707 lines
26 KiB
Python
707 lines
26 KiB
Python
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# 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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"""Python wrappers for reader Datasets."""
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import os
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from tensorflow.python import tf2
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import from_tensor_slices_op
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from tensorflow.python.data.ops import structured_function
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from tensorflow.python.data.util import convert
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.framework import type_spec
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_dataset_ops
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from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
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from tensorflow.python.util import nest
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from tensorflow.python.util.tf_export import tf_export
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_DEFAULT_READER_BUFFER_SIZE_BYTES = 256 * 1024 # 256 KB
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def _normalise_fspath(path):
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"""Convert pathlib-like objects to str (__fspath__ compatibility, PEP 519)."""
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return os.fspath(path) if isinstance(path, os.PathLike) else path
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def _create_or_validate_filenames_dataset(filenames, name=None):
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"""Creates (or validates) a dataset of filenames.
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Args:
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filenames: Either a list or dataset of filenames. If it is a list, it is
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convert to a dataset. If it is a dataset, its type and shape is validated.
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name: (Optional.) A name for the tf.data operation.
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Returns:
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A dataset of filenames.
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"""
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if isinstance(filenames, dataset_ops.DatasetV2):
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element_type = dataset_ops.get_legacy_output_types(filenames)
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if element_type != dtypes.string:
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raise TypeError(
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"The `filenames` argument must contain `tf.string` elements. Got a "
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f"dataset of `{element_type!r}` elements.")
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element_shape = dataset_ops.get_legacy_output_shapes(filenames)
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if not element_shape.is_compatible_with(tensor_shape.TensorShape([])):
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raise TypeError(
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"The `filenames` argument must contain `tf.string` elements of shape "
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"[] (i.e. scalars). Got a dataset of element shape "
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f"{element_shape!r}.")
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else:
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filenames = nest.map_structure(_normalise_fspath, filenames)
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filenames = ops.convert_to_tensor(filenames, dtype_hint=dtypes.string)
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if filenames.dtype != dtypes.string:
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raise TypeError(
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"The `filenames` argument must contain `tf.string` elements. Got "
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f"`{filenames.dtype!r}` elements.")
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filenames = array_ops.reshape(filenames, [-1], name="flat_filenames")
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filenames = from_tensor_slices_op._TensorSliceDataset( # pylint: disable=protected-access
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filenames,
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is_files=True,
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name=name)
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return filenames
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def _create_dataset_reader(dataset_creator,
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filenames,
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num_parallel_reads=None,
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name=None):
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"""Creates a dataset that reads the given files using the given reader.
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Args:
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dataset_creator: A function that takes in a single file name and returns a
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dataset.
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filenames: A `tf.data.Dataset` containing one or more filenames.
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num_parallel_reads: The number of parallel reads we should do.
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name: (Optional.) A name for the tf.data operation.
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Returns:
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A `Dataset` that reads data from `filenames`.
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"""
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def read_one_file(filename):
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filename = ops.convert_to_tensor(filename, dtypes.string, name="filename")
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return dataset_creator(filename)
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if num_parallel_reads is None:
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return filenames.flat_map(read_one_file, name=name)
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elif num_parallel_reads == dataset_ops.AUTOTUNE:
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return filenames.interleave(
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read_one_file, num_parallel_calls=num_parallel_reads, name=name)
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else:
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return ParallelInterleaveDataset(
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filenames,
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read_one_file,
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cycle_length=num_parallel_reads,
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block_length=1,
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sloppy=False,
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buffer_output_elements=None,
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prefetch_input_elements=None,
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name=name)
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def _get_type(value):
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"""Returns the type of `value` if it is a TypeSpec."""
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if isinstance(value, type_spec.TypeSpec):
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return value.value_type()
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else:
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return type(value)
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class _TextLineDataset(dataset_ops.DatasetSource):
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"""A `Dataset` comprising records from one or more text files."""
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def __init__(self,
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filenames,
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compression_type=None,
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buffer_size=None,
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name=None):
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"""Creates a `TextLineDataset`.
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Args:
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filenames: A `tf.string` tensor containing one or more filenames.
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compression_type: (Optional.) A `tf.string` scalar evaluating to one of
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`""` (no compression), `"ZLIB"`, or `"GZIP"`.
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buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
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to buffer. A value of 0 results in the default buffering values chosen
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based on the compression type.
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name: (Optional.) A name for the tf.data operation.
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"""
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self._filenames = filenames
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self._compression_type = convert.optional_param_to_tensor(
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"compression_type",
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compression_type,
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argument_default="",
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argument_dtype=dtypes.string)
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self._buffer_size = convert.optional_param_to_tensor(
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"buffer_size",
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buffer_size,
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argument_default=_DEFAULT_READER_BUFFER_SIZE_BYTES)
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self._name = name
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variant_tensor = gen_dataset_ops.text_line_dataset(
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self._filenames,
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self._compression_type,
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self._buffer_size,
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metadata=self._metadata.SerializeToString())
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super(_TextLineDataset, self).__init__(variant_tensor)
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@property
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def element_spec(self):
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return tensor_spec.TensorSpec([], dtypes.string)
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@tf_export("data.TextLineDataset", v1=[])
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class TextLineDatasetV2(dataset_ops.DatasetSource):
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r"""Creates a `Dataset` comprising lines from one or more text files.
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The `tf.data.TextLineDataset` loads text from text files and creates a dataset
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where each line of the files becomes an element of the dataset.
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For example, suppose we have 2 files "text_lines0.txt" and "text_lines1.txt"
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with the following lines:
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>>> with open('/tmp/text_lines0.txt', 'w') as f:
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... f.write('the cow\n')
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... f.write('jumped over\n')
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... f.write('the moon\n')
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>>> with open('/tmp/text_lines1.txt', 'w') as f:
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... f.write('jack and jill\n')
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... f.write('went up\n')
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... f.write('the hill\n')
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We can construct a TextLineDataset from them as follows:
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>>> dataset = tf.data.TextLineDataset(['/tmp/text_lines0.txt',
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... '/tmp/text_lines1.txt'])
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The elements of the dataset are expected to be:
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>>> for element in dataset.as_numpy_iterator():
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... print(element)
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b'the cow'
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b'jumped over'
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b'the moon'
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b'jack and jill'
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b'went up'
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b'the hill'
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"""
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def __init__(self,
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filenames,
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compression_type=None,
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buffer_size=None,
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num_parallel_reads=None,
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name=None):
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r"""Creates a `TextLineDataset`.
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The elements of the dataset will be the lines of the input files, using
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the newline character '\n' to denote line splits. The newline characters
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will be stripped off of each element.
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Args:
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filenames: A `tf.data.Dataset` whose elements are `tf.string` scalars, a
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`tf.string` tensor, or a value that can be converted to a `tf.string`
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tensor (such as a list of Python strings).
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compression_type: (Optional.) A `tf.string` scalar evaluating to one of
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`""` (no compression), `"ZLIB"`, or `"GZIP"`.
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buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
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to buffer. A value of 0 results in the default buffering values chosen
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based on the compression type.
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num_parallel_reads: (Optional.) A `tf.int64` scalar representing the
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number of files to read in parallel. If greater than one, the records of
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files read in parallel are outputted in an interleaved order. If your
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input pipeline is I/O bottlenecked, consider setting this parameter to a
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value greater than one to parallelize the I/O. If `None`, files will be
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read sequentially.
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name: (Optional.) A name for the tf.data operation.
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"""
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filenames = _create_or_validate_filenames_dataset(filenames, name=name)
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self._filenames = filenames
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self._compression_type = compression_type
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self._buffer_size = buffer_size
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def creator_fn(filename):
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return _TextLineDataset(
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filename, compression_type, buffer_size, name=name)
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self._impl = _create_dataset_reader(
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creator_fn, filenames, num_parallel_reads, name=name)
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variant_tensor = self._impl._variant_tensor # pylint: disable=protected-access
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super(TextLineDatasetV2, self).__init__(variant_tensor)
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@property
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def element_spec(self):
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return tensor_spec.TensorSpec([], dtypes.string)
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@tf_export(v1=["data.TextLineDataset"])
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class TextLineDatasetV1(dataset_ops.DatasetV1Adapter):
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"""A `Dataset` comprising lines from one or more text files."""
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def __init__(self,
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filenames,
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compression_type=None,
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buffer_size=None,
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num_parallel_reads=None,
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name=None):
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wrapped = TextLineDatasetV2(filenames, compression_type, buffer_size,
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num_parallel_reads, name)
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super(TextLineDatasetV1, self).__init__(wrapped)
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__init__.__doc__ = TextLineDatasetV2.__init__.__doc__
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@property
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def _filenames(self):
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return self._dataset._filenames # pylint: disable=protected-access
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@_filenames.setter
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def _filenames(self, value):
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self._dataset._filenames = value # pylint: disable=protected-access
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class _TFRecordDataset(dataset_ops.DatasetSource):
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"""A `Dataset` comprising records from one or more TFRecord files."""
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def __init__(self,
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filenames,
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compression_type=None,
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buffer_size=None,
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name=None):
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"""Creates a `TFRecordDataset`.
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Args:
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filenames: A `tf.string` tensor containing one or more filenames.
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compression_type: (Optional.) A `tf.string` scalar evaluating to one of
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`""` (no compression), `"ZLIB"`, or `"GZIP"`.
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buffer_size: (Optional.) A `tf.int64` scalar representing the number of
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bytes in the read buffer. 0 means no buffering.
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name: (Optional.) A name for the tf.data operation.
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"""
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self._filenames = filenames
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self._compression_type = convert.optional_param_to_tensor(
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"compression_type",
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compression_type,
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argument_default="",
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argument_dtype=dtypes.string)
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self._buffer_size = convert.optional_param_to_tensor(
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"buffer_size",
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buffer_size,
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argument_default=_DEFAULT_READER_BUFFER_SIZE_BYTES)
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self._name = name
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variant_tensor = gen_dataset_ops.tf_record_dataset(
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self._filenames, self._compression_type, self._buffer_size,
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metadata=self._metadata.SerializeToString())
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super(_TFRecordDataset, self).__init__(variant_tensor)
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@property
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def element_spec(self):
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return tensor_spec.TensorSpec([], dtypes.string)
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class ParallelInterleaveDataset(dataset_ops.UnaryDataset):
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"""A `Dataset` that maps a function over its input and flattens the result."""
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def __init__(self,
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input_dataset,
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map_func,
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cycle_length,
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block_length,
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sloppy,
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buffer_output_elements,
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prefetch_input_elements,
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name=None):
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"""See `tf.data.experimental.parallel_interleave()` for details."""
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self._input_dataset = input_dataset
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self._map_func = structured_function.StructuredFunctionWrapper(
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map_func, self._transformation_name(), dataset=input_dataset)
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if not isinstance(self._map_func.output_structure, dataset_ops.DatasetSpec):
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raise TypeError(
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"The `map_func` argument must return a `Dataset` object. Got "
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f"{_get_type(self._map_func.output_structure)!r}.")
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self._element_spec = self._map_func.output_structure._element_spec # pylint: disable=protected-access
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self._cycle_length = ops.convert_to_tensor(
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cycle_length, dtype=dtypes.int64, name="cycle_length")
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self._block_length = ops.convert_to_tensor(
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block_length, dtype=dtypes.int64, name="block_length")
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self._buffer_output_elements = convert.optional_param_to_tensor(
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"buffer_output_elements",
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buffer_output_elements,
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argument_default=2 * block_length)
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self._prefetch_input_elements = convert.optional_param_to_tensor(
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"prefetch_input_elements",
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prefetch_input_elements,
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argument_default=2 * cycle_length)
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|
|
if sloppy is None:
|
||
|
|
self._deterministic = "default"
|
||
|
|
elif sloppy:
|
||
|
|
self._deterministic = "false"
|
||
|
|
else:
|
||
|
|
self._deterministic = "true"
|
||
|
|
self._name = name
|
||
|
|
|
||
|
|
variant_tensor = ged_ops.legacy_parallel_interleave_dataset_v2(
|
||
|
|
self._input_dataset._variant_tensor, # pylint: disable=protected-access
|
||
|
|
self._map_func.function.captured_inputs,
|
||
|
|
self._cycle_length,
|
||
|
|
self._block_length,
|
||
|
|
self._buffer_output_elements,
|
||
|
|
self._prefetch_input_elements,
|
||
|
|
f=self._map_func.function,
|
||
|
|
deterministic=self._deterministic,
|
||
|
|
**self._common_args)
|
||
|
|
super(ParallelInterleaveDataset, self).__init__(input_dataset,
|
||
|
|
variant_tensor)
|
||
|
|
|
||
|
|
def _functions(self):
|
||
|
|
return [self._map_func]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def element_spec(self):
|
||
|
|
return self._element_spec
|
||
|
|
|
||
|
|
def _transformation_name(self):
|
||
|
|
return "tf.data.experimental.parallel_interleave()"
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export("data.TFRecordDataset", v1=[])
|
||
|
|
class TFRecordDatasetV2(dataset_ops.DatasetV2):
|
||
|
|
"""A `Dataset` comprising records from one or more TFRecord files.
|
||
|
|
|
||
|
|
This dataset loads TFRecords from the files as bytes, exactly as they were
|
||
|
|
written.`TFRecordDataset` does not do any parsing or decoding on its own.
|
||
|
|
Parsing and decoding can be done by applying `Dataset.map` transformations
|
||
|
|
after the `TFRecordDataset`.
|
||
|
|
|
||
|
|
A minimal example is given below:
|
||
|
|
|
||
|
|
>>> import tempfile
|
||
|
|
>>> example_path = os.path.join(tempfile.gettempdir(), "example.tfrecords")
|
||
|
|
>>> np.random.seed(0)
|
||
|
|
|
||
|
|
>>> # Write the records to a file.
|
||
|
|
... with tf.io.TFRecordWriter(example_path) as file_writer:
|
||
|
|
... for _ in range(4):
|
||
|
|
... x, y = np.random.random(), np.random.random()
|
||
|
|
...
|
||
|
|
... record_bytes = tf.train.Example(features=tf.train.Features(feature={
|
||
|
|
... "x": tf.train.Feature(float_list=tf.train.FloatList(value=[x])),
|
||
|
|
... "y": tf.train.Feature(float_list=tf.train.FloatList(value=[y])),
|
||
|
|
... })).SerializeToString()
|
||
|
|
... file_writer.write(record_bytes)
|
||
|
|
|
||
|
|
>>> # Read the data back out.
|
||
|
|
>>> def decode_fn(record_bytes):
|
||
|
|
... return tf.io.parse_single_example(
|
||
|
|
... # Data
|
||
|
|
... record_bytes,
|
||
|
|
...
|
||
|
|
... # Schema
|
||
|
|
... {"x": tf.io.FixedLenFeature([], dtype=tf.float32),
|
||
|
|
... "y": tf.io.FixedLenFeature([], dtype=tf.float32)}
|
||
|
|
... )
|
||
|
|
|
||
|
|
>>> for batch in tf.data.TFRecordDataset([example_path]).map(decode_fn):
|
||
|
|
... print("x = {x:.4f}, y = {y:.4f}".format(**batch))
|
||
|
|
x = 0.5488, y = 0.7152
|
||
|
|
x = 0.6028, y = 0.5449
|
||
|
|
x = 0.4237, y = 0.6459
|
||
|
|
x = 0.4376, y = 0.8918
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
filenames,
|
||
|
|
compression_type=None,
|
||
|
|
buffer_size=None,
|
||
|
|
num_parallel_reads=None,
|
||
|
|
name=None):
|
||
|
|
"""Creates a `TFRecordDataset` to read one or more TFRecord files.
|
||
|
|
|
||
|
|
Each element of the dataset will contain a single TFRecord.
|
||
|
|
|
||
|
|
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. If your input pipeline is I/O bottlenecked,
|
||
|
|
consider setting this parameter to a value 1-100 MBs. If `None`, a
|
||
|
|
sensible default for both local and remote file systems is used.
|
||
|
|
num_parallel_reads: (Optional.) A `tf.int64` scalar representing the
|
||
|
|
number of files to read in parallel. If greater than one, the records of
|
||
|
|
files read in parallel are outputted in an interleaved order. If your
|
||
|
|
input pipeline is I/O bottlenecked, consider setting this parameter to a
|
||
|
|
value greater than one to parallelize the I/O. If `None`, files will be
|
||
|
|
read sequentially.
|
||
|
|
name: (Optional.) A name for the tf.data operation.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
TypeError: If any argument does not have the expected type.
|
||
|
|
ValueError: If any argument does not have the expected shape.
|
||
|
|
"""
|
||
|
|
filenames = _create_or_validate_filenames_dataset(filenames, name=name)
|
||
|
|
|
||
|
|
self._filenames = filenames
|
||
|
|
self._compression_type = compression_type
|
||
|
|
self._buffer_size = buffer_size
|
||
|
|
self._num_parallel_reads = num_parallel_reads
|
||
|
|
|
||
|
|
def creator_fn(filename):
|
||
|
|
return _TFRecordDataset(
|
||
|
|
filename, compression_type, buffer_size, name=name)
|
||
|
|
|
||
|
|
self._impl = _create_dataset_reader(
|
||
|
|
creator_fn, filenames, num_parallel_reads, name=name)
|
||
|
|
variant_tensor = self._impl._variant_tensor # pylint: disable=protected-access
|
||
|
|
super(TFRecordDatasetV2, self).__init__(variant_tensor)
|
||
|
|
|
||
|
|
def _inputs(self):
|
||
|
|
return self._impl._inputs() # pylint: disable=protected-access
|
||
|
|
|
||
|
|
@property
|
||
|
|
def element_spec(self):
|
||
|
|
return tensor_spec.TensorSpec([], dtypes.string)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export(v1=["data.TFRecordDataset"])
|
||
|
|
class TFRecordDatasetV1(dataset_ops.DatasetV1Adapter):
|
||
|
|
"""A `Dataset` comprising records from one or more TFRecord files."""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
filenames,
|
||
|
|
compression_type=None,
|
||
|
|
buffer_size=None,
|
||
|
|
num_parallel_reads=None,
|
||
|
|
name=None):
|
||
|
|
wrapped = TFRecordDatasetV2(
|
||
|
|
filenames, compression_type, buffer_size, num_parallel_reads, name=name)
|
||
|
|
super(TFRecordDatasetV1, self).__init__(wrapped)
|
||
|
|
|
||
|
|
__init__.__doc__ = TFRecordDatasetV2.__init__.__doc__
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _filenames(self):
|
||
|
|
return self._dataset._filenames # pylint: disable=protected-access
|
||
|
|
|
||
|
|
@_filenames.setter
|
||
|
|
def _filenames(self, value):
|
||
|
|
self._dataset._filenames = value # pylint: disable=protected-access
|
||
|
|
|
||
|
|
|
||
|
|
class _FixedLengthRecordDataset(dataset_ops.DatasetSource):
|
||
|
|
"""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,
|
||
|
|
compression_type=None,
|
||
|
|
name=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.
|
||
|
|
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
|
||
|
|
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
|
||
|
|
name: (Optional.) A name for the tf.data operation.
|
||
|
|
"""
|
||
|
|
self._filenames = 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)
|
||
|
|
self._compression_type = convert.optional_param_to_tensor(
|
||
|
|
"compression_type",
|
||
|
|
compression_type,
|
||
|
|
argument_default="",
|
||
|
|
argument_dtype=dtypes.string)
|
||
|
|
self._name = name
|
||
|
|
|
||
|
|
variant_tensor = gen_dataset_ops.fixed_length_record_dataset_v2(
|
||
|
|
self._filenames,
|
||
|
|
self._header_bytes,
|
||
|
|
self._record_bytes,
|
||
|
|
self._footer_bytes,
|
||
|
|
self._buffer_size,
|
||
|
|
self._compression_type,
|
||
|
|
metadata=self._metadata.SerializeToString())
|
||
|
|
super(_FixedLengthRecordDataset, self).__init__(variant_tensor)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def element_spec(self):
|
||
|
|
return tensor_spec.TensorSpec([], dtypes.string)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export("data.FixedLengthRecordDataset", v1=[])
|
||
|
|
class FixedLengthRecordDatasetV2(dataset_ops.DatasetSource):
|
||
|
|
"""A `Dataset` of fixed-length records from one or more binary files.
|
||
|
|
|
||
|
|
The `tf.data.FixedLengthRecordDataset` reads fixed length records from binary
|
||
|
|
files and creates a dataset where each record becomes an element of the
|
||
|
|
dataset. The binary files can have a fixed length header and a fixed length
|
||
|
|
footer, which will both be skipped.
|
||
|
|
|
||
|
|
For example, suppose we have 2 files "fixed_length0.bin" and
|
||
|
|
"fixed_length1.bin" with the following content:
|
||
|
|
|
||
|
|
>>> with open('/tmp/fixed_length0.bin', 'wb') as f:
|
||
|
|
... f.write(b'HEADER012345FOOTER')
|
||
|
|
>>> with open('/tmp/fixed_length1.bin', 'wb') as f:
|
||
|
|
... f.write(b'HEADER6789abFOOTER')
|
||
|
|
|
||
|
|
We can construct a `FixedLengthRecordDataset` from them as follows:
|
||
|
|
|
||
|
|
>>> dataset1 = tf.data.FixedLengthRecordDataset(
|
||
|
|
... filenames=['/tmp/fixed_length0.bin', '/tmp/fixed_length1.bin'],
|
||
|
|
... record_bytes=2, header_bytes=6, footer_bytes=6)
|
||
|
|
|
||
|
|
The elements of the dataset are:
|
||
|
|
|
||
|
|
>>> for element in dataset1.as_numpy_iterator():
|
||
|
|
... print(element)
|
||
|
|
b'01'
|
||
|
|
b'23'
|
||
|
|
b'45'
|
||
|
|
b'67'
|
||
|
|
b'89'
|
||
|
|
b'ab'
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self,
|
||
|
|
filenames,
|
||
|
|
record_bytes,
|
||
|
|
header_bytes=None,
|
||
|
|
footer_bytes=None,
|
||
|
|
buffer_size=None,
|
||
|
|
compression_type=None,
|
||
|
|
num_parallel_reads=None,
|
||
|
|
name=None):
|
||
|
|
"""Creates a `FixedLengthRecordDataset`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
filenames: A `tf.string` tensor or `tf.data.Dataset` 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.
|
||
|
|
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
|
||
|
|
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
|
||
|
|
num_parallel_reads: (Optional.) A `tf.int64` scalar representing the
|
||
|
|
number of files to read in parallel. If greater than one, the records of
|
||
|
|
files read in parallel are outputted in an interleaved order. If your
|
||
|
|
input pipeline is I/O bottlenecked, consider setting this parameter to a
|
||
|
|
value greater than one to parallelize the I/O. If `None`, files will be
|
||
|
|
read sequentially.
|
||
|
|
name: (Optional.) A name for the tf.data operation.
|
||
|
|
"""
|
||
|
|
filenames = _create_or_validate_filenames_dataset(filenames, name=name)
|
||
|
|
|
||
|
|
self._filenames = filenames
|
||
|
|
self._record_bytes = record_bytes
|
||
|
|
self._header_bytes = header_bytes
|
||
|
|
self._footer_bytes = footer_bytes
|
||
|
|
self._buffer_size = buffer_size
|
||
|
|
self._compression_type = compression_type
|
||
|
|
|
||
|
|
def creator_fn(filename):
|
||
|
|
return _FixedLengthRecordDataset(
|
||
|
|
filename,
|
||
|
|
record_bytes,
|
||
|
|
header_bytes,
|
||
|
|
footer_bytes,
|
||
|
|
buffer_size,
|
||
|
|
compression_type,
|
||
|
|
name=name)
|
||
|
|
|
||
|
|
self._impl = _create_dataset_reader(
|
||
|
|
creator_fn, filenames, num_parallel_reads, name=name)
|
||
|
|
variant_tensor = self._impl._variant_tensor # pylint: disable=protected-access
|
||
|
|
super(FixedLengthRecordDatasetV2, self).__init__(variant_tensor)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def element_spec(self):
|
||
|
|
return tensor_spec.TensorSpec([], dtypes.string)
|
||
|
|
|
||
|
|
|
||
|
|
@tf_export(v1=["data.FixedLengthRecordDataset"])
|
||
|
|
class FixedLengthRecordDatasetV1(dataset_ops.DatasetV1Adapter):
|
||
|
|
"""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,
|
||
|
|
compression_type=None,
|
||
|
|
num_parallel_reads=None,
|
||
|
|
name=None):
|
||
|
|
wrapped = FixedLengthRecordDatasetV2(
|
||
|
|
filenames,
|
||
|
|
record_bytes,
|
||
|
|
header_bytes,
|
||
|
|
footer_bytes,
|
||
|
|
buffer_size,
|
||
|
|
compression_type,
|
||
|
|
num_parallel_reads,
|
||
|
|
name=name)
|
||
|
|
super(FixedLengthRecordDatasetV1, self).__init__(wrapped)
|
||
|
|
|
||
|
|
__init__.__doc__ = FixedLengthRecordDatasetV2.__init__.__doc__
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _filenames(self):
|
||
|
|
return self._dataset._filenames # pylint: disable=protected-access
|
||
|
|
|
||
|
|
@_filenames.setter
|
||
|
|
def _filenames(self, value):
|
||
|
|
self._dataset._filenames = value # pylint: disable=protected-access
|
||
|
|
|
||
|
|
|
||
|
|
if tf2.enabled():
|
||
|
|
FixedLengthRecordDataset = FixedLengthRecordDatasetV2
|
||
|
|
TFRecordDataset = TFRecordDatasetV2
|
||
|
|
TextLineDataset = TextLineDatasetV2
|
||
|
|
else:
|
||
|
|
FixedLengthRecordDataset = FixedLengthRecordDatasetV1
|
||
|
|
TFRecordDataset = TFRecordDatasetV1
|
||
|
|
TextLineDataset = TextLineDatasetV1
|