+++ /dev/null
-# 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 os
-import numpy as np
-
-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.ops import iterator_ops
-from tensorflow.python.data.ops import readers
-from tensorflow.python.framework import constant_op
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import errors
-from tensorflow.python.framework import function
-from tensorflow.python.framework import ops
-from tensorflow.python.framework import test_util
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import functional_ops
-from tensorflow.python.ops import gen_dataset_ops
-from tensorflow.python.ops import gradients_impl
-from tensorflow.python.ops import io_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import parsing_ops
-from tensorflow.python.ops import script_ops
-from tensorflow.python.platform import test
-from tensorflow.python.training import server_lib
-
-
-class IteratorTest(test.TestCase):
-
- def testAttemptingGradientsRaiseExceptions(self):
- component = constant_op.constant([1])
- side = constant_op.constant(0)
- add = lambda x: x + side
- dataset = dataset_ops.Dataset.from_tensor_slices(component).map(add)
- value = dataset.make_one_shot_iterator().get_next()
- with self.assertRaisesRegexp(LookupError, "No gradient defined"):
- gradients_impl.gradients(value, component)
- with self.assertRaisesRegexp(LookupError, "No gradient defined"):
- gradients_impl.gradients(value, side)
- with self.assertRaisesRegexp(LookupError, "No gradient defined"):
- gradients_impl.gradients(value, [component, side])
-
- def testOneShotIterator(self):
- components = (np.arange(7),
- np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
- np.array(37.0) * np.arange(7))
-
- def _map_fn(x, y, z):
- return math_ops.square(x), math_ops.square(y), math_ops.square(z)
-
- iterator = (dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn)
- .repeat(14).make_one_shot_iterator())
- 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:
- for _ in range(14):
- for i in range(7):
- result = sess.run(get_next)
- for component, result_component in zip(components, result):
- self.assertAllEqual(component[i]**2, result_component)
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(get_next)
-
- def testOneShotIteratorCaptureByValue(self):
- components = (np.arange(7),
- np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
- np.array(37.0) * np.arange(7))
- tensor_components = tuple([ops.convert_to_tensor(c) for c in components])
-
- def _map_fn(x, y, z):
- return math_ops.square(x), math_ops.square(y), math_ops.square(z)
-
- iterator = (dataset_ops.Dataset.from_tensor_slices(tensor_components)
- .map(_map_fn).repeat(14).make_one_shot_iterator())
- 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:
- for _ in range(14):
- for i in range(7):
- result = sess.run(get_next)
- for component, result_component in zip(components, result):
- self.assertAllEqual(component[i]**2, result_component)
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(get_next)
-
- def testOneShotIteratorInsideContainer(self):
- components = (np.arange(7),
- np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
- np.array(37.0) * np.arange(7))
-
- def within_container():
- def _map_fn(x, y, z):
- return math_ops.square(x), math_ops.square(y), math_ops.square(z)
- iterator = (dataset_ops.Dataset.from_tensor_slices(components)
- .map(_map_fn).repeat(14).make_one_shot_iterator())
- return iterator.get_next()
-
- server = server_lib.Server.create_local_server()
-
- # Create two iterators within unique containers, and run them to
- # make sure that the resources aren't shared.
- #
- # The test below would fail if cname were the same across both
- # sessions.
- for i in range(2):
- with session.Session(server.target) as sess:
- cname = "iteration%d" % i
- with ops.container(cname):
- get_next = within_container()
-
- for _ in range(14):
- for i in range(7):
- result = sess.run(get_next)
- for component, result_component in zip(components, result):
- self.assertAllEqual(component[i]**2, result_component)
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(get_next)
-
- def testOneShotIteratorNonBlocking(self):
- dataset = dataset_ops.Dataset.from_tensors([1, 2, 3]).map(lambda x: x * x)
- iterator = dataset.make_one_shot_iterator()
- next_element = iterator.get_next()
-
- # Create a session with a single thread to ensure that the
- # one-shot iterator initializer does not deadlock.
- config = config_pb2.ConfigProto(inter_op_parallelism_threads=1,
- use_per_session_threads=True)
- with session.Session(config=config) as sess:
- self.assertAllEqual([1, 4, 9], sess.run(next_element))
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(next_element)
-
- # Test with multiple threads invoking the one-shot iterator concurrently.
- with session.Session(config=config) as sess:
- results = []
- def consumer_thread():
- try:
- results.append(sess.run(next_element))
- except errors.OutOfRangeError:
- results.append(None)
-
- num_threads = 8
- threads = [
- self.checkedThread(consumer_thread) for _ in range(num_threads)]
- for t in threads:
- t.start()
- for t in threads:
- t.join()
-
- self.assertEqual(num_threads, len(results))
- self.assertEqual(num_threads - 1,
- len([None for r in results if r is None]))
- self.assertAllEqual([[1, 4, 9]], [r for r in results if r is not None])
-
- def testOneShotIteratorInitializerFails(self):
- # Define a dataset whose initialization will always fail.
- dataset = dataset_ops.Dataset.from_tensors(
- array_ops.check_numerics(
- constant_op.constant(1.0) / constant_op.constant(0.0), "oops"))
- iterator = dataset.make_one_shot_iterator()
- next_element = iterator.get_next()
-
- with self.test_session() as sess:
- with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
- sess.run(next_element)
-
- # Test that subsequent attempts to use the iterator also fail.
- with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
- sess.run(next_element)
-
- with self.test_session() as sess:
- def consumer_thread():
- with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
- sess.run(next_element)
-
- num_threads = 8
- threads = [
- self.checkedThread(consumer_thread) for _ in range(num_threads)]
- for t in threads:
- t.start()
- for t in threads:
- t.join()
-
- def testSimpleSharedResource(self):
- components = (
- np.array(1, dtype=np.int64),
- np.array([1, 2, 3], dtype=np.int64),
- np.array(37.0, dtype=np.float64)
- )
-
- server = server_lib.Server.create_local_server()
-
- # Create two non-overlapping sessions that share the same iterator
- # resource on the same server, and verify that an action of the
- # first session (initializing the iterator) is visible in the
- # second session.
- with ops.Graph().as_default():
- iterator = (dataset_ops.Dataset.from_tensors(components)
- .map(lambda x, y, z: (x, y, z)).make_initializable_iterator(
- shared_name="shared_iterator"))
- init_op = iterator.initializer
- get_next = iterator.get_next()
-
- with session.Session(server.target) 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)
-
- # Re-initialize the iterator in the first session.
- sess.run(init_op)
-
- with ops.Graph().as_default():
- # Re-define the iterator manually, without defining any of the
- # functions in this graph, to ensure that we are not
- # accidentally redefining functions with the same names in the
- # new graph.
- iterator = iterator_ops.Iterator.from_structure(
- shared_name="shared_iterator",
- output_types=(dtypes.int64, dtypes.int64, dtypes.float64),
- output_shapes=([], [3], []))
- get_next = iterator.get_next()
-
- with session.Session(server.target) as sess:
- # Use the iterator without re-initializing in the second session.
- 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 testNotInitializedError(self):
- components = (np.array(1), np.array([1, 2, 3]), np.array(37.0))
- iterator = (dataset_ops.Dataset.from_tensors(components)
- .make_initializable_iterator())
- get_next = iterator.get_next()
-
- with self.test_session() as sess:
- with self.assertRaisesRegexp(errors.FailedPreconditionError,
- "iterator has not been initialized"):
- sess.run(get_next)
-
- def testReinitializableIterator(self):
- dataset_3 = dataset_ops.Dataset.from_tensors(
- constant_op.constant([1, 2, 3]))
- dataset_4 = dataset_ops.Dataset.from_tensors(
- constant_op.constant([4, 5, 6, 7]))
- iterator = iterator_ops.Iterator.from_structure(dataset_3.output_types,
- [None])
-
- dataset_3_init_op = iterator.make_initializer(dataset_3)
- dataset_4_init_op = iterator.make_initializer(dataset_4)
- get_next = iterator.get_next()
-
- self.assertEqual(dataset_3.output_types, iterator.output_types)
- self.assertEqual(dataset_4.output_types, iterator.output_types)
- self.assertEqual([None], iterator.output_shapes.as_list())
-
- with self.test_session() as sess:
- # The iterator is initially uninitialized.
- with self.assertRaises(errors.FailedPreconditionError):
- sess.run(get_next)
-
- # Initialize with one dataset.
- sess.run(dataset_3_init_op)
- self.assertAllEqual([1, 2, 3], sess.run(get_next))
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(get_next)
-
- # Initialize with a different dataset.
- sess.run(dataset_4_init_op)
- self.assertAllEqual([4, 5, 6, 7], sess.run(get_next))
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(get_next)
-
- # Reinitialize with the first dataset.
- sess.run(dataset_3_init_op)
- self.assertAllEqual([1, 2, 3], sess.run(get_next))
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(get_next)
-
- def testReinitializableIteratorStaticErrors(self):
- # Non-matching structure for types and shapes.
- with self.assertRaises(TypeError):
- iterator = iterator_ops.Iterator.from_structure((dtypes.int64,
- dtypes.float64), [None])
-
- # Test validation of dataset argument.
- iterator = iterator_ops.Iterator.from_structure((dtypes.int64,
- dtypes.float64))
-
- # Incompatible structure.
- with self.assertRaises(ValueError):
- iterator.make_initializer(
- dataset_ops.Dataset.from_tensors(((constant_op.constant(
- [1, 2, 3], dtype=dtypes.int64),), (constant_op.constant(
- [4., 5., 6., 7.], dtype=dtypes.float64),))))
-
- # Incompatible types.
- with self.assertRaises(TypeError):
- iterator.make_initializer(
- dataset_ops.Dataset.from_tensors((constant_op.constant(
- [1, 2, 3], dtype=dtypes.int32), constant_op.constant(
- [4., 5., 6., 7.], dtype=dtypes.float32))))
-
- # Incompatible shapes.
- iterator = iterator_ops.Iterator.from_structure(
- (dtypes.int64, dtypes.float64), ([None], []))
- with self.assertRaises(TypeError):
- iterator.make_initializer(
- dataset_ops.Dataset.from_tensors((constant_op.constant(
- [1, 2, 3], dtype=dtypes.int64), constant_op.constant(
- [4., 5., 6., 7.], dtype=dtypes.float64))))
-
- def testIteratorStringHandle(self):
- dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
- dataset_4 = dataset_ops.Dataset.from_tensor_slices([10, 20, 30, 40])
-
- iterator_3 = dataset_3.make_one_shot_iterator()
- iterator_4 = dataset_4.make_one_shot_iterator()
-
- handle_placeholder = array_ops.placeholder(dtypes.string, shape=[])
- feedable_iterator = iterator_ops.Iterator.from_string_handle(
- handle_placeholder, dataset_3.output_types, dataset_3.output_shapes)
- next_element = feedable_iterator.get_next()
-
- self.assertEqual(dataset_3.output_types, feedable_iterator.output_types)
- self.assertEqual(dataset_4.output_types, feedable_iterator.output_types)
- self.assertEqual([], feedable_iterator.output_shapes)
-
- with self.test_session() as sess:
- iterator_3_handle = sess.run(iterator_3.string_handle())
- iterator_4_handle = sess.run(iterator_4.string_handle())
-
- self.assertEqual(
- 10, sess.run(next_element,
- feed_dict={handle_placeholder: iterator_4_handle}))
- self.assertEqual(
- 1, sess.run(next_element,
- feed_dict={handle_placeholder: iterator_3_handle}))
- self.assertEqual(
- 20, sess.run(next_element,
- feed_dict={handle_placeholder: iterator_4_handle}))
- self.assertEqual(
- 2, sess.run(next_element,
- feed_dict={handle_placeholder: iterator_3_handle}))
- self.assertEqual(
- 30, sess.run(next_element,
- feed_dict={handle_placeholder: iterator_4_handle}))
- self.assertEqual(
- 3, sess.run(next_element,
- feed_dict={handle_placeholder: iterator_3_handle}))
- self.assertEqual(
- 40, sess.run(next_element,
- feed_dict={handle_placeholder: iterator_4_handle}))
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(next_element,
- feed_dict={handle_placeholder: iterator_3_handle})
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(next_element,
- feed_dict={handle_placeholder: iterator_4_handle})
-
- def testIteratorStringHandleError(self):
- dataset_int_scalar = (dataset_ops.Dataset.from_tensor_slices([1, 2,
- 3]).repeat())
- dataset_float_vector = (dataset_ops.Dataset.from_tensors([1.0, 2.0, 3.0]))
-
- handle_placeholder = array_ops.placeholder(dtypes.string, shape=[])
-
- feedable_int_scalar = iterator_ops.Iterator.from_string_handle(
- handle_placeholder, dtypes.int32, [])
- feedable_int_vector = iterator_ops.Iterator.from_string_handle(
- handle_placeholder, dtypes.int32, [None])
- feedable_int_any = iterator_ops.Iterator.from_string_handle(
- handle_placeholder, dtypes.int32)
-
- with self.test_session() as sess:
- handle_int_scalar = sess.run(
- dataset_int_scalar.make_one_shot_iterator().string_handle())
- handle_float_vector = sess.run(
- dataset_float_vector.make_one_shot_iterator().string_handle())
-
- self.assertEqual(1,
- sess.run(
- feedable_int_scalar.get_next(),
- feed_dict={handle_placeholder: handle_int_scalar}))
-
- self.assertEqual(2,
- sess.run(
- feedable_int_any.get_next(),
- feed_dict={handle_placeholder: handle_int_scalar}))
-
- with self.assertRaises(errors.InvalidArgumentError):
- print(sess.run(
- feedable_int_vector.get_next(),
- feed_dict={handle_placeholder: handle_int_scalar}))
-
- with self.assertRaises(errors.InvalidArgumentError):
- print(sess.run(
- feedable_int_vector.get_next(),
- feed_dict={handle_placeholder: handle_float_vector}))
-
- def testRemoteIteratorUsingRemoteCallOpDirectSession(self):
- worker_config = config_pb2.ConfigProto()
- worker_config.device_count["CPU"] = 3
-
- with ops.device("/job:localhost/replica:0/task:0/cpu:1"):
- dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
- iterator_3 = dataset_3.make_one_shot_iterator()
- iterator_3_handle = iterator_3.string_handle()
-
- @function.Defun(dtypes.string)
- def _remote_fn(h):
- remote_iterator = iterator_ops.Iterator.from_string_handle(
- h, dataset_3.output_types, dataset_3.output_shapes)
- return remote_iterator.get_next()
-
- with ops.device("/job:localhost/replica:0/task:0/cpu:0"):
- target_placeholder = array_ops.placeholder(dtypes.string, shape=[])
- remote_op = functional_ops.remote_call(
- args=[iterator_3_handle],
- Tout=[dtypes.int32],
- f=_remote_fn,
- target=target_placeholder)
-
- with self.test_session(config=worker_config) as sess:
- elem = sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:1"
- })
- self.assertEqual(elem, [1])
- # Fails when target is cpu:2 where the resource is not located.
- with self.assertRaises(errors.InvalidArgumentError):
- sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:2"
- })
- elem = sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:1"
- })
- self.assertEqual(elem, [2])
- elem = sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:1"
- })
- self.assertEqual(elem, [3])
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:1"
- })
-
- def testRemoteIteratorUsingRemoteCallOpDirectSessionGPUCPU(self):
- if not test_util.is_gpu_available():
- self.skipTest("No GPU available")
-
- with ops.device("/job:localhost/replica:0/task:0/cpu:0"):
- dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
- iterator_3 = dataset_3.make_one_shot_iterator()
- iterator_3_handle = iterator_3.string_handle()
-
- def _encode_raw(byte_array):
- return bytes(bytearray(byte_array))
-
- @function.Defun(dtypes.uint8)
- def _remote_fn(h):
- handle = script_ops.py_func(_encode_raw, [h], dtypes.string)
- remote_iterator = iterator_ops.Iterator.from_string_handle(
- handle, dataset_3.output_types, dataset_3.output_shapes)
- return remote_iterator.get_next()
-
- with ops.device("/job:localhost/replica:0/task:0/device:GPU:0"):
- target_placeholder = array_ops.placeholder(dtypes.string, shape=[])
- iterator_3_handle_uint8 = parsing_ops.decode_raw(
- bytes=iterator_3_handle, out_type=dtypes.uint8)
- remote_op = functional_ops.remote_call(
- args=[iterator_3_handle_uint8],
- Tout=[dtypes.int32],
- f=_remote_fn,
- target=target_placeholder)
-
- with self.test_session() as sess:
- elem = sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:0"
- })
- self.assertEqual(elem, [1])
- elem = sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:0"
- })
- self.assertEqual(elem, [2])
- elem = sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:0"
- })
- self.assertEqual(elem, [3])
- with self.assertRaises(errors.OutOfRangeError):
- sess.run(
- remote_op,
- feed_dict={
- target_placeholder: "/job:localhost/replica:0/task:0/cpu:0"
- })
-
- def testIncorrectIteratorRestore(self):
-
- def _path():
- return os.path.join(self.get_temp_dir(), "iterator")
-
- def _save_op(iterator_resource):
- iterator_state_variant = gen_dataset_ops.serialize_iterator(
- iterator_resource)
- save_op = io_ops.write_file(
- _path(), parsing_ops.serialize_tensor(iterator_state_variant))
- return save_op
-
- def _restore_op(iterator_resource):
- iterator_state_variant = parsing_ops.parse_tensor(
- io_ops.read_file(_path()), dtypes.variant)
- restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource,
- iterator_state_variant)
- return restore_op
-
- def _build_range_dataset_graph():
- start = 1
- stop = 10
- iterator = dataset_ops.Dataset.range(start,
- stop).make_initializable_iterator()
- init_op = iterator.initializer
- get_next = iterator.get_next()
- save_op = _save_op(iterator._iterator_resource)
- restore_op = _restore_op(iterator._iterator_resource)
- return init_op, get_next, save_op, restore_op
-
- def _build_reader_dataset_graph():
- filenames = ["test"] # Does not exist but we don't care in this test.
- iterator = readers.FixedLengthRecordDataset(
- filenames, 1, 0, 0).make_initializable_iterator()
- init_op = iterator.initializer
- get_next_op = iterator.get_next()
- save_op = _save_op(iterator._iterator_resource)
- restore_op = _restore_op(iterator._iterator_resource)
- return init_op, get_next_op, save_op, restore_op
-
- # Saving iterator for RangeDataset graph.
- with ops.Graph().as_default() as g:
- init_op, _, save_op, _ = _build_range_dataset_graph()
- with self.test_session(graph=g) as sess:
- sess.run(init_op)
- sess.run(save_op)
-
- # Attempt to restore the saved iterator into an IteratorResource of
- # incompatible type. An iterator of RangeDataset has output type int64,
- # while an iterator of FixedLengthRecordDataset has output type string.
- # So an InvalidArgumentError should be raised by
- # IteratorResource::set_iterator.
- with ops.Graph().as_default() as g:
- _, _, _, restore_op = _build_reader_dataset_graph()
- with self.test_session(graph=g) as sess:
- with self.assertRaises(errors.InvalidArgumentError):
- sess.run(restore_op)
-
- def testToSingleElement(self):
- skip_value = array_ops.placeholder(dtypes.int64, shape=[])
- take_value = array_ops.placeholder_with_default(
- constant_op.constant(1, dtype=dtypes.int64), shape=[])
-
- dataset = (dataset_ops.Dataset.range(100)
- .skip(skip_value)
- .map(lambda x: x * x)
- .take(take_value))
-
- element = dataset_ops.get_single_element(dataset)
-
- with self.test_session() as sess:
- self.assertEqual(0, sess.run(element, feed_dict={skip_value: 0}))
- self.assertEqual(25, sess.run(element, feed_dict={skip_value: 5}))
- self.assertEqual(100, sess.run(element, feed_dict={skip_value: 10}))
-
- with self.assertRaisesRegexp(errors.InvalidArgumentError,
- "Dataset was empty."):
- sess.run(element, feed_dict={skip_value: 100})
-
- with self.assertRaisesRegexp(errors.InvalidArgumentError,
- "Dataset had more than one element."):
- sess.run(element, feed_dict={skip_value: 0, take_value: 2})
-
-
-if __name__ == "__main__":
- test.main()
projects / platform / upstream / tensorflow.git / commitdiff