def testDocStringExamples(self):
   """Test the examples in apply_op_to_ragged_values.__doc__."""
   rt = ragged.constant([[1, 2, 3], [], [4, 5], [6]])
   v1 = ragged.map_inner_values(array_ops.ones_like, rt)
   v2 = ragged.map_inner_values(math_ops.multiply, rt, rt)
   v3 = ragged.map_inner_values(math_ops.add, rt, 5)
   with self.test_session():
     self.assertEqual(v1.eval().tolist(), [[1, 1, 1], [], [1, 1], [1]])
     self.assertEqual(v2.eval().tolist(), [[1, 4, 9], [], [16, 25], [36]])
     self.assertEqual(v3.eval().tolist(), [[6, 7, 8], [], [9, 10], [11]])
 def testDocStringExamples(self):
     """Test the examples in apply_op_to_ragged_values.__doc__."""
     rt = ragged.constant([[1, 2, 3], [], [4, 5], [6]])
     v1 = ragged.map_inner_values(array_ops.ones_like, rt)
     v2 = ragged.map_inner_values(math_ops.multiply, rt, rt)
     v3 = ragged.map_inner_values(math_ops.add, rt, 5)
     with self.test_session():
         self.assertEqual(v1.eval().tolist(), [[1, 1, 1], [], [1, 1], [1]])
         self.assertEqual(v2.eval().tolist(),
                          [[1, 4, 9], [], [16, 25], [36]])
         self.assertEqual(v3.eval().tolist(),
                          [[6, 7, 8], [], [9, 10], [11]])
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  def testRaggedMapOnStructure_RaggedOutputs(self):
    batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]])
    # [[10, 20, 30], [40], [50, 60, 70]]
    robin = ragged.map_inner_values(mo.multiply, batman, 10)

    features = {'batman': batman, 'robin': robin}

    def _increment(f):
      return {
          'batman': ragged.add(f['batman'], 1),
          'robin': ragged.add(f['robin'], 1),
      }

    output = ragged.map_fn(
        fn=_increment,
        elems=features,
        infer_shape=False,
        dtype={
            'batman':
                ragged.RaggedTensorType(dtype=dtypes.int32, ragged_rank=1),
            'robin':
                ragged.RaggedTensorType(dtype=dtypes.int32, ragged_rank=1)
        },
    )

    with self.test_session():
      self.assertAllEqual(output['batman'].eval().tolist(),
                          [[2, 3, 4], [5], [6, 7, 8]])
      self.assertAllEqual(output['robin'].eval().tolist(),
                          [[11, 21, 31], [41], [51, 61, 71]])
 def assertRaggedMapInnerValuesReturns(self,
                                       op,
                                       expected,
                                       args=(),
                                       kwargs=None):
   kwargs = kwargs or {}
   result = ragged.map_inner_values(op, *args, **kwargs)
   with self.test_session():
     self.assertEqual(result.eval().tolist(), expected)
 def assertRaggedMapInnerValuesReturns(self,
                                       op,
                                       expected,
                                       args=(),
                                       kwargs=None):
     kwargs = kwargs or {}
     result = ragged.map_inner_values(op, *args, **kwargs)
     with self.test_session():
         self.assertEqual(result.eval().tolist(), expected)
 def testRaggedTensorSplitsMismatchErrorAtRuntime(self):
   splits1 = array_ops.placeholder_with_default(
       constant_op.constant([0, 3, 3, 5], dtypes.int64), None)
   splits2 = array_ops.placeholder_with_default(
       constant_op.constant([0, 1, 3, 5], dtypes.int64), None)
   x = ragged.from_row_splits([3, 1, 4, 1, 5], splits1)
   y = ragged.from_row_splits([1, 2, 3, 4, 5], splits2)
   result = ragged.map_inner_values(math_ops.add, x, y)
   with self.test_session():
     self.assertRaisesRegexp(
         errors.InvalidArgumentError,
         r'\[Inputs must have identical ragged splits\] '
         r'\[Condition x == y did not hold element-wise:\].*', result.eval)
 def testRaggedTensorSplitsMismatchErrorAtRuntime(self):
     splits1 = array_ops.placeholder_with_default(
         constant_op.constant([0, 3, 3, 5], dtypes.int64), None)
     splits2 = array_ops.placeholder_with_default(
         constant_op.constant([0, 1, 3, 5], dtypes.int64), None)
     x = ragged.from_row_splits([3, 1, 4, 1, 5], splits1)
     y = ragged.from_row_splits([1, 2, 3, 4, 5], splits2)
     result = ragged.map_inner_values(math_ops.add, x, y)
     with self.test_session():
         self.assertRaisesRegexp(
             errors.InvalidArgumentError,
             r'\[Inputs must have identical ragged splits\] '
             r'\[Condition x == y did not hold element-wise:\].*',
             result.eval)
  def testGradient(self):
    # rt1.shape == rt2.shape == [2, (D2), (D3), 2].
    rt1 = ragged.constant([[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0]]]],
                          ragged_rank=2)
    rt2 = ragged.constant([[[[9.0, 8.0], [7.0, 6.0]], [[5.0, 4.0]]]],
                          ragged_rank=2)
    rt = ragged.map_inner_values(math_ops.add, rt1, rt2 * 2.0)
    st = ragged.to_sparse(rt)

    g1, g2 = gradients_impl.gradients(st.values, [rt1.inner_values,
                                                  rt2.inner_values])
    print(g1, g2)
    with self.test_session():
      self.assertEqual(g1.eval().tolist(), [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])
      self.assertEqual(g2.eval().tolist(), [[2.0, 2.0], [2.0, 2.0], [2.0, 2.0]])
Esempio n. 9
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    def testGradient(self):
        # rt1.shape == rt2.shape == [2, (D2), (D3), 2].
        rt1 = ragged.constant([[[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0]]]],
                              ragged_rank=2)
        rt2 = ragged.constant([[[[9.0, 8.0], [7.0, 6.0]], [[5.0, 4.0]]]],
                              ragged_rank=2)
        rt = ragged.map_inner_values(math_ops.add, rt1, rt2 * 2.0)
        st = ragged.to_sparse(rt)

        g1, g2 = gradients_impl.gradients(st.values,
                                          [rt1.inner_values, rt2.inner_values])
        print(g1, g2)
        with self.test_session():
            self.assertEqual(g1.eval().tolist(),
                             [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]])
            self.assertEqual(g2.eval().tolist(),
                             [[2.0, 2.0], [2.0, 2.0], [2.0, 2.0]])
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  def testRaggedMapOnStructure(self):
    batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]])
    # [[10, 20, 30], [40], [50, 60, 70]]
    robin = ragged.map_inner_values(mo.multiply, batman, 10)

    features = {'batman': batman, 'robin': robin}

    def _reduce_sum_from_all(f):
      return mo.reduce_sum(f['batman']) + mo.reduce_sum(f['robin'])

    output = ragged.map_fn(
        fn=_reduce_sum_from_all,
        elems=features,
        dtype=dtypes.int32,
    )

    with self.test_session():
      self.assertAllEqual(output.eval().tolist(), [66, 44, 198])
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class RaggedMapOpTest(test_util.TensorFlowTestCase, parameterized.TestCase):
  @parameterized.parameters([
      # The following test sets map over a RaggedTensor and apply a
      # transformation that returns with shape:
      # [d1, (d2)] -> [d1]
      dict(
          fn=mo.reduce_mean,
          elems=[[1, 2, 3], [4, 5], [6, 7]],
          expected_output=[2, 4, 6],
      ),
      dict(
          fn=string_ops.reduce_join,
          elems=[['foo', 'bar', 'baz'], ['a'], ['b', 'c']],
          expected_output=[b'foobarbaz', b'a', b'bc'],
          dtype=dtypes.string,
      ),
      # [d1, (d2)] -> [d1, 2]
      dict(
          fn=lambda x: array_ops.stack([mo.reduce_mean(x), mo.reduce_sum(x)]),
          # fn=self.stack_mean_and_sum,
          elems=[[1, 2, 3], [4, 5], [6, 7]],
          expected_output=[[2, 6], [4.5, 9], [6.5, 13]],
          dtype=dtypes.float32,
      ),
      # [d1, (d2)] -> [d1, (d2)]
      dict(
          fn=lambda x: x+1,
          elems=[[1, 2, 3], [4, 5], [6, 7]],
          expected_output=[[2, 3, 4], [5, 6], [7, 8]],
          dtype=dtypes.int64,
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=1),
      ),
      # [d1, (d2), d3] -> [d1, (d2), d3]
      dict(
          fn=lambda x: x+1,
          elems=[[[1, 2], [3, 4]], [], [[5, 6], [7, 8], [9, 0]]],
          elems_ragged_rank=1,
          expected_ragged_rank=1,
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=1),
          expected_output=[[[2, 3], [4, 5]], [], [[6, 7], [8, 9], [10, 1]]],
      ),
      # [d1, (d2)] -> [d1, (d2), (d3)]
      dict(
          fn=lambda x: ragged.from_row_starts(x, [0]),
          elems=[[1, 2, 3], [4, 5], [6, 7]],
          expected_output=[[[1, 2, 3]], [[4, 5]], [[6, 7]]],
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=2),
      ),
      # [d1, (d2), (d3)] -> [d1, (d2), (d3)]
      dict(
          fn=lambda x: ragged.map_inner_values(mo.add, x, 1),
          elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
          expected_output=[[[2, 3, 4]], [[5, 6], [7, 8]]],
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=2),
      ),
      # [d1, (d2), (d3)] -> [d1, (d2)]
      dict(
          fn=lambda x: ragged.reduce_sum(x, axis=1),
          elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
          expected_output=[[6], [9, 13]],
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=1),
      ),
      # [d1, (d2), (d3)] -> [d1, (d3)]
      dict(
          fn=lambda x: ragged.reduce_sum(x, axis=0),
          elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
          expected_output=[[1, 2, 3], [10, 12]],
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=1),
      ),
      # [d1, (d2), (d3)] -> [d1]
      dict(
          fn=ragged.reduce_sum,
          elems=[[[1, 2, 3]], [[4, 5], [6, 7]]],
          expected_output=[6, 22],
          result_dtype=dtypes.int64,
      ),
      # [d1] -> [d1, (d2)]
      dict(
          fn=mo.range,
          elems=[4, 0, 2],
          expected_output=[[0, 1, 2, 3], [], [0, 1]],
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=1),
      ),
      # [d1] -> [d1, (d2), (d3)]
      dict(
          fn=lambda x: ragged.range(mo.range(x)),
          elems=[5, 0, 3],
          expected_output=[
              [[], [0], [0, 1], [0, 1, 2], [0, 1, 2, 3]], [], [[], [0], [0, 1]]
          ],
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=2),
      ),
      # [d1, (d2), (d3), (d4a), (d5)] ->  [d1, (d2), (d3), (d4b), (d5)]
      dict(
          fn=lambda x: ragged.add(x, 1),
          elems=[[[[[1, 2, 3]], [[4], [5]]]], [[[[6, 7]]], [[[8], []]]]],
          expected_output=[[[[[2, 3, 4]], [[5], [6]]]],
                           [[[[7, 8]]], [[[9], []]]]],
          result_dtype=ragged.RaggedTensorType(dtype=dtypes.int64,
                                               ragged_rank=4),
      ),
  ])

  def testRaggedMap(
      self,
      fn,
      elems,
      expected_output,
      expected_ragged_rank=None,
      result_ragged_rank=None,
      elems_ragged_rank=None,
      dtype=dtypes.int64,
      result_dtype=None,
      infer_shape=False,
  ):
    elems = ragged.constant(elems, dtype, elems_ragged_rank)
    output = ragged.map_fn(
        fn=fn, elems=elems, dtype=result_dtype, infer_shape=infer_shape)

    expected_rt = ragged.constant(
        expected_output, ragged_rank=expected_ragged_rank)
    with self.test_session():
      if ragged.is_ragged(expected_output):
        self.assertEqual(output.ragged_rank, expected_rt.ragged_rank)
      output_values = self.evaluate(output)
      self.assertAllEqual(expected_output, output_values.tolist())

  def testRaggedMapOnStructure(self):
    batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]])
    # [[10, 20, 30], [40], [50, 60, 70]]
    robin = ragged.map_inner_values(mo.multiply, batman, 10)

    features = {'batman': batman, 'robin': robin}

    def _reduce_sum_from_all(f):
      return mo.reduce_sum(f['batman']) + mo.reduce_sum(f['robin'])

    output = ragged.map_fn(
        fn=_reduce_sum_from_all,
        elems=features,
        dtype=dtypes.int32,
    )

    with self.test_session():
      self.assertAllEqual(output.eval().tolist(), [66, 44, 198])

  # Test mapping over a dict of RTs can produce a dict of RTs.
  def testRaggedMapOnStructure_RaggedOutputs(self):
    batman = ragged.constant([[1, 2, 3], [4], [5, 6, 7]])
    # [[10, 20, 30], [40], [50, 60, 70]]
    robin = ragged.map_inner_values(mo.multiply, batman, 10)

    features = {'batman': batman, 'robin': robin}

    def _increment(f):
      return {
          'batman': ragged.add(f['batman'], 1),
          'robin': ragged.add(f['robin'], 1),
      }

    output = ragged.map_fn(
        fn=_increment,
        elems=features,
        infer_shape=False,
        dtype={
            'batman':
                ragged.RaggedTensorType(dtype=dtypes.int32, ragged_rank=1),
            'robin':
                ragged.RaggedTensorType(dtype=dtypes.int32, ragged_rank=1)
        },
    )

    with self.test_session():
      self.assertAllEqual(output['batman'].eval().tolist(),
                          [[2, 3, 4], [5], [6, 7, 8]])
      self.assertAllEqual(output['robin'].eval().tolist(),
                          [[11, 21, 31], [41], [51, 61, 71]])

  def testZip(self):
    x = ragged.constant([[10, 20], [30, 40], [50, 60], [70], [80, 90, 100]],
                        dtypes.int64)
    y = array_ops.expand_dims(
        mo.range(ragged.nrows(x), dtype=dtypes.int64), axis=1)

    def _zip(foo):
      y_val, x_val = foo
      bar = backend.tile(y_val, array_ops.shape(x_val))
      return array_ops.stack([bar, x_val], axis=1)

    output = ragged.map_fn(
        _zip, (y, x),
        dtype=ragged.RaggedTensorType(dtype=dtypes.int64, ragged_rank=1),
        infer_shape=False)

    with self.test_session():
      result = self.evaluate(output).tolist()
      self.assertAllEqual(
          result, [[[0, 10], [0, 20]], [[1, 30], [1, 40]], [[2, 50], [2, 60]],
                   [[3, 70]], [[4, 80], [4, 90], [4, 100]]])

  def testBatchGather(self):
    tokens = ragged.constant([['hello', '.', 'there'], ['merhaba'],
                              ['bonjour', '.', 'ca va', '?']])
    indices = ragged.constant([[0, 2], [0], [0, 2]])

    def gather(x):
      tokens_val, indices_val = x
      return array_ops.gather(tokens_val, indices_val)

    data = tokens, indices
    out = ragged.map_fn(
        gather,
        data,
        dtype=ragged.RaggedTensorType(dtype=dtypes.string, ragged_rank=1),
        infer_shape=False)

    with self.test_session():
      self.assertAllEqual(
          self.evaluate(out).tolist(),
          [[b'hello', b'there'], [b'merhaba'], [b'bonjour', b'ca va']])

  def testMismatchRaggedRank(self):
    elems = ragged.constant([[[1, 2, 3]], [[4, 5], [6, 7]]])
    fn = lambda x: ragged.reduce_sum(x, axis=0)
    with self.assertRaisesWithLiteralMatch(
        ValueError, r'The declared ragged rank (23) mismatches the result (1)'):
      _ = ragged.map_fn(
          fn,
          elems,
          dtype=ragged.RaggedTensorType(dtype=dtypes.int64, ragged_rank=23))

  def testMismatchRaggedRank2(self):
    elems = ragged.constant([[1, 2, 3], [4, 5], [6, 7]])
    fn = lambda x: ragged.from_row_starts(x, [0])
    with self.assertRaisesWithLiteralMatch(
        ValueError, r'The declared ragged rank (10) mismatches the result (1)'):
      _ = ragged.map_fn(
          fn,
          elems,
          dtype=ragged.RaggedTensorType(dtype=dtypes.int64, ragged_rank=10))