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# 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.
# ==============================================================================
"""Iteration over tf.data.Datasets when eager execution is enabled."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import threading
from tensorflow.contrib.data.python.ops import prefetching_ops
from tensorflow.python.data.ops import iterator_ops
from tensorflow.python.data.util import nest
from tensorflow.python.data.util import sparse
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_dataset_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.training.checkpointable import base as checkpointable
from tensorflow.python.training.saver import BaseSaverBuilder
_uid_counter = 0
_uid_lock = threading.Lock()
def _generate_shared_name(prefix):
with _uid_lock:
global _uid_counter
uid = _uid_counter
_uid_counter += 1
return "{}{}".format(prefix, uid)
class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase):
"""An iterator producing tf.Tensor objects from a tf.data.Dataset.
NOTE: Unlike the iterator created by the
@{tf.data.Dataset.make_one_shot_iterator} method, this class enables
additional experimental functionality, such as prefetching to the GPU.
"""
def __init__(self, dataset):
"""Creates a new iterator over the given dataset.
For example:
```python
dataset = tf.data.Dataset.range(4)
for x in Iterator(dataset):
print(x)
```
Tensors produced will be placed on the device on which this iterator object
was created.
Args:
dataset: A `tf.data.Dataset` object.
Raises:
TypeError: If `dataset` is an unsupported type.
RuntimeError: When invoked without eager execution enabled.
"""
if isinstance(dataset, prefetching_ops._PrefetchToDeviceDataset): # pylint: disable=protected-access
raise TypeError(
"`tf.contrib.data.prefetch_to_device()` is not compatible with "
"`tf.contrib.eager.Iterator`. Use `for ... in dataset:` to iterate "
"over the dataset instead.")
super(Iterator, self).__init__(dataset)
if not context.context().device_spec.device_type:
is_remote_device = False
else:
is_remote_device = context.context().device_spec.device_type != "CPU"
self._buffer_resource_handle = None
if is_remote_device:
with ops.device("/device:CPU:0"):
iter_string_handle = gen_dataset_ops.iterator_to_string_handle(
self._resource)
@function.Defun(dtypes.string)
def remote_fn(h):
remote_iterator = iterator_ops.Iterator.from_string_handle(
h, self.output_types, self.output_shapes, self.output_classes)
return remote_iterator.get_next()
remote_fn.add_to_graph(None)
target = constant_op.constant("/device:CPU:0")
with ops.device(self._device):
self._buffer_resource_handle = prefetching_ops.function_buffering_resource( # pylint: disable=line-too-long
string_arg=iter_string_handle,
f=remote_fn,
target_device=target,
buffer_size=10,
container="",
shared_name=_generate_shared_name(
"contrib_eager_iterator_function_buffer_resource"))
self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long
handle=self._buffer_resource_handle,
handle_device=self._device)
def _next_internal(self):
"""Returns a nested structure of `tf.Tensor`s containing the next element.
"""
# This runs in sync mode as iterators use an error status to communicate
# that there is no more data to iterate over.
# TODO(b/77291417): Fix
with context.execution_mode(context.SYNC):
if self._buffer_resource_handle is not None:
with ops.device(self._device):
ret = prefetching_ops.function_buffering_resource_get_next(
function_buffer_resource=self._buffer_resource_handle,
output_types=self._flat_output_types)
return sparse.deserialize_sparse_tensors(
nest.pack_sequence_as(self._output_types, ret), self._output_types,
self._output_shapes, self._output_classes)
else:
return super(Iterator, self)._next_internal()
# TODO(shivaniagrawal): Expose checkpointable stateful objects from dataset
# attributes(potential).
class _Saveable(BaseSaverBuilder.SaveableObject):
"""SaveableObject for saving/restoring iterator state."""
def __init__(self, iterator_resource, name):
serialized_iterator = gen_dataset_ops.serialize_iterator(
iterator_resource)
specs = [
BaseSaverBuilder.SaveSpec(serialized_iterator, "", name + "_STATE")
]
# pylint: disable=protected-access
super(Iterator._Saveable, self).__init__(iterator_resource, specs, name)
def restore(self, restored_tensors, restored_shapes):
with ops.colocate_with(self.op):
return gen_dataset_ops.deserialize_iterator(self.op,
restored_tensors[0])
def _gather_saveables_for_checkpoint(self):
def _saveable_factory(name):
return self._Saveable(self._resource, name)
return {"ITERATOR": _saveable_factory}
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