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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 reader Datasets."""
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 convert
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_dataset_ops
from tensorflow.python.util.tf_export import tf_export
# TODO(b/64974358): Increase default buffer size to 256 MB.
_DEFAULT_READER_BUFFER_SIZE_BYTES = 256 * 1024 # 256 KB
@tf_export("data.TextLineDataset")
class TextLineDataset(dataset_ops.Dataset):
"""A `Dataset` comprising lines from one or more text files."""
def __init__(self, filenames, compression_type=None, buffer_size=None):
"""Creates a `TextLineDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
to buffer. A value of 0 results in the default buffering values chosen
based on the compression type.
"""
super(TextLineDataset, self).__init__()
self._filenames = ops.convert_to_tensor(
filenames, dtype=dtypes.string, name="filenames")
self._compression_type = convert.optional_param_to_tensor(
"compression_type",
compression_type,
argument_default="",
argument_dtype=dtypes.string)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)
def _as_variant_tensor(self):
return gen_dataset_ops.text_line_dataset(
self._filenames, self._compression_type, self._buffer_size)
def _inputs(self):
return []
@property
def output_classes(self):
return ops.Tensor
@property
def output_shapes(self):
return tensor_shape.scalar()
@property
def output_types(self):
return dtypes.string
class _TFRecordDataset(dataset_ops.Dataset):
"""A `Dataset` comprising records from one or more TFRecord files."""
def __init__(self, filenames, compression_type=None, buffer_size=None):
"""Creates a `TFRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
buffer_size: (Optional.) A `tf.int64` scalar representing the number of
bytes in the read buffer. 0 means no buffering.
"""
super(_TFRecordDataset, self).__init__()
# Force the type to string even if filenames is an empty list.
self._filenames = ops.convert_to_tensor(
filenames, dtypes.string, name="filenames")
self._compression_type = convert.optional_param_to_tensor(
"compression_type",
compression_type,
argument_default="",
argument_dtype=dtypes.string)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size",
buffer_size,
argument_default=_DEFAULT_READER_BUFFER_SIZE_BYTES)
def _as_variant_tensor(self):
return gen_dataset_ops.tf_record_dataset(
self._filenames, self._compression_type, self._buffer_size)
def _inputs(self):
return []
@property
def output_classes(self):
return ops.Tensor
@property
def output_shapes(self):
return tensor_shape.TensorShape([])
@property
def output_types(self):
return dtypes.string
class ParallelInterleaveDataset(dataset_ops.InterleaveDataset):
"""A `Dataset` that maps a function over its input and flattens the result."""
def __init__(self, input_dataset, map_func, cycle_length, block_length,
sloppy, buffer_output_elements, prefetch_input_elements):
"""See `tf.data.experimental.parallel_interleave()` for details."""
super(ParallelInterleaveDataset, self).__init__(input_dataset, map_func,
cycle_length, block_length)
self._sloppy = ops.convert_to_tensor(
sloppy, dtype=dtypes.bool, name="sloppy")
self._buffer_output_elements = convert.optional_param_to_tensor(
"buffer_output_elements",
buffer_output_elements,
argument_default=2 * block_length)
self._prefetch_input_elements = convert.optional_param_to_tensor(
"prefetch_input_elements",
prefetch_input_elements,
argument_default=2 * cycle_length)
def _as_variant_tensor(self):
# pylint: disable=protected-access
return gen_dataset_ops.parallel_interleave_dataset(
self._input_dataset._as_variant_tensor(),
self._map_func.captured_inputs,
self._cycle_length,
self._block_length,
self._sloppy,
self._buffer_output_elements,
self._prefetch_input_elements,
f=self._map_func,
**dataset_ops.flat_structure(self))
# pylint: enable=protected-access
def _transformation_name(self):
return "tf.data.experimental.parallel_interleave()"
@tf_export("data.TFRecordDataset")
class TFRecordDataset(dataset_ops.Dataset):
"""A `Dataset` comprising records from one or more TFRecord files."""
def __init__(self, filenames, compression_type=None, buffer_size=None,
num_parallel_reads=None):
"""Creates a `TFRecordDataset` to read for one or more TFRecord files.
NOTE: The `num_parallel_reads` argument can be used to improve performance
when reading from a remote filesystem.
Args:
filenames: A `tf.string` tensor or `tf.data.Dataset` containing one or
more filenames.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
buffer_size: (Optional.) A `tf.int64` scalar representing the number of
bytes in the read buffer. 0 means no buffering.
num_parallel_reads: (Optional.) A `tf.int64` scalar representing the
number of files to read in parallel. Defaults to reading files
sequentially.
Raises:
TypeError: If any argument does not have the expected type.
ValueError: If any argument does not have the expected shape.
"""
super(TFRecordDataset, self).__init__()
if isinstance(filenames, dataset_ops.Dataset):
if filenames.output_types != dtypes.string:
raise TypeError(
"`filenames` must be a `tf.data.Dataset` of `tf.string` elements.")
if not filenames.output_shapes.is_compatible_with(tensor_shape.scalar()):
raise ValueError(
"`filenames` must be a `tf.data.Dataset` of scalar `tf.string` "
"elements.")
else:
filenames = ops.convert_to_tensor(filenames, dtype=dtypes.string)
filenames = array_ops.reshape(filenames, [-1], name="flat_filenames")
filenames = dataset_ops.Dataset.from_tensor_slices(filenames)
self._filenames = filenames
self._compression_type = compression_type
self._buffer_size = buffer_size
self._num_parallel_reads = num_parallel_reads
def read_one_file(filename):
return _TFRecordDataset(filename, compression_type, buffer_size)
if num_parallel_reads is None:
self._impl = filenames.flat_map(read_one_file)
else:
self._impl = ParallelInterleaveDataset(
filenames, read_one_file, cycle_length=num_parallel_reads,
block_length=1, sloppy=False, buffer_output_elements=None,
prefetch_input_elements=None)
def _clone(self,
filenames=None,
compression_type=None,
buffer_size=None,
num_parallel_reads=None):
return TFRecordDataset(filenames or self._filenames,
compression_type or self._compression_type,
buffer_size or self._buffer_size,
num_parallel_reads or self._num_parallel_reads)
def _as_variant_tensor(self):
return self._impl._as_variant_tensor() # pylint: disable=protected-access
def _inputs(self):
return self._impl._inputs() # pylint: disable=protected-access
@property
def output_classes(self):
return self._impl.output_classes
@property
def output_shapes(self):
return self._impl.output_shapes
@property
def output_types(self):
return self._impl.output_types
@tf_export("data.FixedLengthRecordDataset")
class FixedLengthRecordDataset(dataset_ops.Dataset):
"""A `Dataset` of fixed-length records from one or more binary files."""
def __init__(self,
filenames,
record_bytes,
header_bytes=None,
footer_bytes=None,
buffer_size=None):
"""Creates a `FixedLengthRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
record_bytes: A `tf.int64` scalar representing the number of bytes in
each record.
header_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to skip at the start of a file.
footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to ignore at the end of a file.
buffer_size: (Optional.) A `tf.int64` scalar representing the number of
bytes to buffer when reading.
"""
super(FixedLengthRecordDataset, self).__init__()
self._filenames = ops.convert_to_tensor(
filenames, dtype=dtypes.string, name="filenames")
self._record_bytes = ops.convert_to_tensor(
record_bytes, dtype=dtypes.int64, name="record_bytes")
self._header_bytes = convert.optional_param_to_tensor(
"header_bytes", header_bytes)
self._footer_bytes = convert.optional_param_to_tensor(
"footer_bytes", footer_bytes)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)
def _as_variant_tensor(self):
return gen_dataset_ops.fixed_length_record_dataset(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size)
def _inputs(self):
return []
@property
def output_classes(self):
return ops.Tensor
@property
def output_shapes(self):
return tensor_shape.scalar()
@property
def output_types(self):
return dtypes.string