EVOLUTION-MANAGER

Edit File: tf_record.py

# Copyright 2015 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.
# ==============================================================================

"""For reading and writing TFRecords files."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python import _pywrap_record_io
from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import tf_export

@tf_export(
    v1=["io.TFRecordCompressionType", "python_io.TFRecordCompressionType"])
@deprecation.deprecated_endpoints("io.TFRecordCompressionType",
                                  "python_io.TFRecordCompressionType")
class TFRecordCompressionType(object):
  """The type of compression for the record."""
  NONE = 0
  ZLIB = 1
  GZIP = 2

@tf_export(
    "io.TFRecordOptions",
    v1=["io.TFRecordOptions", "python_io.TFRecordOptions"])
@deprecation.deprecated_endpoints("python_io.TFRecordOptions")
class TFRecordOptions(object):
  """Options used for manipulating TFRecord files."""
  compression_type_map = {
      TFRecordCompressionType.ZLIB: "ZLIB",
      TFRecordCompressionType.GZIP: "GZIP",
      TFRecordCompressionType.NONE: ""
  }

def __init__(self,
               compression_type=None,
               flush_mode=None,
               input_buffer_size=None,
               output_buffer_size=None,
               window_bits=None,
               compression_level=None,
               compression_method=None,
               mem_level=None,
               compression_strategy=None):
    # pylint: disable=line-too-long
    """Creates a `TFRecordOptions` instance.

Options only effect TFRecordWriter when compression_type is not `None`.
    Documentation, details, and defaults can be found in
    [`zlib_compression_options.h`](https://www.tensorflow.org/code/tensorflow/core/lib/io/zlib_compression_options.h)
    and in the [zlib manual](http://www.zlib.net/manual.html).
    Leaving an option as `None` allows C++ to set a reasonable default.

Args:
      compression_type: `"GZIP"`, `"ZLIB"`, or `""` (no compression).
      flush_mode: flush mode or `None`, Default: Z_NO_FLUSH.
      input_buffer_size: int or `None`.
      output_buffer_size: int or `None`.
      window_bits: int or `None`.
      compression_level: 0 to 9, or `None`.
      compression_method: compression method or `None`.
      mem_level: 1 to 9, or `None`.
      compression_strategy: strategy or `None`. Default: Z_DEFAULT_STRATEGY.

Returns:
      A `TFRecordOptions` object.

Raises:
      ValueError: If compression_type is invalid.
    """
    # pylint: enable=line-too-long
    # Check compression_type is valid, but for backwards compatibility don't
    # immediately convert to a string.
    self.get_compression_type_string(compression_type)
    self.compression_type = compression_type
    self.flush_mode = flush_mode
    self.input_buffer_size = input_buffer_size
    self.output_buffer_size = output_buffer_size
    self.window_bits = window_bits
    self.compression_level = compression_level
    self.compression_method = compression_method
    self.mem_level = mem_level
    self.compression_strategy = compression_strategy

@classmethod
  def get_compression_type_string(cls, options):
    """Convert various option types to a unified string.

Args:
      options: `TFRecordOption`, `TFRecordCompressionType`, or string.

Returns:
      Compression type as string (e.g. `'ZLIB'`, `'GZIP'`, or `''`).

Raises:
      ValueError: If compression_type is invalid.
    """
    if not options:
      return ""
    elif isinstance(options, TFRecordOptions):
      return cls.get_compression_type_string(options.compression_type)
    elif isinstance(options, TFRecordCompressionType):
      return cls.compression_type_map[options]
    elif options in TFRecordOptions.compression_type_map:
      return cls.compression_type_map[options]
    elif options in TFRecordOptions.compression_type_map.values():
      return options
    else:
      raise ValueError('Not a valid compression_type: "{}"'.format(options))

def _as_record_writer_options(self):
    """Convert to RecordWriterOptions for use with PyRecordWriter."""
    options = _pywrap_record_io.RecordWriterOptions(
        compat.as_bytes(
            self.get_compression_type_string(self.compression_type)))

if self.flush_mode is not None:
      options.zlib_options.flush_mode = self.flush_mode
    if self.input_buffer_size is not None:
      options.zlib_options.input_buffer_size = self.input_buffer_size
    if self.output_buffer_size is not None:
      options.zlib_options.output_buffer_size = self.output_buffer_size
    if self.window_bits is not None:
      options.zlib_options.window_bits = self.window_bits
    if self.compression_level is not None:
      options.zlib_options.compression_level = self.compression_level
    if self.compression_method is not None:
      options.zlib_options.compression_method = self.compression_method
    if self.mem_level is not None:
      options.zlib_options.mem_level = self.mem_level
    if self.compression_strategy is not None:
      options.zlib_options.compression_strategy = self.compression_strategy
    return options

@tf_export(v1=["io.tf_record_iterator", "python_io.tf_record_iterator"])
@deprecation.deprecated(
    date=None,
    instructions=("Use eager execution and: \n"
                  "`tf.data.TFRecordDataset(path)`"))
def tf_record_iterator(path, options=None):
  """An iterator that read the records from a TFRecords file.

Args:
    path: The path to the TFRecords file.
    options: (optional) A TFRecordOptions object.

Returns:
    An iterator of serialized TFRecords.

Raises:
    IOError: If `path` cannot be opened for reading.
  """
  compression_type = TFRecordOptions.get_compression_type_string(options)
  return _pywrap_record_io.RecordIterator(path, compression_type)

def tf_record_random_reader(path):
  """Creates a reader that allows random-access reads from a TFRecords file.

The created reader object has the following method:

- `read(offset)`, which returns a tuple of `(record, ending_offset)`, where
      `record` is the TFRecord read at the offset, and
      `ending_offset` is the ending offset of the read record.

The method throws a `tf.errors.DataLossError` if data is corrupted at
      the given offset. The method throws `IndexError` if the offset is out of
      range for the TFRecords file.

Usage example:
  ```py
  reader = tf_record_random_reader(file_path)

record_1, offset_1 = reader.read(0)  # 0 is the initial offset.
  # offset_1 is the ending offset of the 1st record and the starting offset of
  # the next.

record_2, offset_2 = reader.read(offset_1)
  # offset_2 is the ending offset of the 2nd record and the starting offset of
  # the next.
  # We can jump back and read the first record again if so desired.
  reader.read(0)
  ```

Args:
    path: The path to the TFRecords file.

Returns:
    An object that supports random-access reading of the serialized TFRecords.

Raises:
    IOError: If `path` cannot be opened for reading.
  """
  return _pywrap_record_io.RandomRecordReader(path)

@tf_export(
    "io.TFRecordWriter", v1=["io.TFRecordWriter", "python_io.TFRecordWriter"])
@deprecation.deprecated_endpoints("python_io.TFRecordWriter")
class TFRecordWriter(_pywrap_record_io.RecordWriter):
  """A class to write records to a TFRecords file.

[TFRecords tutorial](https://www.tensorflow.org/tutorials/load_data/tfrecord)

TFRecords is a binary format which is optimized for high throughput data
  retrieval, generally in conjunction with `tf.data`. `TFRecordWriter` is used
  to write serialized examples to a file for later consumption. The key steps
  are:

Ahead of time:

- [Convert data into a serialized format](
   https://www.tensorflow.org/tutorials/load_data/tfrecord#tfexample)
   - [Write the serialized data to one or more files](
   https://www.tensorflow.org/tutorials/load_data/tfrecord#tfrecord_files_in_python)

During training or evaluation:

- [Read serialized examples into memory](
   https://www.tensorflow.org/tutorials/load_data/tfrecord#reading_a_tfrecord_file)
   - [Parse (deserialize) examples](
   https://www.tensorflow.org/tutorials/load_data/tfrecord#reading_a_tfrecord_file)

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

This class implements `__enter__` and `__exit__`, and can be used
  in `with` blocks like a normal file. (See the usage example above.)
  """

# TODO(josh11b): Support appending?
  def __init__(self, path, options=None):
    """Opens file `path` and creates a `TFRecordWriter` writing to it.

Args:
      path: The path to the TFRecords file.
      options: (optional) String specifying compression type,
          `TFRecordCompressionType`, or `TFRecordOptions` object.

Raises:
      IOError: If `path` cannot be opened for writing.
      ValueError: If valid compression_type can't be determined from `options`.
    """
    if not isinstance(options, TFRecordOptions):
      options = TFRecordOptions(compression_type=options)

# pylint: disable=protected-access
    super(TFRecordWriter, self).__init__(
        compat.as_bytes(path), options._as_record_writer_options())
    # pylint: enable=protected-access

# TODO(slebedev): The following wrapper methods are there to compensate
  # for lack of signatures in pybind11-generated classes. Switch to
  # __text_signature__ when TensorFlow drops Python 2.X support.
  # See https://github.com/pybind/pybind11/issues/945
  # pylint: disable=useless-super-delegation
  def write(self, record):
    """Write a string record to the file.

Args:
      record: str
    """
    super(TFRecordWriter, self).write(record)

def flush(self):
    """Flush the file."""
    super(TFRecordWriter, self).flush()

def close(self):
    """Close the file."""
    super(TFRecordWriter, self).close()
  # pylint: enable=useless-super-delegation