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# Copyright 2018 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.
# ==============================================================================
"""Experimental API for optimizing `tf.data` pipelines."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.data.python.ops import contrib_op_loader # pylint: disable=unused-import
from tensorflow.contrib.data.python.ops import gen_dataset_ops as contrib_gen_dataset_ops
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_dataset_ops
# TODO(jsimsa): Support RE matching for both individual transformation (e.g. to
# account for indexing) and transformation sequence.
def assert_next(transformations):
"""A transformation that asserts which transformations happen next.
Args:
transformations: A `tf.string` vector `tf.Tensor` identifying the
transformations that are expected to happen next.
Returns:
A `Dataset` transformation function, which can be passed to
@{tf.data.Dataset.apply}.
"""
def _apply_fn(dataset):
"""Function from `Dataset` to `Dataset` that applies the transformation."""
return _AssertNextDataset(dataset, transformations)
return _apply_fn
def optimize(optimizations=None):
"""A transformation that applies optimizations.
Args:
optimizations: (Optional.) A `tf.string` vector `tf.Tensor` identifying
optimizations to use. If not specified, the default set of optimizations
is applied.
Returns:
A `Dataset` transformation function, which can be passed to
@{tf.data.Dataset.apply}.
"""
def _apply_fn(dataset):
"""Function from `Dataset` to `Dataset` that applies the transformation."""
return _OptimizeDataset(dataset, optimizations)
return _apply_fn
class _AssertNextDataset(dataset_ops.Dataset):
"""A `Dataset` that asserts which transformations happen next."""
def __init__(self, input_dataset, transformations):
"""See `assert_next()` for details."""
super(_AssertNextDataset, self).__init__()
self._input_dataset = input_dataset
if transformations is None:
raise ValueError("At least one transformation should be specified")
self._transformations = ops.convert_to_tensor(
transformations, dtype=dtypes.string, name="transformations")
def _as_variant_tensor(self):
return contrib_gen_dataset_ops.assert_next_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
self._transformations,
**dataset_ops.flat_structure(self))
@property
def output_classes(self):
return self._input_dataset.output_classes
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
class _OptimizeDataset(dataset_ops.Dataset):
"""A `Dataset` that acts as an identity, and applies optimizations."""
def __init__(self, input_dataset, optimizations):
"""See `optimize()` for details."""
super(_OptimizeDataset, self).__init__()
self._input_dataset = input_dataset
if optimizations is None:
optimizations = []
self._optimizations = ops.convert_to_tensor(
optimizations, dtype=dtypes.string, name="optimizations")
def _as_variant_tensor(self):
return gen_dataset_ops.optimize_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
self._optimizations,
**dataset_ops.flat_structure(self))
@property
def output_classes(self):
return self._input_dataset.output_classes
@property
def output_shapes(self):
return self._input_dataset.output_shapes
@property
def output_types(self):
return self._input_dataset.output_types
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