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