path: root/tensorflow/python/kernel_tests/slice_op_test.py

# Copyright 2015 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.
# ==============================================================================
"""Functional tests for slice op."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin

from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.platform import test


class SliceTest(test.TestCase):

  def testEmpty(self):
    inp = np.random.rand(4, 4).astype("f")
    for k in xrange(4):
      with self.test_session(use_gpu=True):
        a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.float32)
        slice_t = a[2, k:k]
        slice_val = slice_t.eval()
      self.assertAllEqual(slice_val, inp[2, k:k])

  def testInt32(self):
    inp = np.random.rand(4, 4).astype("i")
    for k in xrange(4):
      with self.test_session(use_gpu=True):
        a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.int32)
        slice_t = a[2, k:k]
        slice_val = slice_t.eval()
      self.assertAllEqual(slice_val, inp[2, k:k])

  def testInt64Slicing(self):
    with self.test_session(use_gpu=True):
      a = constant_op.constant([0, 1, 2], dtype=dtypes.int64)

      # Slice using int64 Tensor.
      i = constant_op.constant(1, dtype=dtypes.int64)
      slice_t = a[i]
      slice_val = slice_t.eval()
      self.assertAllEqual(1, slice_val)
      slice_t = a[i:i+1]
      slice_val = slice_t.eval()
      self.assertAllEqual([1], slice_val)

      # Slice using int64 integer.
      i = np.asarray(1).astype(np.int64)
      slice_t = a[i]
      slice_val = slice_t.eval()
      self.assertAllEqual(1, slice_val)
      slice_t = a[i:i+1]
      slice_val = slice_t.eval()
      self.assertAllEqual([1], slice_val)

  def testSelectAll(self):
    for _ in range(10):
      with self.test_session(use_gpu=True):
        inp = np.random.rand(4, 4, 4, 4).astype("f")
        a = constant_op.constant(inp, shape=[4, 4, 4, 4], dtype=dtypes.float32)

        slice_explicit_t = array_ops.slice(a, [0, 0, 0, 0], [-1, -1, -1, -1])
        slice_implicit_t = a[:, :, :, :]

        self.assertAllEqual(inp, slice_explicit_t.eval())
        self.assertAllEqual(inp, slice_implicit_t.eval())
        self.assertEqual(inp.shape, slice_explicit_t.get_shape())
        self.assertEqual(inp.shape, slice_implicit_t.get_shape())

  def testSingleDimension(self):
    for _ in range(10):
      with self.test_session(use_gpu=True):
        inp = np.random.rand(10).astype("f")
        a = constant_op.constant(inp, shape=[10], dtype=dtypes.float32)

        hi = np.random.randint(0, 9)
        scalar_t = a[hi]
        scalar_val = scalar_t.eval()
        self.assertAllEqual(scalar_val, inp[hi])

        if hi > 0:
          lo = np.random.randint(0, hi)
        else:
          lo = 0
        slice_t = a[lo:hi]
        slice_val = slice_t.eval()
        self.assertAllEqual(slice_val, inp[lo:hi])

  def testScalarInput(self):
    input_val = 0
    with self.test_session() as sess:
      # Test with constant input; shape inference fails.
      with self.assertRaisesWithPredicateMatch(ValueError, "out of range"):
        constant_op.constant(input_val)[:].get_shape()

      # Test evaluating with non-constant input; kernel execution fails.
      input_t = array_ops.placeholder(dtypes.int32)
      slice_t = input_t[:]
      with self.assertRaisesWithPredicateMatch(errors_impl.InvalidArgumentError,
                                               "out of range"):
        sess.run([slice_t], feed_dict={input_t: input_val})

  def testInvalidIndex(self):
    input_val = [1, 2]
    with self.test_session() as sess:
      # Test with constant input; shape inference fails.
      with self.assertRaisesWithPredicateMatch(ValueError, "out of range"):
        constant_op.constant(input_val)[1:, 1:].get_shape()

      # Test evaluating with non-constant input; kernel execution fails.
      input_t = array_ops.placeholder(dtypes.int32)
      slice_t = input_t[1:, 1:]
      with self.assertRaisesWithPredicateMatch(errors_impl.InvalidArgumentError,
                                               "out of range"):
        sess.run([slice_t], feed_dict={input_t: input_val})

  def _testSliceMatrixDim0(self, x, begin, size):
    with self.test_session(use_gpu=True):
      tf_ans = array_ops.slice(x, [begin, 0], [size, x.shape[1]]).eval()
    np_ans = x[begin:begin + size, :]
    self.assertAllEqual(tf_ans, np_ans)

  def testSliceMatrixDim0(self):
    x = np.random.rand(8, 4).astype("f")
    self._testSliceMatrixDim0(x, 1, 2)
    self._testSliceMatrixDim0(x, 3, 3)
    y = np.random.rand(8, 7).astype("f")  # 7 * sizeof(float) is not aligned
    self._testSliceMatrixDim0(y, 1, 2)
    self._testSliceMatrixDim0(y, 3, 3)

  def testSingleElementAll(self):
    for _ in range(10):
      with self.test_session(use_gpu=True):
        inp = np.random.rand(4, 4).astype("f")
        a = constant_op.constant(inp, shape=[4, 4], dtype=dtypes.float32)

        x, y = np.random.randint(0, 3, size=2).tolist()
        slice_t = a[x, 0:y]
        slice_val = slice_t.eval()
      self.assertAllEqual(slice_val, inp[x, 0:y])

  def testSimple(self):
    with self.test_session(use_gpu=True) as sess:
      inp = np.random.rand(4, 4).astype("f")
      a = constant_op.constant(
          [float(x) for x in inp.ravel(order="C")],
          shape=[4, 4],
          dtype=dtypes.float32)
      slice_t = array_ops.slice(a, [0, 0], [2, 2])
      slice2_t = a[:2, :2]
      slice_val, slice2_val = sess.run([slice_t, slice2_t])
    self.assertAllEqual(slice_val, inp[:2, :2])
    self.assertAllEqual(slice2_val, inp[:2, :2])
    self.assertEqual(slice_val.shape, slice_t.get_shape())
    self.assertEqual(slice2_val.shape, slice2_t.get_shape())

  def testComplex(self):
    with self.test_session(use_gpu=True):
      inp = np.random.rand(4, 10, 10, 4).astype("f")
      a = constant_op.constant(inp, dtype=dtypes.float32)

      x = np.random.randint(0, 9)
      z = np.random.randint(0, 9)
      if z > 0:
        y = np.random.randint(0, z)
      else:
        y = 0
      slice_t = a[:, x, y:z, :]
      self.assertAllEqual(slice_t.eval(), inp[:, x, y:z, :])

  def testRandom(self):
    # Random dims of rank 6
    input_shape = np.random.randint(0, 20, size=6)
    inp = np.random.rand(*input_shape).astype("f")
    with self.test_session(use_gpu=True) as sess:
      a = constant_op.constant(
          [float(x) for x in inp.ravel(order="C")],
          shape=input_shape,
          dtype=dtypes.float32)
      indices = [0 if x == 0 else np.random.randint(x) for x in input_shape]
      sizes = [
          np.random.randint(0, input_shape[i] - indices[i] + 1)
          for i in range(6)
      ]
      slice_t = array_ops.slice(a, indices, sizes)
      slice2_t = a[indices[0]:indices[0] + sizes[0], indices[1]:indices[
          1] + sizes[1], indices[2]:indices[2] + sizes[2], indices[3]:indices[3]
                   + sizes[3], indices[4]:indices[4] + sizes[4], indices[5]:
                   indices[5] + sizes[5]]

      slice_val, slice2_val = sess.run([slice_t, slice2_t])

    expected_val = inp[indices[0]:indices[0] + sizes[0], indices[1]:indices[
        1] + sizes[1], indices[2]:indices[2] + sizes[2], indices[3]:indices[
            3] + sizes[3], indices[4]:indices[4] + sizes[4], indices[5]:indices[
                5] + sizes[5]]
    self.assertAllEqual(slice_val, expected_val)
    self.assertAllEqual(slice2_val, expected_val)
    self.assertEqual(expected_val.shape, slice_t.get_shape())
    self.assertEqual(expected_val.shape, slice2_t.get_shape())

  def testPartialShapeInference(self):
    z = array_ops.zeros((1, 2, 3))
    self.assertAllEqual(z.get_shape().as_list(), [1, 2, 3])

    m1 = array_ops.slice(z, [0, 0, 0], [-1, -1, -1])
    self.assertAllEqual(m1.get_shape().as_list(), [1, 2, 3])

    m2 = array_ops.slice(z, [0, 0, 0], [constant_op.constant(1) + 0, 2, -1])
    self.assertAllEqual(m2.get_shape().as_list(), [None, 2, None])


  def _testGradientSlice(self, input_shape, slice_begin, slice_size):
    with self.test_session(use_gpu=True):
      num_inputs = np.prod(input_shape)
      num_grads = np.prod(slice_size)
      inp = np.random.rand(num_inputs).astype("f").reshape(input_shape)
      a = constant_op.constant(
          [float(x) for x in inp.ravel(order="C")],
          shape=input_shape,
          dtype=dtypes.float32)
      slice_t = array_ops.slice(a, slice_begin, slice_size)
      grads = np.random.rand(num_grads).astype("f").reshape(slice_size)
      grad_tensor = constant_op.constant(grads)
      grad = gradients_impl.gradients(slice_t, [a], grad_tensor)[0]
      result = grad.eval()

    # Create a zero tensor of the input shape ane place
    # the grads into the right location to compare against TensorFlow.
    np_ans = np.zeros(input_shape)
    slices = []
    for i in xrange(len(input_shape)):
      slices.append(slice(slice_begin[i], slice_begin[i] + slice_size[i]))
    np_ans[slices] = grads

    self.assertAllClose(np_ans, result)

  def _testGradientVariableSize(self):
    with self.test_session(use_gpu=True):
      inp = constant_op.constant([1.0, 2.0, 3.0], name="in")
      out = array_ops.slice(inp, [1], [-1])
      grad_actual = gradients_impl.gradients(out, inp)[0].eval()
    self.assertAllClose([0., 1., 1.], grad_actual)

  def testGradientsAll(self):
    # Slice the middle square out of a 4x4 input
    self._testGradientSlice([4, 4], [1, 1], [2, 2])

    # Slice the upper left square out of a 4x4 input
    self._testGradientSlice([4, 4], [0, 0], [2, 2])

    # Slice a non-square input starting from (2,1)
    self._testGradientSlice([4, 4], [2, 1], [1, 2])

    # Slice a 3D tensor
    self._testGradientSlice([3, 3, 3], [0, 1, 0], [2, 1, 1])

    # Use -1 as a slice dimension.
    self._testGradientVariableSize()

  def testNotIterable(self):
    # NOTE(mrry): If we register __getitem__ as an overloaded
    # operator, Python will valiantly attempt to iterate over the
    # Tensor from 0 to infinity.  This test ensures that this
    # unintended behavior is prevented.
    c = constant_op.constant(5.0)
    with self.assertRaisesWithPredicateMatch(
        TypeError, lambda e: "`Tensor` objects are not iterable" in str(e)):
      for _ in c:
        pass

  def testComputedShape(self):
    # NOTE(mrry): We cannot currently handle partially-known values,
    # because `tf.slice()` uses -1 to specify a wildcard size, and
    # this can't be handled using the
    # `tensor_util.constant_value_as_shape()` trick.
    a = constant_op.constant([[1, 2, 3], [4, 5, 6]])
    begin = constant_op.constant(0)
    size = constant_op.constant(1)
    b = array_ops.slice(a, [begin, 0], [size, 2])
    self.assertEqual([1, 2], b.get_shape())

    begin = array_ops.placeholder(dtypes.int32, shape=())
    c = array_ops.slice(a, [begin, 0], [-1, 2])
    self.assertEqual([None, 2], c.get_shape().as_list())

  def testSliceOfSlice(self):
    with self.test_session(use_gpu=True):
      a = constant_op.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
      b = a[1:, :]
      c = b[:-1, :]
      d = c[1, :]
      res = 2 * d - c[1, :] + a[2, :] - 2 * b[-2, :]
      self.assertAllEqual([0, 0, 0], res.eval())


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