159 lines
6.1 KiB
Python
159 lines
6.1 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Iteration over tf.data.Datasets when eager execution is enabled."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import threading
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from tensorflow.contrib.data.python.ops import prefetching_ops
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from tensorflow.python.data.ops import iterator_ops
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from tensorflow.python.data.util import nest
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from tensorflow.python.data.util import sparse
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import function
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import gen_dataset_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.training.checkpointable import base as checkpointable
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from tensorflow.python.training.saver import BaseSaverBuilder
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_uid_counter = 0
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_uid_lock = threading.Lock()
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def _generate_shared_name(prefix):
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with _uid_lock:
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global _uid_counter
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uid = _uid_counter
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_uid_counter += 1
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return "{}{}".format(prefix, uid)
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class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase):
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"""An iterator producing tf.Tensor objects from a tf.data.Dataset.
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NOTE: Unlike the iterator created by the
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@{tf.data.Dataset.make_one_shot_iterator} method, this class enables
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additional experimental functionality, such as prefetching to the GPU.
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"""
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def __init__(self, dataset):
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"""Creates a new iterator over the given dataset.
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For example:
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```python
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dataset = tf.data.Dataset.range(4)
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for x in Iterator(dataset):
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print(x)
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```
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Tensors produced will be placed on the device on which this iterator object
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was created.
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Args:
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dataset: A `tf.data.Dataset` object.
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Raises:
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TypeError: If `dataset` is an unsupported type.
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RuntimeError: When invoked without eager execution enabled.
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"""
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if isinstance(dataset, prefetching_ops._PrefetchToDeviceDataset): # pylint: disable=protected-access
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raise TypeError(
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"`tf.contrib.data.prefetch_to_device()` is not compatible with "
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"`tf.contrib.eager.Iterator`. Use `for ... in dataset:` to iterate "
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"over the dataset instead.")
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super(Iterator, self).__init__(dataset)
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if not context.context().device_spec.device_type:
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is_remote_device = False
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else:
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is_remote_device = context.context().device_spec.device_type != "CPU"
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self._buffer_resource_handle = None
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if is_remote_device:
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with ops.device("/device:CPU:0"):
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iter_string_handle = gen_dataset_ops.iterator_to_string_handle(
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self._resource)
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@function.Defun(dtypes.string)
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def remote_fn(h):
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remote_iterator = iterator_ops.Iterator.from_string_handle(
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h, self.output_types, self.output_shapes, self.output_classes)
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return remote_iterator.get_next()
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remote_fn.add_to_graph(None)
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target = constant_op.constant("/device:CPU:0")
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with ops.device(self._device):
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self._buffer_resource_handle = prefetching_ops.function_buffering_resource( # pylint: disable=line-too-long
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string_arg=iter_string_handle,
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output_types=self._flat_output_types,
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f=remote_fn,
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target_device=target,
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buffer_size=10,
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container="",
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shared_name=_generate_shared_name(
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"contrib_eager_iterator_function_buffer_resource"))
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self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long
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handle=self._buffer_resource_handle,
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handle_device=self._device)
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def _next_internal(self):
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"""Returns a nested structure of `tf.Tensor`s containing the next element.
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"""
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# This runs in sync mode as iterators use an error status to communicate
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# that there is no more data to iterate over.
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# TODO(b/77291417): Fix
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with context.execution_mode(context.SYNC):
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if self._buffer_resource_handle is not None:
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with ops.device(self._device):
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ret = prefetching_ops.function_buffering_resource_get_next(
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function_buffer_resource=self._buffer_resource_handle,
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output_types=self._flat_output_types)
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return sparse.deserialize_sparse_tensors(
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nest.pack_sequence_as(self._output_types, ret), self._output_types,
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self._output_shapes, self._output_classes)
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else:
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return super(Iterator, self)._next_internal()
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# TODO(shivaniagrawal): Expose checkpointable stateful objects from dataset
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# attributes(potential).
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class _Saveable(BaseSaverBuilder.SaveableObject):
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"""SaveableObject for saving/restoring iterator state."""
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def __init__(self, iterator_resource, name):
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serialized_iterator = gen_dataset_ops.serialize_iterator(
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iterator_resource)
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specs = [
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BaseSaverBuilder.SaveSpec(serialized_iterator, "", name + "_STATE")
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]
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# pylint: disable=protected-access
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super(Iterator._Saveable, self).__init__(iterator_resource, specs, name)
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def restore(self, restored_tensors, restored_shapes):
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with ops.colocate_with(self.op):
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return gen_dataset_ops.deserialize_iterator(self.op,
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restored_tensors[0])
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def _gather_saveables_for_checkpoint(self):
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def _saveable_factory(name):
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return self._Saveable(self._resource, name)
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return {"ITERATOR": _saveable_factory}
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