1 files changed, 0 insertions, 242 deletions
diff --git a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py
deleted file mode 100644
index 33c48e20be..0000000000
--- a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py
+++ /dev/null
@@ -1,242 +0,0 @@
-# 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 the experimental input pipeline ops."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.contrib.data.python.ops import sliding
-from tensorflow.python.data.ops import dataset_ops
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import errors
-from tensorflow.python.framework import sparse_tensor
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.platform import test
-
-
-class SlideDatasetTest(test.TestCase):
-
-  def testSlideDataset(self):
-    """Test an dataset that maps a TF function across its input elements."""
-    components = (np.arange(7),
-                  np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
-                  np.array(37.0) * np.arange(7))
-
-    count = array_ops.placeholder(dtypes.int64, shape=[])
-    window_size = array_ops.placeholder(dtypes.int64, shape=[])
-    stride = array_ops.placeholder(dtypes.int64, shape=[])
-
-    def _map_fn(x, y, z):
-      return math_ops.square(x), math_ops.square(y), math_ops.square(z)
-
-    # The pipeline is TensorSliceDataset -> MapDataset(square_3) ->
-    # RepeatDataset(count) -> _SlideDataset(window_size, stride).
-    iterator = (dataset_ops.Dataset.from_tensor_slices(components)
-                .map(_map_fn)
-                .repeat(count)
-                .apply(sliding.sliding_window_batch(window_size, stride))
-                .make_initializable_iterator())
-    init_op = iterator.initializer
-    get_next = iterator.get_next()
-
-    self.assertEqual([[None] + list(c.shape[1:]) for c in components],
-                     [t.shape.as_list() for t in get_next])
-
-    with self.test_session() as sess:
-      # Slide over a finite input, where the window_size divides the
-      # total number of elements.
-      sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 7})
-      # Same formula with convolution layer.
-      num_batches = (20 * 7 - 14) // 7 + 1
-      for i in range(num_batches):
-        result = sess.run(get_next)
-        for component, result_component in zip(components, result):
-          for j in range(14):
-            self.assertAllEqual(component[(i*7 + j) % 7]**2,
-                                result_component[j])
-      with self.assertRaises(errors.OutOfRangeError):
-        sess.run(get_next)
-
-      # Slide over a finite input, where the window_size does not
-      # divide the total number of elements.
-      sess.run(init_op, feed_dict={count: 20, window_size: 17, stride: 9})
-
-      num_batches = (20 * 7 - 17) // 9 + 1
-      for i in range(num_batches):
-        result = sess.run(get_next)
-        for component, result_component in zip(components, result):
-          for j in range(17):
-            self.assertAllEqual(component[(i*9 + j) % 7]**2,
-                                result_component[j])
-      with self.assertRaises(errors.OutOfRangeError):
-        sess.run(get_next)
-
-      # Slide over a finite input, which is less than window_size,
-      # should fail straight away.
-      sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 4})
-      with self.assertRaises(errors.OutOfRangeError):
-        sess.run(get_next)
-
-      sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 8})
-      with self.assertRaises(errors.OutOfRangeError):
-        sess.run(get_next)
-
-      # Slide over an empty input should fail straight away.
-      sess.run(init_op, feed_dict={count: 0, window_size: 8, stride: 4})
-      with self.assertRaises(errors.OutOfRangeError):
-        sess.run(get_next)
-
-      # Empty window_size should be an initialization time error.
-      with self.assertRaises(errors.InvalidArgumentError):
-        sess.run(init_op, feed_dict={count: 14, window_size: 0, stride: 0})
-
-      # Invalid stride should be an initialization time error.
-      with self.assertRaises(errors.InvalidArgumentError):
-        sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 0})
-      with self.assertRaises(errors.InvalidArgumentError):
-        sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 3})
-      with self.assertRaises(errors.InvalidArgumentError):
-        sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 5})
-
-  def assertSparseValuesEqual(self, a, b):
-    self.assertAllEqual(a.indices, b.indices)
-    self.assertAllEqual(a.values, b.values)
-    self.assertAllEqual(a.dense_shape, b.dense_shape)
-
-  def testSlideSparse(self):
-
-    def _sparse(i):
-      return sparse_tensor.SparseTensorValue(
-          indices=[[0]], values=(i * [1]), dense_shape=[1])
-
-    iterator = dataset_ops.Dataset.range(10).map(_sparse).apply(
-        sliding.sliding_window_batch(5, 3)).make_initializable_iterator()
-    init_op = iterator.initializer
-    get_next = iterator.get_next()
-
-    with self.test_session() as sess:
-      sess.run(init_op)
-      num_batches = (10 - 5) // 3 + 1
-      for i in range(num_batches):
-        actual = sess.run(get_next)
-        expected = sparse_tensor.SparseTensorValue(
-            indices=[[0, 0], [1, 0], [2, 0], [3, 0], [4, 0]],
-            values=[i * 3, i * 3 + 1, i * 3 + 2, i * 3 + 3, i * 3 + 4],
-            dense_shape=[5, 1])
-        self.assertTrue(sparse_tensor.is_sparse(actual))
-        self.assertSparseValuesEqual(actual, expected)
-      with self.assertRaises(errors.OutOfRangeError):
-        sess.run(get_next)
-
-  def testSlideSparseWithDifferentDenseShapes(self):
-
-    def _sparse(i):
-      return sparse_tensor.SparseTensorValue(
-          indices=array_ops.expand_dims(
-              math_ops.range(i, dtype=dtypes.int64), 1),
-          values=array_ops.fill([math_ops.to_int32(i)], i),
-          dense_shape=[i])
-
-    iterator = dataset_ops.Dataset.range(10).map(_sparse).apply(
-        sliding.sliding_window_batch(5, 3)).make_initializable_iterator()
-    init_op = iterator.initializer
-    get_next = iterator.get_next()
-
-    with self.test_session() as sess:
-      sess.run(init_op)
-      num_batches = (10 - 5) // 3 + 1
-      for i in range(num_batches):
-        actual = sess.run(get_next)
-        expected_indices = []
-        expected_values = []
-        for j in range(5):
-          for k in range(i * 3 + j):
-            expected_indices.append([j, k])
-            expected_values.append(i * 3 + j)
-        expected = sparse_tensor.SparseTensorValue(
-            indices=expected_indices,
-            values=expected_values,
-            dense_shape=[5, i * 3 + 5 - 1])
-        self.assertTrue(sparse_tensor.is_sparse(actual))
-        self.assertSparseValuesEqual(actual, expected)
-      with self.assertRaises(errors.OutOfRangeError):
-        sess.run(get_next)
-
-  def testNestedSlideSparse(self):
-
-    def _sparse(i):
-      return sparse_tensor.SparseTensorValue(
-          indices=[[0]], values=(i * [1]), dense_shape=[1])
-
-    iterator = (dataset_ops.Dataset.range(10)
-                .map(_sparse)
-                .apply(sliding.sliding_window_batch(4, 2))
-                .apply(sliding.sliding_window_batch(3, 1))
-                .make_initializable_iterator())
-    init_op = iterator.initializer
-    get_next = iterator.get_next()
-
-    with self.test_session() as sess:
-      sess.run(init_op)
-      # Slide: 1st batch.
-      actual = sess.run(get_next)
-      expected = sparse_tensor.SparseTensorValue(
-          indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0],
-                   [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0],
-                   [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]],
-          values=[0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 6, 7],
-          dense_shape=[3, 4, 1])
-      self.assertTrue(sparse_tensor.is_sparse(actual))
-      self.assertSparseValuesEqual(actual, expected)
-      # Slide: 2nd batch.
-      actual = sess.run(get_next)
-      expected = sparse_tensor.SparseTensorValue(
-          indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0],
-                   [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0],
-                   [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]],
-          values=[2, 3, 4, 5, 4, 5, 6, 7, 6, 7, 8, 9],
-          dense_shape=[3, 4, 1])
-      self.assertTrue(sparse_tensor.is_sparse(actual))
-      self.assertSparseValuesEqual(actual, expected)
-      with self.assertRaises(errors.OutOfRangeError):
-        sess.run(get_next)
-
-  def testSlideShapeError(self):
-
-    def generator():
-      yield [1.0, 2.0, 3.0]
-      yield [4.0, 5.0, 6.0]
-      yield [7.0, 8.0, 9.0, 10.0]
-
-    iterator = (dataset_ops.Dataset.from_generator(generator, dtypes.float32,
-                                                   output_shapes=[None])
-                .apply(sliding.sliding_window_batch(3, 1))
-                .make_initializable_iterator())
-    next_element = iterator.get_next()
-
-    with self.test_session() as sess:
-      sess.run(iterator.initializer)
-      with self.assertRaisesRegexp(
-          errors.InvalidArgumentError,
-          r"Cannot batch tensors with different shapes in component 0. "
-          r"First element had shape \[3\] and element 2 had shape \[4\]."):
-        sess.run(next_element)
-
-
-if __name__ == "__main__":
-  test.main()