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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.dataset_ops import Dataset
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 gen_dataset_ops
# TODO(b/64974358): Increase default buffer size to 256 MB.
_DEFAULT_READER_BUFFER_SIZE_BYTES = 256 * 1024 # 256 KB
class TextLineDataset(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)
@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):
"""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)
@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 FixedLengthRecordDataset(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)
@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