataset.from_tensors(components)
  
      i32 = dtypes.int32
  
      test_cases = [((i32, i32, i32), None),
                    (((i32, i32), i32), None),
                    ((i32, i32, i32), (None, None, None)),
                    ((i32, i32, i32), ([17], [17], [20, 30]))]
  
      for new_types, new_shape_lists in test_cases:
        # pylint: disable=protected-access
        new = batching._RestructuredDataset(dataset, new_types, new_shape_lists)
        # pylint: enable=protected-access
        self.assertEqual(new_types, new.output_types)
        if new_shape_lists is not None:
          for expected_shape_list, shape in zip(
              nest.flatten(new_shape_lists), nest.flatten(new.output_shapes)):
            if expected_shape_list is None:
              self.assertIs(None, shape.ndims)
            else:
              self.assertEqual(expected_shape_list, shape.as_list())
  
      fail_cases = [((i32, dtypes.int64, i32), None),
                    ((i32, i32, i32, i32), None),
                    ((i32, i32, i32), ((None, None), None)),
                    ((i32, i32, i32), (None, None, None, None)),
                    ((i32, i32, i32), (None, [None], [21, 30]))]
  
      for new_types, new_shape_lists in fail_cases:
        with self.assertRaises(ValueError):
          # pylint: disable=protected-access
          new = batching._RestructuredDataset(dataset, new_types, new_shape_lists)
          # pylint: enable=protected-access
  
  
  if __name__ == "__main__":
    test.main()
  
apis: tensorflow.python.ops.math_ops.equal tensorflow.python.data.util.nest.flatten tensorflow.python.ops.array_ops.placeholder tensorflow.contrib.data.python.ops.dataset_ops.Dataset.range tensorflow.contrib.data.python.ops.dataset_ops.Dataset.from_sparse_tensor_slices tensorflow.contrib.data.python.ops.batching._RestructuredDataset tensorflow.python.framework.ops.device tensorflow.contrib.data.python.ops.dataset_ops.Dataset.from_tensors tensorflow.python.ops.array_ops.sparse_placeholder numpy.arange tensorflow.python.framework.sparse_tensor.SparseTensorValue tensorflow.contrib.data.python.ops.dataset_ops.Dataset.from_tensor_slices tensorflow.core.protobuf.config_pb2.ConfigProto numpy.empty tensorflow.python.platform.test.main tensorflow.python.ops.resource_variable_ops.ResourceVariable tensorflow.python.ops.array_ops.stack numpy.array tensorflow.contrib.data.python.ops.dataset_ops.Dataset.from_generator