[tf.data] Move cardinality to core API.

To migrate, replace `tf.data.experimental.cardinality(dataset)` with `dataset.cardinality()`.

PiperOrigin-RevId: 314246452
Change-Id: I6839b1519adda790b91b5197ec30bac339ca5149

tensorflow/python/data/__init__.py

@@ -24,8 +24,10 @@ from __future__ import print_function
 # pylint: disable=unused-import
 from tensorflow.python.data import experimental
 from tensorflow.python.data.ops.dataset_ops import Dataset
+from tensorflow.python.data.ops.dataset_ops import INFINITE as INFINITE_CARDINALITY
 from tensorflow.python.data.ops.dataset_ops import make_initializable_iterator
 from tensorflow.python.data.ops.dataset_ops import make_one_shot_iterator
+from tensorflow.python.data.ops.dataset_ops import UNKNOWN as UNKNOWN_CARDINALITY
 from tensorflow.python.data.ops.iterator_ops import Iterator
 from tensorflow.python.data.ops.readers import FixedLengthRecordDataset
 from tensorflow.python.data.ops.readers import TextLineDataset

tensorflow/python/data/experimental/ops/cardinality.py

@@ -32,6 +32,7 @@ tf_export("data.experimental.UNKNOWN_CARDINALITY").export_constant(
     __name__, "UNKNOWN")
 
 
+# TODO(b/157691652): Deprecate this method after migrating users to the new API.
 @tf_export("data.experimental.cardinality")
 def cardinality(dataset):
   """Returns the cardinality of `dataset`, if known.

tensorflow/python/data/kernel_tests/BUILD

@@ -46,6 +46,17 @@ tf_py_test(
     ],
 )
 
+tf_py_test(
+    name = "cardinality_test",
+    srcs = ["cardinality_test.py"],
+    deps = [
+        "//tensorflow/python/data/experimental/ops:cardinality",
+        "//tensorflow/python/data/kernel_tests:test_base",
+        "//tensorflow/python/data/ops:dataset_ops",
+        "@absl_py//absl/testing:parameterized",
+    ],
+)
+
 tf_py_test(
     name = "checkpoint_test",
     size = "medium",
@@ -385,6 +396,19 @@ cuda_py_test(
     ],
 )
 
+tf_py_test(
+    name = "len_test",
+    size = "small",
+    srcs = ["len_test.py"],
+    deps = [
+        ":test_base",
+        "//tensorflow/python:constant_op",
+        "//tensorflow/python:framework_combinations",
+        "//tensorflow/python/data/ops:dataset_ops",
+        "@absl_py//absl/testing:parameterized",
+    ],
+)
+
 tf_py_test(
     name = "list_files_test",
     size = "small",

tensorflow/python/data/kernel_tests/cardinality_test.py

@@ -0,0 +1,174 @@
+# 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 `tf.data.Dataset.cardinality()`."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import functools
+
+from absl.testing import parameterized
+
+from tensorflow.python.data.kernel_tests import test_base
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import combinations
+from tensorflow.python.platform import test
+
+
+def _test_combinations():
+  # pylint: disable=g-long-lambda
+  cases = [
+      ("Batch1",
+       lambda: dataset_ops.Dataset.range(5).batch(2, drop_remainder=True), 2),
+      ("Batch2",
+       lambda: dataset_ops.Dataset.range(5).batch(2, drop_remainder=False), 3),
+      ("Batch3",
+       lambda: dataset_ops.Dataset.range(5).filter(lambda _: True).batch(2),
+       dataset_ops.UNKNOWN),
+      ("Batch4", lambda: dataset_ops.Dataset.range(5).repeat().batch(2),
+       dataset_ops.INFINITE),
+      ("Cache1", lambda: dataset_ops.Dataset.range(5).cache(), 5),
+      ("Cache2", lambda: dataset_ops.Dataset.range(5).cache("foo"), 5),
+      ("Concatenate1", lambda: dataset_ops.Dataset.range(5).concatenate(
+          dataset_ops.Dataset.range(5)), 10),
+      ("Concatenate2",
+       lambda: dataset_ops.Dataset.range(5).filter(lambda _: True).concatenate(
+           dataset_ops.Dataset.range(5)), dataset_ops.UNKNOWN),
+      ("Concatenate3", lambda: dataset_ops.Dataset.range(5).repeat().
+       concatenate(dataset_ops.Dataset.range(5)), dataset_ops.INFINITE),
+      ("Concatenate4", lambda: dataset_ops.Dataset.range(5).concatenate(
+          dataset_ops.Dataset.range(5).filter(lambda _: True)),
+       dataset_ops.UNKNOWN),
+      ("Concatenate5",
+       lambda: dataset_ops.Dataset.range(5).filter(lambda _: True).concatenate(
+           dataset_ops.Dataset.range(5).filter(lambda _: True)),
+       dataset_ops.UNKNOWN),
+      ("Concatenate6", lambda: dataset_ops.Dataset.range(5).repeat().
+       concatenate(dataset_ops.Dataset.range(5).filter(lambda _: True)),
+       dataset_ops.INFINITE),
+      ("Concatenate7", lambda: dataset_ops.Dataset.range(5).concatenate(
+          dataset_ops.Dataset.range(5).repeat()), dataset_ops.INFINITE),
+      ("Concatenate8",
+       lambda: dataset_ops.Dataset.range(5).filter(lambda _: True).concatenate(
+           dataset_ops.Dataset.range(5).repeat()), dataset_ops.INFINITE),
+      ("Concatenate9",
+       lambda: dataset_ops.Dataset.range(5).repeat().concatenate(
+           dataset_ops.Dataset.range(5).repeat()), dataset_ops.INFINITE),
+      ("FlatMap", lambda: dataset_ops.Dataset.range(5).flat_map(
+          lambda _: dataset_ops.Dataset.from_tensors(0)), dataset_ops.UNKNOWN),
+      ("Filter", lambda: dataset_ops.Dataset.range(5).filter(lambda _: True),
+       dataset_ops.UNKNOWN),
+      ("FromTensors1", lambda: dataset_ops.Dataset.from_tensors(0), 1),
+      ("FromTensors2", lambda: dataset_ops.Dataset.from_tensors((0, 1)), 1),
+      ("FromTensorSlices1",
+       lambda: dataset_ops.Dataset.from_tensor_slices([0, 0, 0]), 3),
+      ("FromTensorSlices2", lambda: dataset_ops.Dataset.from_tensor_slices(
+          ([0, 0, 0], [1, 1, 1])), 3),
+      ("Interleave1", lambda: dataset_ops.Dataset.range(5).interleave(
+          lambda _: dataset_ops.Dataset.from_tensors(0), cycle_length=1),
+       dataset_ops.UNKNOWN),
+      ("Interleave2", lambda: dataset_ops.Dataset.range(5).interleave(
+          lambda _: dataset_ops.Dataset.from_tensors(0),
+          cycle_length=1,
+          num_parallel_calls=1), dataset_ops.UNKNOWN),
+      ("Map1", lambda: dataset_ops.Dataset.range(5).map(lambda x: x), 5),
+      ("Map2", lambda: dataset_ops.Dataset.range(5).map(
+          lambda x: x, num_parallel_calls=1), 5),
+      ("PaddedBatch1", lambda: dataset_ops.Dataset.range(5).padded_batch(
+          2, [], drop_remainder=True), 2),
+      ("PaddedBatch2", lambda: dataset_ops.Dataset.range(5).padded_batch(
+          2, [], drop_remainder=False), 3),
+      ("PaddedBatch3", lambda: dataset_ops.Dataset.range(5).filter(
+          lambda _: True).padded_batch(2, []), dataset_ops.UNKNOWN),
+      ("PaddedBatch4",
+       lambda: dataset_ops.Dataset.range(5).repeat().padded_batch(2, []),
+       dataset_ops.INFINITE),
+      ("Prefetch", lambda: dataset_ops.Dataset.range(5).prefetch(buffer_size=1),
+       5),
+      ("Range1", lambda: dataset_ops.Dataset.range(0), 0),
+      ("Range2", lambda: dataset_ops.Dataset.range(5), 5),
+      ("Range3", lambda: dataset_ops.Dataset.range(5, 10), 5),
+      ("Range4", lambda: dataset_ops.Dataset.range(10, 5), 0),
+      ("Range5", lambda: dataset_ops.Dataset.range(5, 10, 2), 3),
+      ("Range6", lambda: dataset_ops.Dataset.range(10, 5, -2), 3),
+      ("Repeat1", lambda: dataset_ops.Dataset.range(0).repeat(0), 0),
+      ("Repeat2", lambda: dataset_ops.Dataset.range(1).repeat(0), 0),
+      ("Repeat3", lambda: dataset_ops.Dataset.range(0).repeat(5), 0),
+      ("Repeat4", lambda: dataset_ops.Dataset.range(1).repeat(5), 5),
+      ("Repeat5", lambda: dataset_ops.Dataset.range(0).repeat(), 0),
+      ("Repeat6", lambda: dataset_ops.Dataset.range(1).repeat(),
+       dataset_ops.INFINITE),
+      ("Shuffle", lambda: dataset_ops.Dataset.range(5).shuffle(buffer_size=1),
+       5),
+      ("Shard1", lambda: dataset_ops.Dataset.range(5).shard(2, 0), 3),
+      ("Shard2", lambda: dataset_ops.Dataset.range(5).shard(8, 7), 0),
+      ("Shard3",
+       lambda: dataset_ops.Dataset.range(5).filter(lambda _: True).shard(2, 0),
+       dataset_ops.UNKNOWN),
+      ("Shard4", lambda: dataset_ops.Dataset.range(5).repeat().shard(2, 0),
+       dataset_ops.INFINITE),
+      ("Skip1", lambda: dataset_ops.Dataset.range(5).skip(2), 3),
+      ("Skip2", lambda: dataset_ops.Dataset.range(5).skip(8), 0),
+      ("Skip3",
+       lambda: dataset_ops.Dataset.range(5).filter(lambda _: True).skip(2),
+       dataset_ops.UNKNOWN),
+      ("Skip4", lambda: dataset_ops.Dataset.range(5).repeat().skip(2),
+       dataset_ops.INFINITE),
+      ("Take1", lambda: dataset_ops.Dataset.range(5).take(2), 2),
+      ("Take2", lambda: dataset_ops.Dataset.range(5).take(8), 5),
+      ("Take3",
+       lambda: dataset_ops.Dataset.range(5).filter(lambda _: True).take(2),
+       dataset_ops.UNKNOWN),
+      ("Take4", lambda: dataset_ops.Dataset.range(5).repeat().take(2), 2),
+      ("Window1", lambda: dataset_ops.Dataset.range(5).window(
+          size=2, shift=2, drop_remainder=True), 2),
+      ("Window2", lambda: dataset_ops.Dataset.range(5).window(
+          size=2, shift=2, drop_remainder=False), 3),
+      ("Zip1", lambda: dataset_ops.Dataset.zip(dataset_ops.Dataset.range(5)),
+       5),
+      ("Zip2", lambda: dataset_ops.Dataset.zip(
+          (dataset_ops.Dataset.range(5), dataset_ops.Dataset.range(3))), 3),
+      ("Zip3", lambda: dataset_ops.Dataset.zip((dataset_ops.Dataset.range(
+          5), dataset_ops.Dataset.range(3).repeat())), 5),
+      ("Zip4", lambda: dataset_ops.Dataset.zip(
+          (dataset_ops.Dataset.range(5).repeat(), dataset_ops.Dataset.range(3).
+           repeat())), dataset_ops.INFINITE),
+      ("Zip5", lambda: dataset_ops.Dataset.zip(
+          (dataset_ops.Dataset.range(5), dataset_ops.Dataset.range(3).filter(
+              lambda _: True))), dataset_ops.UNKNOWN),
+  ]
+
+  def reduce_fn(x, y):
+    name, dataset_fn, expected_result = y
+    return x + combinations.combine(
+        dataset_fn=combinations.NamedObject(name, dataset_fn),
+        expected_result=expected_result)
+
+  return functools.reduce(reduce_fn, cases, [])
+
+
+class CardinalityTest(test_base.DatasetTestBase, parameterized.TestCase):
+  """Tests for `tf.data.Dataset.cardinality()`."""
+
+  @combinations.generate(
+      combinations.times(test_base.default_test_combinations(),
+                         _test_combinations()))
+  def testCardinality(self, dataset_fn, expected_result):
+    dataset = dataset_fn()
+    self.assertEqual(self.evaluate(dataset.cardinality()), expected_result)
+
+
+if __name__ == "__main__":
+  test.main()

tensorflow/python/data/kernel_tests/len_test.py

@@ -0,0 +1,59 @@
+# Copyright 2020 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.__len__()`."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl.testing import parameterized
+
+from tensorflow.python.data.kernel_tests import test_base
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import combinations
+from tensorflow.python.platform import test
+
+
+class LenTest(test_base.DatasetTestBase, parameterized.TestCase):
+
+  @combinations.generate(test_base.eager_only_combinations())
+  def testKnown(self):
+    num_elements = 10
+    ds = dataset_ops.Dataset.range(num_elements)
+    self.assertLen(ds, 10)
+
+  @combinations.generate(test_base.eager_only_combinations())
+  def testInfinite(self):
+    num_elements = 10
+    ds = dataset_ops.Dataset.range(num_elements).repeat()
+    with self.assertRaisesRegex(TypeError, 'infinite'):
+      len(ds)
+
+  @combinations.generate(test_base.eager_only_combinations())
+  def testUnknown(self):
+    num_elements = 10
+    ds = dataset_ops.Dataset.range(num_elements).filter(lambda x: True)
+    with self.assertRaisesRegex(TypeError, 'unknown'):
+      len(ds)
+
+  @combinations.generate(test_base.graph_only_combinations())
+  def testGraphMode(self):
+    num_elements = 10
+    ds = dataset_ops.Dataset.range(num_elements)
+    with self.assertRaisesRegex(TypeError, 'not supported while tracing'):
+      len(ds)
+
+
+if __name__ == '__main__':
+  test.main()

tensorflow/python/data/ops/dataset_ops.py

@@ -94,6 +94,12 @@ ops.NotDifferentiable("ReduceDataset")
 AUTOTUNE = -1
 tf_export("data.experimental.AUTOTUNE").export_constant(__name__, "AUTOTUNE")
 
+# Constants representing infinite and unknown cardinalities.
+INFINITE = -1
+UNKNOWN = -2
+tf_export("data.INFINITE_CARDINALITY").export_constant(__name__, "INFINITE")
+tf_export("data.UNKNOWN_CARDINALITY").export_constant(__name__, "UNKNOWN")
+
 
 @tf_export("data.Dataset", v1=[])
 @six.add_metaclass(abc.ABCMeta)
@@ -410,6 +416,36 @@ class DatasetV2(tracking_base.Trackable, composite_tensor.CompositeTensor):
       raise RuntimeError("__iter__() is only supported inside of tf.function "
                          "or when eager execution is enabled.")
 
+  def __bool__(self):
+    return True  # Required as __len__ is defined
+
+  __nonzero__ = __bool__  # Python 2 backward compatibility
+
+  def __len__(self):
+    """Returns the length of the dataset if it is known and finite.
+
+    This method requires that you are running in eager mode, and that the
+    length of the dataset is known and non-infinite. When the length may be
+    unknown or infinite, or if you are running in graph mode, use
+    `tf.data.Dataset.cardinality` instead.
+
+    Returns:
+      An integer representing the length of the dataset.
+
+    Raises:
+      RuntimeError: If the dataset length is unknown or infinite, or if eager
+        execution is not enabled.
+    """
+    if not context.executing_eagerly():
+      raise TypeError("__len__() is not supported while tracing functions. "
+                      "Use `tf.data.Dataset.cardinality` instead.")
+    length = self.cardinality()
+    if length.numpy() == INFINITE:
+      raise TypeError("dataset length is infinite.")
+    if length.numpy() == UNKNOWN:
+      raise TypeError("dataset length is unknown.")
+    return length
+
   @abc.abstractproperty
   def element_spec(self):
     """The type specification of an element of this dataset.
@@ -2095,6 +2131,34 @@ name=None))
     """
     return _OptionsDataset(self, options)
 
+  def cardinality(self):
+    """Returns the cardinality of the dataset, if known.
+
+    `cardinality` may return `tf.data.INFINITE_CARDINALITY` if the dataset
+    contains an infinite number of elements or `tf.data.UNKNOWN_CARDINALITY` if
+    the analysis fails to determine the number of elements in the dataset
+    (e.g. when the dataset source is a file).
+
+    >>> dataset = tf.data.Dataset.range(42)
+    >>> print(dataset.cardinality().numpy())
+    42
+    >>> dataset = dataset.repeat()
+    >>> cardinality = dataset.cardinality()
+    >>> print((cardinality == tf.data.INFINITE_CARDINALITY).numpy())
+    True
+    >>> dataset = dataset.filter(lambda x: True)
+    >>> cardinality = dataset.cardinality()
+    >>> print((cardinality == tf.data.UNKNOWN_CARDINALITY).numpy())
+    True
+
+    Returns:
+      A scalar `tf.int64` `Tensor` representing the cardinality of the dataset.
+      If the cardinality is infinite or unknown, `cardinality` returns the
+      named constants `tf.data.INFINITE_CARDINALITY` and
+      `tf.data.UNKNOWN_CARDINALITY` respectively.
+    """
+    return ged_ops.dataset_cardinality(self._variant_tensor)
+
 
 @tf_export(v1=["data.Dataset"])
 class DatasetV1(DatasetV2):

tensorflow/tools/api/golden/v1/tensorflow.data.-dataset.pbtxt

@@ -41,6 +41,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v1/tensorflow.data.-fixed-length-record-dataset.pbtxt

@@ -43,6 +43,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v1/tensorflow.data.-t-f-record-dataset.pbtxt

@@ -43,6 +43,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v1/tensorflow.data.-text-line-dataset.pbtxt

@@ -43,6 +43,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-csv-dataset.pbtxt

@@ -43,6 +43,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-random-dataset.pbtxt

@@ -43,6 +43,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v1/tensorflow.data.experimental.-sql-dataset.pbtxt

@@ -43,6 +43,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v1/tensorflow.data.pbtxt

@@ -12,6 +12,10 @@ tf_module {
     name: "FixedLengthRecordDataset"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "INFINITE_CARDINALITY"
+    mtype: "<type \'int\'>"
+  }
   member {
     name: "Iterator"
     mtype: "<type \'type\'>"
@@ -28,6 +32,10 @@ tf_module {
     name: "TextLineDataset"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "UNKNOWN_CARDINALITY"
+    mtype: "<type \'int\'>"
+  }
   member {
     name: "experimental"
     mtype: "<type \'module\'>"

tensorflow/tools/api/golden/v2/tensorflow.data.-dataset.pbtxt

@@ -28,6 +28,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.data.-fixed-length-record-dataset.pbtxt

@@ -30,6 +30,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.data.-t-f-record-dataset.pbtxt

@@ -29,6 +29,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.data.-text-line-dataset.pbtxt

@@ -30,6 +30,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-csv-dataset.pbtxt

@@ -30,6 +30,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-random-dataset.pbtxt

@@ -30,6 +30,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.data.experimental.-sql-dataset.pbtxt

@@ -30,6 +30,10 @@ tf_class {
     name: "cache"
     argspec: "args=[\'self\', \'filename\'], varargs=None, keywords=None, defaults=[\'\'], "
   }
+  member_method {
+    name: "cardinality"
+    argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
+  }
   member_method {
     name: "concatenate"
     argspec: "args=[\'self\', \'dataset\'], varargs=None, keywords=None, defaults=None"

tensorflow/tools/api/golden/v2/tensorflow.data.pbtxt

@@ -12,6 +12,10 @@ tf_module {
     name: "FixedLengthRecordDataset"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "INFINITE_CARDINALITY"
+    mtype: "<type \'int\'>"
+  }
   member {
     name: "Options"
     mtype: "<type \'type\'>"
@@ -24,6 +28,10 @@ tf_module {
     name: "TextLineDataset"
     mtype: "<type \'type\'>"
   }
+  member {
+    name: "UNKNOWN_CARDINALITY"
+    mtype: "<type \'int\'>"
+  }
   member {
     name: "experimental"
     mtype: "<type \'module\'>"