def _testNumThreadsHelper(self, num_threads, override_threadpool_fn):
def get_thread_id(_):
# Python creates a dummy thread object to represent the current
# thread when called from an "alien" thread (such as a
# `PrivateThreadPool` thread in this case). It does not include
# the TensorFlow-given display name, but it has a unique
# identifier that maps one-to-one with the underlying OS thread.
return np.array(threading.current_thread().ident).astype(np.int64)
dataset = (
dataset_ops.Dataset.range(1000).map(
lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
num_parallel_calls=32).apply(unique.unique()))
dataset = override_threadpool_fn(dataset)
next_element = self.getNext(dataset, requires_initialization=True)
thread_ids = []
try:
while True:
thread_ids.append(self.evaluate(next_element()))
except errors.OutOfRangeError:
pass
self.assertLen(thread_ids, len(set(thread_ids)))
self.assertNotEmpty(thread_ids)
if num_threads:
# NOTE(mrry): We don't control the thread pool scheduling, and
# so cannot guarantee that all of the threads in the pool will
# perform work.
self.assertLessEqual(len(thread_ids), num_threads)
def testOverrideThreadPool(self):
def get_thread_id(_):
# Python creates a dummy thread object to represent the current
# thread when called from an "alien" thread (such as a
# `PrivateThreadPool` thread in this case). It does not include
# the TensorFlow-given display name, but it has a unique
# identifier that maps one-to-one with the underlying OS thread.
return np.array(threading.current_thread().ident).astype(np.int64)
for num_threads in [1, 2, 4, 8, 16]:
dataset = (Dataset.range(1000).map(
lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
num_parallel_calls=32).apply(unique.unique()))
dataset = threadpool.override_threadpool(
dataset,
threadpool.PrivateThreadPool(
num_threads,
display_name='private_thread_pool_%d' % num_threads))
thread_ids = []
for next_element in datasets.Iterator(dataset):
thread_ids.append(next_element)
self.assertEqual(len(thread_ids), len(set(thread_ids)))
self.assertGreater(len(thread_ids), 0)
# NOTE(mrry): We don't control the thread pool scheduling, and
# so cannot guarantee that all of the threads in the pool will
# perform work.
self.assertLessEqual(len(thread_ids), num_threads)
def testOverrideThreadPool(self):
def get_thread_id(_):
# Python creates a dummy thread object to represent the current
# thread when called from an "alien" thread (such as a
# `PrivateThreadPool` thread in this case). It does not include
# the TensorFlow-given display name, but it has a unique
# identifier that maps one-to-one with the underlying OS thread.
return np.array(threading.current_thread().ident).astype(np.int64)
for num_threads in [1, 2, 4, 8, 16]:
dataset = (
Dataset.range(1000).map(
lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
num_parallel_calls=32).apply(unique.unique()))
dataset = threadpool.override_threadpool(
dataset,
threadpool.PrivateThreadPool(
num_threads, display_name='private_thread_pool_%d' % num_threads))
thread_ids = []
for next_element in datasets.Iterator(dataset):
thread_ids.append(next_element)
self.assertEqual(len(thread_ids), len(set(thread_ids)))
self.assertGreater(len(thread_ids), 0)
# NOTE(mrry): We don't control the thread pool scheduling, and
# so cannot guarantee that all of the threads in the pool will
# perform work.
self.assertLessEqual(len(thread_ids), num_threads)
def _testSimpleHelper(self, dtype, test_cases):
"""Test the `unique()` transformation on a list of test cases.
Args:
dtype: The `dtype` of the elements in each test case.
test_cases: A list of pairs of lists. The first component is the test
input that will be passed to the transformation; the second component
is the expected sequence of outputs from the transformation.
"""
# The `current_test_case` will be updated when we loop over `test_cases`
# below; declare it here so that the generator can capture it once.
current_test_case = []
dataset = dataset_ops.Dataset.from_generator(lambda: current_test_case,
dtype).apply(unique.unique())
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
for test_case, expected in test_cases:
current_test_case = test_case
self.evaluate(iterator.initializer)
for element in expected:
if dtype == dtypes.string:
element = compat.as_bytes(element)
self.assertAllEqual(element, self.evaluate(next_element))
with self.assertRaises(errors.OutOfRangeError):
sess.run(next_element)
def _testSimpleHelper(self, dtype, test_cases):
"""Test the `unique()` transformation on a list of test cases.
Args:
dtype: The `dtype` of the elements in each test case.
test_cases: A list of pairs of lists. The first component is the test
input that will be passed to the transformation; the second component
is the expected sequence of outputs from the transformation.
"""
# The `current_test_case` will be updated when we loop over `test_cases`
# below; declare it here so that the generator can capture it once.
current_test_case = []
dataset = dataset_ops.Dataset.from_generator(
lambda: current_test_case, dtype).apply(unique.unique())
iterator = dataset_ops.make_initializable_iterator(dataset)
next_element = iterator.get_next()
with self.cached_session() as sess:
for test_case, expected in test_cases:
current_test_case = test_case
self.evaluate(iterator.initializer)
for element in expected:
if dtype == dtypes.string:
element = compat.as_bytes(element)
self.assertAllEqual(element, self.evaluate(next_element))
with self.assertRaises(errors.OutOfRangeError):
self.evaluate(next_element)
def testUnsupportedOpInPipeline(self):
dataset = dataset_ops.Dataset.list_files(self.test_filenames)
dataset = dataset.flat_map(core_readers.TFRecordDataset)
dataset = dataset.batch(5)
dataset = dataset.apply(unique.unique())
with self.assertRaises(errors.NotFoundError):
dataset = distribute._AutoShardDataset(dataset, 2, 0)
self.evaluate(self.getNext(dataset)())
def testUnsupportedOpInPipeline(self):
dataset = dataset_ops.Dataset.list_files(self.test_filenames)
dataset = dataset.flat_map(core_readers.TFRecordDataset)
dataset = dataset.batch(5)
dataset = dataset.apply(unique.unique())
with self.assertRaises(errors.NotFoundError):
dataset = distribute._AutoShardDataset(dataset, 2, 0)
self.evaluate(self.getNext(dataset)())
def testUnsupportedTypes(self):
"""Should raise TypeError when element type doesn't match with the
dtypes.int64, dtypes.int32 or dtypes.string (supported types).
"""
for dtype in [
dtypes.bool, dtypes.double, dtypes.complex64, dtypes.float32,
dtypes.float64, dtypes.qint16, dtypes.qint32
]:
with self.assertRaises(TypeError):
_ = dataset_ops.Dataset.from_generator(
lambda: [], dtype).apply(unique.unique())
def testUnknownOpInPipelineStillShardsAtTheEnd(self):
dataset = dataset_ops.Dataset.list_files(self.test_filenames, shuffle=False)
dataset = dataset.flat_map(core_readers.TFRecordDataset)
dataset = dataset.apply(unique.unique())
dataset = distribute._AutoShardDataset(dataset, 5, 0)
expected = [
b"Record %d of file %d" % (r, f) # pylint:disable=g-complex-comprehension
for f in range(0, 10)
for r in (0, 5)
]
self.assertDatasetProduces(dataset, expected)
def testUnknownOpInPipelineStillShardsAtTheEnd(self):
dataset = dataset_ops.Dataset.list_files(self._filenames,
shuffle=False)
dataset = dataset.flat_map(core_readers.TFRecordDataset)
dataset = dataset.apply(unique.unique())
dataset = distribute._AutoShardDataset(dataset, 5, 0)
expected = [
b"Record %d of file %d" % (r, f) # pylint:disable=g-complex-comprehension
for f in range(0, 10) for r in (0, 5)
]
self.assertDatasetProduces(dataset, expected)
def unique():
"""Creates a `Dataset` from another `Dataset`, discarding duplicates.
Use this transformation to produce a dataset that contains one instance of
each unique element in the input. For example:
```python
dataset = tf.data.Dataset.from_tensor_slices([1, 37, 2, 37, 2, 1])
# Using `unique()` will drop the duplicate elements.
dataset = dataset.apply(tf.data.experimental.unique()) # ==> { 1, 37, 2 }
```
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
return experimental_unique.unique()
def unique():
"""Creates a `Dataset` from another `Dataset`, discarding duplicates.
Use this transformation to produce a dataset that contains one instance of
each unique element in the input. For example:
```python
dataset = tf.data.Dataset.from_tensor_slices([1, 37, 2, 37, 2, 1])
# Using `unique()` will drop the duplicate elements.
dataset = dataset.apply(tf.data.experimental.unique()) # ==> { 1, 37, 2 }
```
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
return experimental_unique.unique()
def testNumThreads(self, num_threads, max_intra_op_parallelism):
def get_thread_id(_):
# Python creates a dummy thread object to represent the current
# thread when called from an "alien" thread (such as a
# `PrivateThreadPool` thread in this case). It does not include
# the TensorFlow-given display name, but it has a unique
# identifier that maps one-to-one with the underlying OS thread.
return np.array(threading.current_thread().ident).astype(np.int64)
dataset = (
dataset_ops.Dataset.range(1000).map(
lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
num_parallel_calls=32).apply(unique.unique()))
dataset = threadpool.override_threadpool(
dataset,
threadpool.PrivateThreadPool(
num_threads,
max_intra_op_parallelism=max_intra_op_parallelism,
display_name="private_thread_pool_%d" % num_threads))
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
sess.run(iterator.initializer)
thread_ids = []
try:
while True:
thread_ids.append(sess.run(next_element))
except errors.OutOfRangeError:
pass
self.assertEqual(len(thread_ids), len(set(thread_ids)))
self.assertGreater(len(thread_ids), 0)
# NOTE(mrry): We don't control the thread pool scheduling, and
# so cannot guarantee that all of the threads in the pool will
# perform work.
self.assertLessEqual(len(thread_ids), num_threads)
def _testSimpleHelper(self, dtype, test_cases):
"""Test the `unique()` transformation on a list of test cases.
Args:
dtype: The `dtype` of the elements in each test case.
test_cases: A list of pairs of lists. The first component is the test
input that will be passed to the transformation; the second component
is the expected sequence of outputs from the transformation.
"""
# The `current_test_case` will be updated when we loop over `test_cases`
# below; declare it here so that the generator can capture it once.
current_test_case = []
dataset = dataset_ops.Dataset.from_generator(lambda: current_test_case,
dtype).apply(unique.unique())
for test_case, expected in test_cases:
current_test_case = test_case
self.assertDatasetProduces(dataset, [
compat.as_bytes(element) if dtype == dtypes.string else element
for element in expected
])
def _testSimpleHelper(self, dtype, test_cases):
"""Test the `unique()` transformation on a list of test cases.
Args:
dtype: The `dtype` of the elements in each test case.
test_cases: A list of pairs of lists. The first component is the test
input that will be passed to the transformation; the second component
is the expected sequence of outputs from the transformation.
"""
# The `current_test_case` will be updated when we loop over `test_cases`
# below; declare it here so that the generator can capture it once.
current_test_case = []
dataset = dataset_ops.Dataset.from_generator(
lambda: current_test_case, dtype).apply(unique.unique())
for test_case, expected in test_cases:
current_test_case = test_case
self.assertDatasetProduces(dataset, [
compat.as_bytes(element) if dtype == dtypes.string else element
for element in expected
])
def testNumThreads(self, num_threads, max_intra_op_parallelism):
def get_thread_id(_):
# Python creates a dummy thread object to represent the current
# thread when called from an "alien" thread (such as a
# `PrivateThreadPool` thread in this case). It does not include
# the TensorFlow-given display name, but it has a unique
# identifier that maps one-to-one with the underlying OS thread.
return np.array(threading.current_thread().ident).astype(np.int64)
dataset = (
dataset_ops.Dataset.range(1000).map(
lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
num_parallel_calls=32).apply(unique.unique()))
dataset = threadpool.override_threadpool(
dataset,
threadpool.PrivateThreadPool(
num_threads,
max_intra_op_parallelism=max_intra_op_parallelism,
display_name="private_thread_pool_%d" % num_threads))
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
with self.cached_session() as sess:
self.evaluate(iterator.initializer)
thread_ids = []
try:
while True:
thread_ids.append(sess.run(next_element))
except errors.OutOfRangeError:
pass
self.assertEqual(len(thread_ids), len(set(thread_ids)))
self.assertGreater(len(thread_ids), 0)
# NOTE(mrry): We don't control the thread pool scheduling, and
# so cannot guarantee that all of the threads in the pool will
# perform work.
self.assertLessEqual(len(thread_ids), num_threads)
def build_dataset(num_elements, unique_elem_range):
return dataset_ops.Dataset.range(num_elements).map(
lambda x: x % unique_elem_range).apply(unique.unique())
def build_dataset(num_elements, unique_elem_range):
return dataset_ops.Dataset.range(num_elements).map(
lambda x: x % unique_elem_range).apply(unique.unique())
