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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.
# ==============================================================================
"""Python wrappers for Datasets and Iterators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import nest
from tensorflow.python.data.util import sparse
from tensorflow.python.ops import gen_dataset_ops
def get_single_element(dataset):
"""Returns the single element in `dataset` as a nested structure of tensors.
This function enables you to use a @{tf.data.Dataset} in a stateless
"tensor-in tensor-out" expression, without creating a @{tf.data.Iterator}.
This can be useful when your preprocessing transformations are expressed
as a `Dataset`, and you want to use the transformation at serving time.
For example:
```python
input_batch = tf.placeholder(tf.string, shape=[BATCH_SIZE])
def preprocessing_fn(input_str):
# ...
return image, label
dataset = (tf.data.Dataset.from_tensor_slices(input_batch)
.map(preprocessing_fn, num_parallel_calls=BATCH_SIZE)
.batch(BATCH_SIZE))
image_batch, label_batch = tf.contrib.data.get_single_element(dataset)
```
Args:
dataset: A @{tf.data.Dataset} object containing a single element.
Returns:
A nested structure of @{tf.Tensor} objects, corresponding to the single
element of `dataset`.
Raises:
TypeError: if `dataset` is not a `tf.data.Dataset` object.
InvalidArgumentError (at runtime): if `dataset` does not contain exactly
one element.
"""
if not isinstance(dataset, dataset_ops.Dataset):
raise TypeError("`dataset` must be a `tf.data.Dataset` object.")
nested_ret = nest.pack_sequence_as(
dataset.output_types, gen_dataset_ops.dataset_to_single_element(
dataset._as_variant_tensor(), # pylint: disable=protected-access
**dataset_ops.flat_structure(dataset)))
return sparse.deserialize_sparse_tensors(
nested_ret, dataset.output_types, dataset.output_shapes,
dataset.output_classes)
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